Note: Descriptions are shown in the official language in which they were submitted.
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MATERIAL HANDLING SYSTEM AND METHOD THEREFOR
CROSS-REFERENCE TO RELATED APPLICATION
[0001]
This application claims the benefit of and is a non-
provisional of United States provisional patent application number
63/288,253 filed on December 10, 2021, the disclosure of which is
incorporated herein by reference in its entirety.
BACKGROUND
1. Field
[0002]
The present disclosure generally relates to material
handling systems, and more particularly, to handling and placing
goods onto pallets with the material handling system.
2. Brief Description of Related Developments
[0003]
Warehouses or distribution centers for goods generate
pallets of goods for various customers, where such customers
include but are not limited to retail stores. Each of the various
customers order goods, which order is fulfilled by the warehouse
or distribution center by loading the ordered goods onto one or
more pallets. Each of the various customers may have their own
preferred way of depalletizing goods ordered from the warehouse or
distribution center to facilitate restocking of those goods on
store shelves.
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BRIEF DESCRIPTION OF THE DRAWINGS
[0004] The foregoing aspects and other features of the present
disclosure are explained in the following description, taken in
connection with the accompanying drawings, wherein:
[0005] Fig. 1 is an exemplary schematic illustration of a
warehouse or distribution center incorporating aspects of the
present disclosure;
[0006] Fig. 2 is an exemplary a schematic illustration of a
pallet load packages distribution in accordance with aspects of
the present disclosure;
[0007] Fig. 3 is an exemplary a schematic illustration of a
pallet load packages distribution in accordance with aspects of
the present disclosure;
[0008] Fig. 4 is an exemplary a schematic illustration of a
pallet load packages distribution in accordance with aspects of
the present disclosure;
[0009] Fig. 5 is an exemplary a schematic illustration of an
order for pallet planning in accordance with aspects of the present
disclosure;
[0010] Fig. 6 is an exemplary illustration of a pallet-aisle
binary matrix in accordance with aspects of the present disclosure;
[0011] Fig. 7 is an exemplary method in accordance with aspects
of the present disclosure;
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[ 0 0 1 2 ] Fig. 8 s an exemplary illustration of a planned order
in accordance with aspects of the present disclosure;
[0013] Fig. 9 is an exemplary illustration of an pallet to aisle
selection process in accordance with aspects of the present
disclosure;
[0014] Fig. 10 is an exemplary illustration of case unit
distribution for pallet loads in accordance with aspects of the
present disclosure;
[0015] Fig. 11 is an exemplary illustration of case unit
distribution for pallet loads in accordance with aspects of the
present disclosure;
[0016] Figs. 12A and 12B are diagrams of exemplary methods in
accordance with aspects of the present disclosure;
[0017] Fig. 13 is a diagram of an exemplary method in accordance
with aspects of the present disclosure;
[0018] Fig. 14 is a diagram of an exemplary method in accordance
with aspects of the present disclosure;
[0019] Fig. 15 is a diagram of an exemplary method in accordance
with aspects of the present disclosure;
[0020] Fig. 16 is a diagram of an exemplary method in accordance
with aspects of the present disclosure; and
[0021] Fig. 17 is a graph illustrating a variation of case
dimensions within a representative population of cases.
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DETAILED DESCRIPTION
[0022] Fig. 1 illustrates an exemplary warehouse or
distribution center 199 (generally referred to herein as warehouse
199) in accordance with aspects of the present disclosure.
Although the aspects of the present disclosure will be described
with reference to the drawings, it should be understood that the
aspects of the present disclosure can be embodied in many forms.
In addition, any suitable size, shape or type of elements or
materials could be used.
[0023] The aspects of the present disclosure generally apply to
warehouse systems where pallet loads (such as those described
herein and which are collectively referred to as pallet load(s)
PALO) are built by automated machinery, such as robotized
palletizers 162, 162', according to controller generated pallet
plans. However, the aspects of the present disclosure may also be
applied to manual pallet building where a pallet load generator
(such as described herein) outputs an itemization (in accordance
with the present disclosure) of case units CU to be included on a
pallet, where a human worker builds the pallet with the
predetermined itemized case units CU based on warehouse rules and
prior work experience. The aspects of the present disclosure may
also be applied to manual warehouses where the pallet plans are
computer-generated, in accordance with the present disclosure, and
output in a tangible form (e.g., video monitors, graphical user
interfaces, smart devices such as phones and tablets, paper
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instructions, etc.) in an advisory role for -human workers to follow
so as to build the pallets described herein.
Here, the goods
included in the pallet loads PALO are delivered to a human worker
in a predetermined sequence, inferred from the pallet plans, by
conveyors, mobile robots, or other suitable conveyance.
[0024]
In accordance with the present disclosure, each pallet
load PALO is planned with any suitable computational method
including, but not limited to, those described in United States
patent numbers 8965559 issued on February 24, 2015 and 9969572
issued on May 15, 2018, the disclosures of which are incorporated
herein by reference in their entireties. A "planned pallet" or
"planned pallet load" as used herein is a pallet load that has of
a list of goods (e.g., individual items, boxes, totes, trays, etc.
as described herein and generally referred to as case units CU)
with assigned coordinates (X, Y, Z - see Fig. 1) for a corner of
the goods that has coordinates closes to the origin (X=0, Y=0,
Z=0) of the pallet coordinate system. The orientation of the goods
along the X, Y, Z axes has values of, e.g., length, width, height
or width, length, height for goods that cannot be tipped on a side.
Additional values may be provided for goods that can be placed on
a surface on any side of the goods.
These additional values
include, e.g., length, height, width or width, height, length, or
height, length, width, or height, width, length. The pallet plan
is a physically valid plan where (1) goods are non-intersecting in
physical space, (2) each of the goods is stably supported by other
goods or the pallet base, (3) no part of any goods lies outside
the predetermined bounds of the pallets outer dimensions Lp, Wp,
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Hp (or a predetermined volume Vp of the pallet load defined by the
outer dimensions Lp, Wp, Hp), and (4) the total weight of goods on
a pallet does not exceed a predetermined maximum weight Wmax for
the pallet load PALO.
[0025]
Also in accordance with the present disclosure, a
"planned order" is a number list of planned pallets such that all
ordered case units CU belong to some pallets in the list and there
are no case units CU that do not belong to any pallet load. It is
noted that consecutive case units CU in an order list do not have
to be assigned to the same or consecutive pallet loads.
For
example, case unit number 1 may be assigned to pallet load number
5, while case unit number 2 is assigned to pallet load number 3.
[0026]
It is also noted that the case units CU may have integer
values of the "product group types" that the case units CU belong
to within a retail store. For example, retail stores generally
assign a predetermined relationship between these product group
types and the physical locations (e.g., aisles, departments,
sections, etc.) within the store at which the products group types
are located.
As used herein, the product group types and the
corresponding physical locations within the retail store are
generally referred to as "aisles." It is noted that the aisles
are aisles within a retail store and are not to be confused with
(distribution center) storage/picking aisles of the storage array
130 of the (distribution center) material handling system 190.
Here, the retail store aisles and the distribution center picking
aisles (of the storage array 130) are fully decoupled from one
another. It is also noted the retail store aisles are referred to
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with numerical designations ranging from 1 to n (e.g., aisle 1,
aisle 2, ..., aisle n), where n is an integer value denoting a
predetermined highest aisle number for a given store. While the
aisles may be numbered, the locations of the aisles in the sore
may not be sequential.
In accordance with the aspects of the
present disclosure, case units CU belonging to a common (e.g., the
same) aisle (e.g., physical location/aisle and/or product group
type) are assigned to a common pallet (unless otherwise noted) for
the pallet load packages distribution methods described herein.
[0027]
In one aspect, aisles in a retail store that are close
in number (e.g., such as aisles 34 and 35) may be physically close
to one another in space. In this aspect, the present disclosure
may optimize the products placed on a given pallet by combining
products from physically close aisles (e.g., aisles 34 and 35) on
a common pallet, rather than combining products from aisles that
are physically separated from each other (e.g., such as aisles 34
and 73).
[0028]
In other aspects, the relationship between the aisle
numbers and spatial proximity of the aisles may be more complex
than adjacent aisle numbers (e.g., aisles 34 and 35) being
physically adjacent in space. For example, adjacent or close aisle
numbers (e.g., aisles 20 and aisle 21) may not mean that the aisles
are physically close to each other in space (e.g., aisle 20 may be
located on one end of the retail store while aisle 21 may be
located on an opposite end of the retail store). Here, a pairwise
relationship between two aisles may be provided with respect to
the assignment of case units to pallet loads as described herein.
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For example, in accordance with the aspects of the present
disclosure, the pairwise relationship between the two aisles is in
the form of coefficients A[i,k] for aisle i and aisle k.
This
pairwise relationship not only describes the physical proximity
between the two aisles, but also retail store preferences to keep
products from these aisles on one pallet or separate pallets based
on, for example, retail store business logic outside of a distance-
based unloading optimization. An example of such business logic
may be the separation of caustic products (e.g., laundry detergent)
and food items (e.g., baby food) which are preferably transported
on separate pallet loads.
[0029] The aspects of the present disclosure are also
applicable to any suitable volume of products in any given aisle.
For example, some aisles may have a total volume of case units
that is much larger than a volume of single pallet (e.g., see
volume V2 of aisle 2 in Fig. 5). Here, the aspects of the present
disclosure assign the volume of case units to whole pallet loads
until a remaining volume of the case units does not fill a whole
pallet load. Here, the remaining volume of case units is assigned
to a pallet in accordance with the package distribution methods
described herein. As another example, a volume of case units for
other aisles may be a few case units or even a single case unit,
in which case these case units are assigned to a pallet load in
accordance with the package distribution methods described herein.
[0030]
Referring also to Figs. 2-4, as will be described herein,
a material handling system 190 of the warehouse 199 is configured
to effect optimization of an automatic process of planning and
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building mixed-product orders 299 (see, e.g., Fig. 2) that are to
be delivered to, for example, retail stores (or other suitable
customers to which goods are delivered on pallets). The retail
stores placing the orders are referred to herein as order stores
200 (see, e.g., Figs. 2-4). Each of one or more pallet loads PALO
in a mixed-product order 299 is built by the material handling
system 190 such that each pallet load PALO is a "store friendly
pallet" or "store friendly pallet load." Here, "store friendly"
means the pallet load PALO is configured for easy and efficient
unloading and distribution to store shelves.
For descriptive
purposes only, "store friendly" refers to a store affinity of the
pallet load or a pallet load store affinity, such that the pallet
load configuration (i.e., the pallet load build) includes a
predetermined characteristic (or factor) of store affinity that
biases or factors resolution of each pallet load PALO to conform
and provide each resultant pallet load PALO with retail store
characteristics that are in accordance with or are sympathetic to
a retail store predetermined characteristic as will be described
herein.
For example, when pallet load(s) PALO of a fulfilled
mixed-product order 299 (see Figs. 2-4) arrive at an order store
200, the pallet load(s) PALO (e.g., pallets loads PALOC in Fig. 2,
PALOA, PALOA' in Fig. 3, and PALOC, PALOC' in Fig. 4) are quickly
unloaded (e.g., such as in accordance with "just in time" inventory
practices) and the goods thereof are distributed (e.g.,
restocked/stocked) onto the store shelves 233 with minimal
disruption to store operations. To facilitate the quick unloading
and distribution of the goods onto the store shelves 233, the
material handling system 190 is configured to build the pallet
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load(s) PAM() such that the structure of the goods CU (also referred
to herein as packages, products, case units, mixed cases, cases,
shipping cases, and shipping units) on the pallet load(s) PALO are
grouped in a manner similar to the way the goods CU are distributed
onto the store shelves 233.
[0031]
Each warehouse customer (e.g., order store 200) of the
warehouse 199 may have its own preference with respect to the
handling of pallet loads within the order store 200. The aspects
of the present disclosure provide for the building of store
friendly pallets that correspond to the different ways the pallets
loads are handled and products are distributed by the warehouse
customers.
[0032]
Referring to Fig. 2, one exemplary way of handling pallet
loads PALO may be referred to as a "clustered aisle pallet load
packages distribution method" and
includes
deconstructing/downstacking the pallet load(s) PALOC in a loading
dock area 222 (or other suitable area) of an order store and
putting goods CU belonging to different sections of the order store
200 onto two or more separate secondary pallets PAL21-PAL23 (three
secondary pallets are shown in Fig. 2 for exemplary purposes only).
These secondary pallets PAL21-PAL23 include goods CU assigned to
predetermined shopping aisles and are moved into the respective
predetermined shopping aisles for unloading (see Fig. 2). With
the secondary pallets PAL21-PAL23 in the respective shopping
aisle, the goods CU from the secondary pallets PAL21-PAL23 are
distributed onto assigned shelves 233.
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[0033]
Referring to Flg. 3, another example of handling pallet
loads PALO may be referred to as an "adjacent aisle pallet load
packages distribution method" and includes moving whole pallet
loads PALOA, PALOA' (e.g., without downstacking of the pallet)
into the shopping aisles. With the pallet loads PALOA, PALOA' in
the shopping aisles, goods CU are distributed substantially
directly from the pallet load(s) PALOA, PALOA' to the assigned
shelves 233 (see Fig. 3).
Here, the goods are arranged on the
pallet load(s) PALOA, PALOA' so as to minimize a travel distance
of each pallet load PALOA, PALOA' within the store and to
substantially avoid a return of the pallets PALOA, PALOA' to aisles
which the corresponding pallets have previously visited (e.g., the
pallet passes through an aisle only once along a predetermined
path 301, 302). The goods CU may be arranged on the pallet load
PALO, PALOA' according to a path of travel 300, 302 of the
respective pallet load PALOA, PALOA' through the shopping aisles.
[0034]
Referring to Fig. 4, still another example of handling
pallets PALO may be referred to as a "mixed mode clustered and
adjacent aisles pallet load packages distribution method" and
includes a combination of the above handling methods.
With
reference to Fig. 4, the pallet loads PALOC, PALOC' arrive at the
order store 200 in trucks (or other suitable conveyance) from the
warehouse/distribution center 199. The pallet loads PALOC, PALOC'
are moved (without downstacking the pallets) into the shopping
area in a general vicinity of the shelves to which the goods CU on
the pallet loads PALOC, PALOC' are assigned. With the pallet loads
PALOC, PALOC' generally located near the assigned shelves, the
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pallet loads PALOC, PALOC' are downstacked into respective
secondary pallets PAL021, PAL022, PAL023, PAL021', PAL022' that
are assigned to respective shopping aisles. Here, the pallet loads
PALOC, PALOC' are built so that each pallet load PALOC, PALOC'
includes goods belonging/assigned to store aisles that are close
to one another (e.g., pallet load PALOC includes goods that are
located in aisle 1, aisle 2 (which is adjacent to aisle 1), and
aisle 4 which is but one aisle away from aisle 2; similarly pallet
load PALOC' includes goods that belong/assigned to adjacent aisles
12 and 13). The goods CU may also be arranged in the respective
pallet load PALOC, PALOC' such that the pallet structure
corresponds with the manner in which the goods are downstacked to
the respective secondary pallets (e.g., such as a sequential
downstacking where, for example, goods assigned to secondary
pallet PAL021 are on the top of the pallet structure of pallet
load PALOC, goods assigned to secondary pallet PAL022 are in the
middle of the pallet structure of pallet load PALOC, and goods
assigned to secondary pallet PAL023 are at the bottom of the pallet
structure of pallet load PALOC).
In this aspect, the aisles to
which the goods CU are assigned may not be arranged along a
respective specific path (see, e.g., paths 301, 302 in Fig. 3) for
unloading goods CU of a respective pallet load PALO, PALO' onto
the store shelves.
[0035] The above-described examples of
pallet
handling/downstacking methods in the order store 200 are exemplary
only.
It is again noted that the pallet loads PALOC, PALOC',
PALOA, PALOA' for each of the pallet handling/downstacking methods
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are generally referred to herein as pallet loads PALO. It is also
noted that the pallet load(s) PALO may be built in any suitable
manner by the material handling system 190 so that the goods on
the pallet load(s) PALO are arranged according to any suitable at
least one order pallet to order store affinity characteristic 166,
166' for the pallet load packages distribution methods described
herein. It is noted that the store affinity pallet load resolution
(as described herein) is decoupled from the storage array 130
disposition and material handling system 190 throughput of cases
CU to the palletizer 162. Here, the output of cases CU from the
storage array 130 by the material handling system 190 is selected
to conform to or otherwise depends on (is based on) the store
affinity pallet load resolution.
In one or more aspects, the
throughput of cases CU output by the material handling system 190
may be effected in a manner similar to that described in United
States patent application number 17/091,265 filed on November 6,
2020 and titled "Pallet Building System with Flexible Sequencing,"
the disclosure of which is incorporated herein by reference in its
entirety.
In accordance with the aspects of the present
disclosure, the case CU disposition within the storage array 130
may be freely optimized for optimum throughput separate from
resolution and building of the store affinity pallet load PALO.
An example of throughput optimization can be found in United States
patent number 9,733,638 issued on August 15, 2017 and titled
"Automated Storage and Retrieval System and Control System
Thereof," the disclosure of which is incorporated herein by
reference in its entirety.
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[ 0 "-6] Referring to Fig. 1, the material handling system 190
may be disposed in a retail distribution center or warehouse 199,
for example, to fulfill orders received from retail stores (e.g.,
order stores 200 - see Figs. 2-4) for replenishment goods shipped in
cases, packages, and or parcels. The terms case, package and parcel
are used interchangeably herein and as noted before may be any
container that may be used for shipping and may be filled with one
or more product units by the producer. Case or cases as used herein
means case, package or parcel units not stored in trays, on totes,
etc. (e.g. uncontained). It is noted that the case units CU may include
cases of items/unit (e.g. case of soup cans, boxes of cereal, etc.)
or individual item/units that are adapted to be taken off of or placed
on a pallet. In accordance with the present disclosure, case units
(e.g. cartons, barrels, boxes, crates, jugs, shrink wrapped trays
or groups or any other suitable device for holding goods) may have
variable sizes and may be used to hold goods in shipping and may
be configured so they are capable of being palletized for shipping.
Case units CU may also include totes, boxes, and/or containers of
one or more individual goods, unpacked/decommissioned (generally
referred to as breakpack goods) from original packaging and placed
into the tote, boxes, and/or containers (collectively referred to
as totes) with one or more other individual goods of mixed or common
types at an order fill station. It is noted that when, for example,
incoming bundles or pallet loads PALN (e.g. from manufacturers or
suppliers of case units arrive at the material handling system 190
for replenishment of the goods stored within a storage array 130 of
the material handling system 190, the content of each pallet load
PALN may be uniform (e.g. each pallet holds a predetermined number
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of the same item - one pallet holds soup and another pallet holds
cereal). As may be realized, the cases of such pallet load PALN
load may be substantially similar or in other words, homogenous
cases (e.g. similar dimensions), and may have the same SKU
(otherwise, as noted before the pallets may be "rainbow" pallets
having layers formed of homogeneous cases).
[0037]
As pallet loads PALO leave the material handling system
190, with cases or totes filling store replenishment orders, the
pallet loads PALO may contain any suitable number and combination
of different case units (e.g. each pallet may hold different types
of case units - a pallet holds a combination of canned soup, cereal,
beverage packs, cosmetics and household cleaners).
The cases
combined onto a single pallet may have different dimensions and/or
different SKU's.
[0038]
The material handling system 190 generally includes a
storage array 130 and an automated package transport system 195.
The storage array 130 includes storage spaces 130S for holding
case units CU therein.
The automated transport system 195 is
communicably connected to the storage array 130 for storing case
units CU within the storage spaces 130S of the storage array 130
and for retrieving case units CU from the storage spaces 130S of
the storage array 130.
[0039]
An automated palletizer 162, 162' includes an automated
package pick device 162D (e.g., robot arm, gantry picker, etc.)
capable of moving case units CU from a package deposit section
(such as out-feed transfer station 160) to a pallet (also referred
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to herein as a pallet base) to form a pallet load PALO from the
case units CU, where the pallet load PALO includes more than one
composite layers L1-Ln of case units CU. As described herein, the
more than one composite layers L1-Ln of case units CU are formed
of case units CU arranged in the pallet load PALO embodying at
least one pallet to order store affinity characteristic 166, 166'
for a predetermined method of pallet load packages distribution at
an order store 200 (see Figs. 2-4). The automated palletizer 162,
162' is communicably connected to the automated package transport
system 195. The automated package transport system 195 provides
individual case units CU from the storage array 130 to the
automated palletizer 162 for forming the pallet load PALO, where
the pallet load PALO includes more than one composite layers Ll-
Ln of case units CU. The individual case units CU from the storage
array 130 from which the pallet load PALO is built have a case
dimension (e.g., any one or more of a case length, a case width,
and
a case height), where the case dimension (s) have a
substantially Gaussian distribution or a substantially stochastic
probability as represented by a normal probability curve as
illustrated in Fig. 17.
Fig. 17 is a graph illustrating the
variation of case dimensions (e.g. length, height and width) within
a representative population of cases CU such as may be found in
the material handling system 190 and used to generate mixed case
pallet loads PALO according to customer replenishment orders (as
described herein). As may be realized, the orders may result in
mixed case pallet loads PALO including many cases with dimensions
from disparate portions of the dimension spectrum illustrated in
Fig. 17.
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[ 0 0 4 0 ]
A controller 164, 164' is operably connected to the
automated palletizer 164. The controller 164, 164' is programmed
with non-transitory computer program code defining a pallet load
generator 165, 165' with at least one pallet to order store
affinity characteristic 166, 166' (as will be described herein),
for a predetermined method of pallet load PALO case unit CU
distribution at the order store 200.
As described herein, the
pallet load generator 166, 166' is configured so that the pallet
load PALO is formed by the automated palletizer 162 of case units
CU arranged in the pallet load PALO embodying the at least one
pallet to order store affinity characteristic 166, 166'.
[0041]
In greater detail now, and with reference still to Fig.
1, the material handling system 190 may be configured for
installation in, for example, existing warehouse structures or
adapted to new warehouse structures. As noted before the material
handling system 190 shown in Fig. 1 is representative and may include
for example, in-feed and out-feed conveyors (e.g., transferring case
units from and to the respective depalletizer 162' and palletizer 162)
terminating on respective in-feed and out-feed transfer stations 170,
160, lift module(s) 150A, 150B, a storage array 130 (e.g., including
suitable structure such as racks, vehicle riding surfaces, storage
shelves, etc.), and a number of autonomous transport vehicles 110
(also referred to herein as "bots").
[0042]
It is noted that the material handling system 190 is formed
at least by the storage array 130 and the bots 110. In some aspects
the lift modules 150A, 150B also form part of the material handling
system 190; however in other aspects the lift modules 150A, 150B may
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form vertical sequencers in addition to the material handling system
as described in United States patent application number 17/091,265
filed on November 6, 2020 and titled "Pallet Building System with
Flexible Sequencing," the disclosure of which is incorporated herein
by reference in its entirety).
In alternate aspects, the material
handling system 190 may also include robot or bot transfer stations
140 that may provide an interface between the bots 110 and the lift
module(s) 150A, 150B.
[0043]
The storage array 130 includes any suitable structure that
forms multiple (stacked) storage levels 130L1-130Ln (see Fig. 1,
generally referred to as storage levels 130L or a storage level
130L, and where n is an integer that denotes an upper number of
storage levels present in the material handling system 190) of
storage rack modules where each level 130L includes respective
picking aisles 130A, storage spaces 130S, and transfer decks 130B
for transferring case units between any of the storage spaces 130S
of the storage structure 130 and a shelf of the lift module(s)
150A, 150B.
The storage spaces 130S are arranged along (or
alongside) one or more sides of each picking aisles 130A so that
bots 110 travelling along a picking aisle 130A have access to the
storage spaces 130S on either side of the picking aisle 130A.
[0044]
The picking aisles 130A are in one aspect configured to
provide guided travel of the bots 110 (such as along a vehicle
riding surface VRSR that includes bot guiding features such as
rails) while in other aspects the picking aisles are configured to
provide unrestrained travel of the loot 110 (e.g., along a vehicle
riding surface VRSU that is open and undeterministic with respect
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to bot 110 guidance/travel).
The transfer decks 130R have open
and undeterministic bot support travel surfaces VRS along which
the bots 110 travel under guidance and control provided by bot
steering (e.g., such steering being effected by one or more of
differential drive wheel steering, steerable wheels, etc.).
In
one or more aspects, the transfer decks 130B have multiple lanes
between which the bots 110 freely transition for accessing the
picking aisles 130A and/or lift modules 150A, 150B. The picking
aisles 130A, and transfer decks 130B also allow the bots 110 to
place case units CU into picking stock and to retrieve ordered
case units CU. In alternate aspects, each storage level 130L may
also include respective bot transfer stations 140 that provide a case
unit transfer interface between the bots 110 and the lift module(s)
150A, 150B.
[0045]
The bots 110 may be configured to place case units CU, such
as the above described retail merchandise, into picking stock in the
one or more levels 130L of the storage array 130 and then selectively
retrieve ordered case units CU for shipping the ordered case units CU
to, for example, an order store 200 (see, e.g., Figs. 2-4) or other
suitable location.
[0046]
The in-feed transfer stations 170 and out-feed transfer
stations 160 may operate together with their respective lift module(s)
150A, 150B for bi-directionally transferring case units CU to and from
one or more levels 130L of the storage structure 130. It is noted
that while the lift modules 150A, 150B may be described as being
dedicated inbound lift modules 150A and outbound lift modules 150B,
in alternate aspects each of the lift modules 150A, 150B may be used
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for both inbound and outbound transfer of case units from the material
handling system 190. Similarly, while the palletizers 162, 162' may
be described as being dedicated (inbound) depalletizers 162' and
(outbound) palletizers 162, in alternate aspects, each of the
palletizers 162, 162' may be used for both inbound and outbound
transfer of case units from the material handling system 190.
[0047] As may be realized, the material handling system 190 may
include multiple in-feed and out-feed lift modules 150A, 150B that are
accessible by, for example, bots 110 of the material handling system 190
so that one or more case unit(s), uncontained (e.g. case unit(s) are not
held in trays), or contained (within a tray or tote) can be transferred
from a lift module 150A, 150B to each storage space 130S on a respective
level 130L and from each storage space 130S to any one of the lift modules
150A, 150B on the respective level 130L. The hots 110 may be configured
to transfer the case units between the storage spaces 130S (e.g., located
in the picking aisles 130A or other suitable storage space/case unit
buffer disposed along the transfer deck 130B) and the lift modules 150A,
150B. Generally, the lift modules 150A, 150B include at least one
movable payload support that may move the case unit(s) between the in-
feed and out-feed transfer stations 160, 170 and the respective level
130L of the storage space 130S where the case unit(s) CU is stored and
retrieved. The lift module(s) may have any suitable configuration,
such as for example reciprocating lift, or any other suitable
configuration. The lift module(s) 150A, 150B include any suitable
controller (such as controller 120 or other suitable controller
coupled to controller 120, warehouse management system 2500,
and/or palletizer controller 164, 164') and may form a sequencer
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or sorter in a manner similar to that described in United States
patent application number 16/444,592 filed on June 18, 2019 and
titled "Vertical Sequencer for Product Order Fulfillment÷ (the
disclosure of which is incorporated herein by reference in its
entirety).
[0048]
The material handling system 190 may include a control
system, comprising for example one or more control servers 120
that are communicably connected to the in-feed and out-feed
conveyors and transfer stations 170, 160, the lift modules 150A,
150B, and the bots 110 via a suitable communication and control
network 180. The communication and control network 180 may have
any suitable architecture, which, for example, may incorporate
various programmable logic controllers (PLC) such as for
commanding the operations of the in-feed and out-feed conveyors
and transfer stations 170, 160, the lift modules 150A, 150B, and
other suitable system automation.
The control server 120 may
include high-level programming that effects a case management
system (CMS) managing the case flow through the material handling
system 190.
[0049] The network 180 may further include suitable
communication for effecting a bi-directional interface with the
bots 110.
For example, the bots 110 may include an on-board
processor/controller 1220. The network 180 may include a suitable
bi-directional communication suite enabling the bot controller
1220 to request or receive commands from the control server 120
for effecting desired transport (e.g. placing into storage
locations or retrieving from storage locations) of case units CU
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and to send desired hot 110 information and data including hot 110
ephemeris, status and other desired data, to the control server
120.
[0050] As seen in Fig. 1, the control server 120 may be further
connected to a warehouse management system 2500 for providing, for
example, inventory management, and customer order fulfillment
information to the CMS 120 level program. A suitable example of
a material handling system arranged for holding and storing case
units is described in U.S. Patent No. 9,096,375, issued on August
4, 2015 the disclosure of which is incorporated by reference herein
in its entirety.
[0051] Referring to Figs. 1-5, building pallet loads PALO In
accordance with at least one pallet to order store affinity
characteristic will be described in greater detail with respect to
the aspects of the present disclosure. As noted above, the at
least one pallet to order store affinity characteristic 166, 166'
is at least one (e.g., a predetermined customer affinity) for the
clustered aisle pallet load packages distribution method (see,
e.g., Fig. 2), the adjacent aisles pallet load package distribution
method (see, e.g., Fig. 3), and the mixed mode clustered and
adjacent aisles pallet load packages method (see, e.g., Fig. 4).
The at least one pallet to order store affinity characteristic
166, 166 may be stored in any suitable memory, such as a memory
of the control server 120 and/or palletizer 162, 162' as described
herein, and employed by the control server 120 and/or palletizer
162, 162' for generating the pallet loads PALO described herein.
The term aisle as used hereinafter refers to, unless otherwise
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noted, an order store aisle for which the pallet load PAtO is
destined.
[0052]
Referring to Figs. 1 and 5, an exemplary graph for a
sample order for pallet planning (see Fig. 5) is illustrated and
includes case units from one or more aisles of the order store 200
(Figs. 2-4).
In the exemplary sample order shown in Fig. 5 the
aisles are numbered 1-12. A total volume V1-V12 of case units CU
ordered for each respective aisle 1-12 is represented by a height
of the respective bar in the graph (each bar corresponding to a
respective aisle 1-12).
The volumes V1-V12 are illustrated as
fractional units relative to an expected total volume Vp of case
units CU on a full pallet load (e.g., the full pallet load having
maximum pallet load dimensions in length Lp, width Wp, and height
Hp).
The volume Vp is a product of the maximum dimensions Lp
(length), Wp (width), Hp (height) (e.g., of the space allocated
for case units CU on the pallet load), multiplied by the expected
volumetric efficiency E of packing products on the pallet load,
where:
[0053] Vp = Lp * Wp * Hp * E [eq.
1]
[0054]
Generally, the dimensions (e.g., length, width, height)
of the goods/case units CU are known where the case units CU have
a general cuboid shape. Here, the known dimensions of the case
units provide for the determination of the total volume Vp of case
units CU (e.g., a combined volume of the case units CU assigned to
any one given pallet load). As an example, and depending on the
computational method for planning individual pallet loads, the
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average total product volume on a pallet is statistically ahout
0.8 with a standard deviation of 0.03 of a volume of the outer
bounds of a pallet load having the dimensions Lp (length) x Wp
(width) x Hp (height) (e.g., about 80% of the pallet volume is
occupied by goods, while the rest is empty space between the
goods).
The expected efficiency E depends on the packing
algorithms of the computational method (such as those described
herein), which for state-of-the-art packing algorithms (such as
those of the computational methods described herein) and mixed
products, containing boxes of a variety of dimensions, should
generally exceed the value of about 0.8.
[0055]
Generally referring to Fig. 5, it can be seen that some
of the aisles 1-12 (see, e.g., aisle 2) may have a total volume
(such as volume V2 of aisle 2) that exceeds an expected (e.g.,
maximum) volume Vp of one pallet load PALO. Other aisles 1-12 may
have respective volumes that are small or smaller than (see volume
V9 of aisle 9) compared to the expected total volume Vp.
As
described herein, in accordance with the aspects of the present
disclosure, the pallet load generator 165, 165' (e.g., of the
control server 120 and/or palletizer 162, 162') is configured to
resolve the pallet load PALO in accordance with at least one pallet
to order store affinity characteristic 166, 166' so that the pallet
load PALO is one or more of:
[0056]
maximized with respect to at least one of a maximum
pallet load volume Vp and a maximum pallet load weight Wmax,
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[ 0 0 5 7 ]
has a maximum number of packages from a minimum number
of store aisles,
[0058]
generated to have a minimum number of pallet loads for
each store order,
[0059]
generated so that, for each pallet load destined for the
order store 200, the case units CU forming the pallet load
represent a minimum number of order store aisles, and
[0060]
generated so that, for each pallet load destined for an
order store 200, the resolved pallet load represents a minimum
number of order store aisles.
[0061]
With reference to Figs. 1, 2, 5, 6, 7, 8, and 12, the
pallet to order store affinity characteristic 166, 166' for the
clustered aisle pallet load packages distribution method will be
described in greater detail.
The clustered aisle pallet load
packages distribution method minimizes both of (1) the number of
pallets created from a given set of products and (2) an average
pallets-per-aisle ratio RPA. The pallets-per-aisle ratio RPA is
determined as a total count of instances of products from each
aisle on each pallet, divided by the total number of aisles. The
pallets-per-aisle ratio RPA may be understood as the number of
times the pallet load PALO will be present in any aisle, or the
number of aisles in which the pallet load PALO is present to
unload.
This number is sought to be minimized (e.g., brought
towards 1).
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[ 0 0 6 2 ]
The pallets-per-aisle ratio RPA may he represented by a
pallet-aisle binary matrix PA as illustrated in Fig. 6. Here the
pallet-aisle binary matrix PA has the number of rows equal to the
number of aisles (eight aisles are illustrated for exemplary
purposes) and the number of columns equal to the number of pallets
(four pallets are illustrated for exemplary purposes) planned for
a given order. If a product from the aisle (i) is present in the
pallet (j), then the element of the pallet-aisle binary matrix
PA[i,j] at the row (i) and the column (j) is equal to 1, otherwise
the element of the pallet-aisle binary matrix PA[i,j] at the row
(i) and the column (j) is equal to 0. The pallets-per-aisle ratio
RPA is determined as the sum of all elements of the pallet-aisle
binary matrix PA divided by the number of aisles with products
present in the order. It is noted that if every aisle is present
only in one pallet, then the pallets-per-aisle ratio RPA is equal
to 1. The pallets-per-aisle ratio RPA is higher with more products
from some aisles scattered across several pallets. Where products
from every aisle are present in every pallet, then the pallets-
per-aisle ratio RPA is equal to the number of pallets.
In the
example illustrated in Fig. 6, the pallets-per-aisle ratio RPA is
equal to 11/8 or 1.375. Here, the pallets-per-aisle ratio RPA is
greater than 1 because the products from aisle 1 are present in
pallets 1 and 3, products from aisle 6 are present in pallets 2
and 4, and products from aisle 8 are present in pallets 1 and 4.
[0063]
In the clustered aisle pallet load packages distribution
method all single-aisle pallets are planned for aisles with a
volume of case units CU exceeding an expected pallet volume Vp or
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maximum pallet weight Wmax as will be described in greater detall
herein. Remaining pallets for filling a store order are planned
from combinations of aisles where such planning employs a repeating
dual loop determination, such as illustrated in Fig. 12A where for
each aisle combination iteration TAi (e.g., nested within the
broader pallet build iteration Pj), a pallet is planned such that
the volume Vc(TAi) is maximized with respect to the expected pallet
volume Vp (e.g., so as to minimize a number of pallets in the
order) and the pallets-per-aisle ratio RPA is minimized (e.g., so
as to approach 1). Here, the repeating dual loop determination
iterates through combinations of aisles until a pallet being
planned is planned successfully (as described in greater detail
below) and iterates through pallets until a whole store order is
consumed (e.g., the order is filled) and there are no more case
units CU in the store order that are unplanned (i.e., not assigned
to a pallet load). Here, the order store affinity characteristic
166, 166' is informed by the repeating dual loop determination
where at least one loop of which relates order store aisles to
each other. At least the other loop of the repeating dual loop
determination determines available combinations of order store
aisles resolving arrangement of case units or packages CU in a
given pallet load PALO. The repeating dual loop determination is
illustrated in, for example, Figs. 7 and 12B and will be described
below with respect to a pallet load build in accordance with the
order store affinity characteristic for the clustered aisle pallet
load packages distribution method.
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[ 0 0 6 4 ]
The warehouse management server 2500 or the control
system 120 (or any other suitable controller of the warehouse 199)
receives a store order (Fig. 12B, Block 1200). Where, the
warehouse management server 2500 receives the store order, the
store order is conveyed to the control server 120 through the
network 180 or in any other suitable manner. The control server
120 commands the automated package transport system 195 to retrieve
the ordered goods from the storage array 130 for transport to the
palletizer 162.
For example, the hots 110 on one or more
predetermined storage levels 130L1-13Ln are commanded by the
control server 120 to retrieve ordered case units CU from
predetermined storage spaces 130S of the respective storage level
130L1-130Ln. The hots transport the retrieved case units CU from
the storage spaces 130S to the lift(s) 150B so that the retrieved
case units CU are output through the out-feed transfer station 160
in a predetermined order to the palletizer.
Here, the
predetermined order of case unit CU output is determined at least
in part by order store affinity characteristic 166, 166'.
[0065]
One or more of the control server 120 and palletizer 162
is/are configured to determine the pallet to order store affinity
characteristic 166, 166' (Fig. 12B, Block 1210) based on, for
example, the order store 200 that places the order. For example,
a palletizer controller 164, 164' of one or more of the control
server 120 and the palletizer 162 is configured with a pallet load
generator 165, 165'. For example, in one aspect, each respective
order store 200 may inform the pallet load generator 165, 165 of
the respective pallet to order store affinity characteristic 166,
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166' prior to placement of the order (such as when the order store
opens an account with the warehouse 199, or at any other suitable
time, and the pallet to order store affinity characteristic 166,
166' is communicated or entered into the warehouse management
system).
Here, the pallet load generator 166, 166' may include
any suitable table that relates each order store 200 with the
respective pallet to order store affinity characteristic 166,
166'.
In other aspects, the pallet to order store affinity
characteristic 166, 166' may be communicated to the pallet load
generator 165, 165' coincident with placing the order (such as an
entry in the order submission, where the pallet load generator
determines the pallet to order store affinity characteristic 166,
166' substantially directly from the order regardless of an
identity of the order store 200). As noted above, in this example
the pallet to order store affinity characteristic 166, 166' is for
the clustered aisle pallet load packages distribution method.
[0066]
The pallet load generator 165, 165' determines any
aisles that have a total volume of case units Vcomb that is greater
than the expected volume Vp of a pallet load PALO (Fig. 7 Block
700A) (in the example illustrated in Fig. 5, aisle 2 has a volume
V2 that is greater than the expected volume Vp). Alternatively,
the pallet load generator 165, 165' determines any aisles that
have a total weight of case units Wcomb that is greater than an
expected weight Wmax (e.g., a maximum weight) of a pallet load
PALO. Based on the presence of any aisles with a total volume of
case units Vcomb that is greater than the expected volume Vp or a
total weight of case units Wcomb greater than the expected weight
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Wmax (referred to collectively herein as the "aisles-in-excess"),
the pallet load generator 165, 165' plans pallet loads PALO formed
entirely with case units ordered for and belonging to the aisles-
in-excess (Fig. 7, Block 710).
In some aspects, there will be
some case units CU remaining from the aisles-in-excess (Fig. 7,
Block 720), which remaining case units are included in subsequent
pallet loads. For example, the pallet load generator 165, 165'
forms pallet load 1 (see Fig. 8) with a portion V2A of the case
unit volume V2 from Fig. 5, while a remaining portion V2B of the
case unit volume V2 from Fig. 5 is included in pallet load 5 as
will be described below.
[0067]
The subsequent pallet loads (or pallet loads where there
are no aisles-in-excess) are planned from one store aisle or
combinations of more than one store aisle. As described herein,
the aisle combinations are created computationally, by the pallet
load generator 165, 165', so as to minimize the pallet-per-aisle
ratio and maximize the case unit volume of each pallet load PALO.
Here, each of the available aisle combinations of the order store
aisles is determined based on a maximization of the pallet load
or, in other aspects as described herein, a combined maximization
of the pallet load and a contiguity or adjacency of aisles in the
available combination, where the maximization of the pallet load
is weighted higher than the contiguity or adjacency of the aisles.
[0068]
Each of the subsequent pallet loads have a total volume
of case units Vcomb that is less than the expected product volume
Vp of the pallet load PALO, and a total weight of case units Wcomb
that is less than the expected weight Wmax of the pallet load PALO.
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Each of the combinations of aisles may have different numbers of
aisles ranging from one aisle to a total number of aisles remaining
in the order. A list of allowed aisle combinations ALC (see Fig.
1) may be determined (Fig. 7, Block 730) by employing a binary
representation of an integer iterator, where the integer iterator
has a value k that ranges from 1 to 2Na-1, where Na is the number
of aisles remaining in the order. Each increment of this integer
iterator corresponds to a potential aisle combination as follows:
If the 111th bit from the least significant bit of the binary
representation of the integer iterator is 1, then the aisle m from
the remaining list of aisles is present in the combination, and if
the least significant bit is 0, then the aisle m is absent from
the combination.
[0069]
As an example of employment of the integer iterator,
assume a store order that has 5 aisles (there may be more or less
than five aisles) and the integer iterator is equal to 12, i.e.,
the twelfth iteration (noting that eleven iterations of a possible
31 iterations occurred prior to the twelfth iteration (where for
this example the integer iterator ranges from 1 to 31 as determined
by k = 2Na_i = 25-1 = 31 iterators/iterations), and that there may
be subsequent iterations after the twelfth iteration such as where
aisles remain in the order).
The binary representation of the
number 12 (i.e., the integer iterator) is 01100.
The number of
aisles, arranged in an order from highest to lowest, may be
arranged in a grid relative to the binary representation of the
integer iterator (so that the numbers of the aisles align with a
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corresponding number in the binary representation of the integer
iterator) as follows:
Aisle
4 3 2 1
Number
Bit
values of
0 1 1 0
0
integer
iterator
[0070] As noted above, where a bit of the integer iterator
corresponding to an aisle is 1 then case units CU from that aisle
are present in the combination of aisles. In the example provided
above, the bits of the integer iterator corresponding to aisles 4
and 3 are 1, meaning that case units CU from aisles 4 and 3 are
included in the 12th iterative combination of aisles while aisles
5, 2, and 1 are excluded from the 12Lh iterative combination of
aisles.
[0071] For each value k of the integer iterator, the total
volume Vcomb and weight Wcomb of case units CU in the corresponding
aisle (e.g., the respective aisle combination for a given value k
of the integer iterator) is determined and compared, by the pallet
load generator 165, 165', with the expected pallet volume Vp and
maximum pallet weight Wmax. If any of the values of Vcomh and
Wcomb exceed the values of Vp and Wmax respectively, the aisle
combinations having at least one of Vcomb and Wcomb values
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exceeding the values of Vp and Wmax are discarded.
If both of
the values of Vcomb and Wcomb are less than the values of Vp and
Wmax respectively, the aisle combinations having both Vcomb and
Wcomb values less the values of Vp and Wmax are added to the list
of allowed aisle combinations ALC. Referring to the example above,
the combined volumes V3 and V4 of aisles 3 and 4, respectively,
must be less than or equal to the expected pallet volume Vp and
the combined weights W3 and W4 of aisles 3 and 4, respectively,
must be less than or equal to the maximum pallet weight Wmax in
order to be included in the list of allowed aisle combinations
ALC.
[0072]
The list of allowed aisle combinations ALC may be sorted
in any suitable manner, such as in descending order of the total
(case unit) volume Vcomb of each of the aisle combinations.
Sorting the list of allowed aisle combinations in descending order
of total case volume Vcomb may provide for building the fewest
number of pallets for a given store order.
Here, the list of
allowed aisle combinations ALC serves as a list of candidate
combinations of products selected to plan a pallet load PALO in an
output pallet list for a given store order.
[0073]
An exemplary sorted list of allowed aisle combinations
ALC of ten aisles may be presented as follows:
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Aisle combination 1 Aisles 2, 3, 8 Vcomb/Vp = .99
Aisle combination 2 Aisles 1, 4, 6, 9 Vcomb/Vp = .98
Aisle combination 3 Aisles 3, 7 Vcomb/Vp = .98
Aisle combination 4 Aisles 4, 5, 10 Vcomb/Vp = .96
where the right-most column represents a total volume ratio of
case units of the respective aisles in the aisle combination (e.g.,
the combined volume Vcomb) relative to the expected pallet volume
VP.
[0074]
It is noted that in aspects where the number of aisles
included in a store order is large, each aisle may be subdivided
into any suitable number of aisle subdivisions, where a size of
the aisle subdivisions may depend on computational resources of
the pallet load generator 165, 165'.
The size of the aisle
subdivisions may also effect a least number of pallets
generated/output by the warehouse 199 for a given store order.
The aisle subdivisions may be grouped with other aisle subdivisions
to form store partitions in which each aisle subdivision is treated
as an aisle and the list of aisle combinations ALC is determined
in the manner described above for each of the store partitions.
[0075]
As noted above the pallet to order store affinity
characteristic for the clustered aisles pallet load packages
distribution method is informed by a repeating dual loop DRL
determination where at least one loop of which determines available
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combinations of order store aisles resolving arrangement of
packages in the pallet load and another at least one loop of which
relates order store aisles to each other. In the repeating dual
loop DRL pallet loads are planned by employing the list of aisle
combinations ALC.
[0076]
In one loop of the repeating dual loop DRL, the pallet
load generator 165, 165' determines available aisle combinations
resolving package arrangement in a pallet load (Fig. 12B, Block
1230). An entry from the list of aisle combinations ALC having
the highest Vcomb/Vp ratio (which in the example above is aisle
combination 1) is chosen (Fig. 7, Block 735) and a pallet load
PALO is planned with the case units CU corresponding to the aisles
in the chosen aisle combination (Fig. 7, Block 740), thus effecting
an optimization with respect to the minimum number of pallets.
Where a pallet plan for the chosen aisle combination does not fit
all case units from the aisles in the chosen aisle combination in
the pallet load PALO (which means that some of the case units of
the corresponding aisles remain unpacked for inclusion in other
pallets, confirming or verifying optimization of the pallet-per-
aisle ratio RPA - Fig. 7, Block 745), the pallet plan is discarded
(Fig. 7, Block 750).
A next entry from the list of aisle
combinations ALC having the next highest Vcomb/Vp ratio (e.g., the
next aisle combination, which in the example above is the aisle
combination 2) is chosen (Fig. 7, Block 735), thus again effecting
an optimization with respect to the minimum number of pallets. A
pallet load PALO is planned with the case units CU corresponding
to the aisles in the next aisle combination (Fig. 7, Block 740),
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where 131ocks 740, 745, 750, 735 are repeated (for subsequent aisle
combinations, e.g., aisle combination 2, aisle combination 3,
aisle combination 4, and so on) until a pallet plan for a chosen
entry from the list of aisle combinations ALC succeeds in packing
all case units for the corresponding aisles in the pallet load
(e.g., the pallet load PALO), again confirming or verifying
optimization of the pallet-per-aisle ratio RPA. Here, the aisle
combinations are analyzed by the pallet load generator 165, 165'
in sequence, via a repeating dual loop DRL determination, with
respect to pallet planning until a planning solution is found that
will include all case units ordered for the aisles in the aisle
combination.
[0077] As an example of the sequential analyzation of the aisle
combinations, using the aisle combinations 1-4 above, the pallet
load generator 165, 165' first analyzes aisle combination 1 (aisles
2, 3, 8) to determine whether all ordered case units CU for aisles
2, 3, and 8 will fit in one pallet load having the maximum volume
Vp and maximum weight Wmax. For exemplary purposes assume that
not all ordered case units for aisles 2, 3, and 8 will fit in one
pallet load, and as such the next aisle combination in the aisle
combination sequence (e.g., aisle combination 2) is analyzed.
Here, the pallet load generator 165, 165' analyzes aisle
combination 2 (aisles 1, 4, 6, and 9) to determine whether all
ordered case units CU for aisles 1, 4, 6, and 9 will fit in one
pallet load having the maximum volume Vp and maximum weight Wmax.
For exemplary purposes assume that all ordered case units for
aisles 1, 4, 6, and 9 will fit in one pallet load, and as such the
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determination loop sequentially analyzing the aisle dombination is
stopped and the remaining aisle combinations (e.g., aisle
combinations 3 and 4) are not analyzed. Any subsequent pallet
load, as described below, will be generated with an updated set of
aisle combinations (that is separate and distinct from the previous
set of aisle combinations and that excludes the aisles for which
all ordered case units have been assigned to a pallet load).
[0078] The successful pallet plan (which in the above example
is aisle combination 2) forms the planned pallet load PALO and is
added to an output list (Fig. 7, Block 755) that is executed by
the automated package transport system 195 such that the automated
package transport system 195 picks and sorts the case units CU in
the planned pallet load PALO (Fig. 123, Block 1220) for building
of the planned pallet load at the palletizer 162 (Fig. 12B, Block
1250). In some aspects, case unit picking and pallet building for
a given store order may occur substantially simultaneously with
the planning of subsequent pallet loads in that store order, while
in other aspects, case unit picking and pallet building may occur
after all pallets are planned for the store order.
[0079] In another loop of the repeating dual loop DRL, where a
planned pallet load PALO is successfully planned, the pallet load
generator 165, 165 determines if there are any case units CU from
any aisle in the store order that have not been included in a
(successfully) planned pallet load PALO (Fig. 7, Block 760). Where
there are no more case units CU, the pallet planning is stopped
(Fig. 7, Block 765) and the case units CU of the planned pallet
loads PALO for the store order are retrieved from storage and
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sorted by the automated package transport system 199 (Fig. 12R,
Block 1220) and the pallet loads PALO are built (Fig. 12B, Block
1250) by the palletizer 162. Where case units CU remain, another
(e.g., subsequent) pallet is planned for inclusion in the store
order (Fig. 7, Block 770). Here, the pallet load generator 165,
165' updates the relationships between the store aisles (Fig. 12B,
Block 1240; Fig. 7, Block 730) such that all combinations of aisles
containing any aisle fully consumed by previous pallets (e.g.,
aisles for which all ordered case units have already been assigned
to a pallet load) are removed and an updated list of aisle
combinations ALC is generated (Fig. 7, Block 730). The repeating
dual loop DRL continues until there are no case units CU left
unplanned for any aisle of the store order (i.e., all ordered case
units are assigned to a pallet load). The pallet load generator
165, 165' is configured to relate each store aisle to each other
(Fig. 12B, Block 1240, see also Fig. 7, Block 730 described herein)
to one or more of minimize the total number of pallet loads in the
order and minimize the pallet-per-aisle ratio RPA. It is noted,
as described herein, the term "aisle" as used herein generally
denotes both an order store aisle and a product group to which an
integer value is assigned. As such, the order store aisles (e.g.,
physical location in the order store) are related to each other by
at least one of an aisle to aisle affinity characteristic and a
product group type to product group type affinity characteristic.
[0080] Fig. 8 is an exemplary store order 800 planned with the
pallet load generator 165, 165' employing the clustered aisle
pallet load packages distribution method described above. In this
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exemplary order ROO, the volume of case units in each aisle
illustrated in Fig. 5 are shown included in the respective pallet
loads (e.g., pallet 1-pallet 6), where each pallet load is
sequentially planned (as described above) so as to have a volume
Vcomb that is less than the maximum pallet volume Vp. As can be
seen in Fig. 8, the volume V2 corresponding to case units CU
ordered for aisle 2 is divided (as described above) among pallet
loads 1 and 5 such that pallet load 1 is fully consumed by case
units ordered for aisle 2.
It is noted that the last planned
pallet load (e.g., pallet load 6) may have a combined volume Vcomb
that is smaller than the previously planned pallet loads (e.g.,
pallet loads 1-5) because the last pallet load includes case units
for aisles that were not included in the previous aisle
combinations for previously planned pallet loads 1-5, which
previously planned pallet loads were optimized for volume or
weight, effecting an optimization of the minimum number of pallets
and/or an optimization of the pallet-per-aisle ratio RPA.
[0081]
In accordance with the clustered aisle pallet load
packages distribution method, the generated pallet load(s) PALO
are built by the palletizer 162 and shipped (Fig. 12B, Block 1260)
to the order store 200.
The pallet load(s) PALO arrive at the
order store 200 from the warehouse 199. Referring to Fig. 2, the
pallet load(s) PALO are generally received in a loading dock area
222 of the order store 200.
Each of the pallet load(s) PALO
includes products from several physical locations (e.g., aisles,
departments, sections, etc.) of the order store 200. For exemplary
purposes, these physical locations will be referred to herein as
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al sl es . It
s noted that whi e al sl es 1-4 and al sles 11-14 are
illustrated in Fig. 2, the order store may have any suitable number
of aisles.
In the clustered aisle pallet load packages
distribution method the case units CU stored on a pallet load PALO
are unloaded (e.g., manually or with automation, such as an
automated depalletizer similar to those described herein with
respect to palletizers 162, 162') from the pallet load PALO onto
separate and distinct secondary pallet loads PAL021, PAL022,
PAL023 (three are shown for exemplary purposes and it should be
understood that there may be more or less than three secondary
pallet loads). Here, each of the secondary pallet loads PAL021,
PAL022, PAL023 includes case units CU from separate single aisles.
For example, pallet load PAL021 includes only case units CU
assigned to aisle 1, pallet load PAL022 includes only case units
CU assigned to aisle 3, and pallet load PALO 23 includes only case
units CU assigned to aisle 12.
The secondary pallets PAL021,
PAL022, PAL023 are moved (e.g., manually and/or with an automated
conveyance) from the loading dock area 222 to the respective
assigned aisle in the shopping area 224 of the order store 200
where the case units CU of the respective secondary pallet load
PAL021, PAL022, PAL023 are unloaded and placed on the respective
store shelf 233 of the respective assigned aisle.
[0082]
In the clustered aisle pallet load packages distribution
method, the pallet load PALO may hold case units CU assigned to
aisles that are located spatially distant (e.g., far) from one
another in the order store 200. As described above, unloading of
the case units CU assigned to a respective aisle onto a respective
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secondary pallet load PAL021, PAL022, PAL023 is such that the
pallet load PALO holding case units CU assigned to aisles that are
located spatially distant (e.g., far) from one another has
substantially little to no impact on the restocking/stocking of
the store shelves 233. Here, in the clustered aisle pallet load
packages distribution method, case units CU from different aisles
may be assigned to a common pallet load PALO (regardless of aisle
proximity) to maximize the number of full-size pallet loads (e.g.,
pallet loads having the maximum pallet load dimensions and/or
weight), and to minimize the number of pallet loads PALO on a
conveyance that moves the pallet loads PALO from the warehouse 199
to the order store 200.
[0083]
Referring now to Figs. 1, 4, 5, 9, 12, and 13, the pallet
to order store affinity characteristic for the mixed mode clustered
and adjacent aisles pallet load packages distribution method will
be described in greater detail.
The mixed mode clustered and
adjacent aisles pallet load packages distribution method minimizes
both of (1) the number of pallets created from a given set of
products and (2) an average pallets-per-aisle ratio RPA, while
minimizing a distance between shelf locations of case units
assigned to each pallet.
For purposes of description, aisle
numbers that are numerically close to each other are also spatially
close to each other (e.g., aisles 10 and 11 are near one another
while aisle 60 is far from both aisles 10 and 11). Here the above-
described clustered aisle pallet load packages distribution method
is modified so that pallet loads are planned (e.g., based on a
contiguity or adjacency of one order store aisle to another order
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store aisle) where, in the order store 200, products on a common
pallet are unloaded into aisles that are contiguous or adjacent
one another.
[0084] In the mixed mode clustered and adjacent aisles pallet
load packages distribution method orders are placed by the order
store 200 and the at least one store order affinity characteristic
is determined in the manner described above with respect to Fig.
12B, Blocks 1200 and 1210. Blocks 700A, 700B, 710, 720 of Fig. 13
are the same as the similarly numbered blocks in Fig. 7 described
above. As such, whole pallets are planned from aisles with case
unit volumes that are greater than the pallet load volume Vp and/or
weights that are greater than the maximum pallet load weight Wmax,
where the remaining case units ordered for those aisles are
included in the aisle combination analysis (Figs. 13, Blocks 700A,
700B, 710, and 720) in the manner described above.
The aisle
combinations for the mixed mode clustered and adjacent aisles
pallet load packages distribution method are also determined in
the manner described above with respect to Fig. 7, Block 730 (see
also Fig. 12B, Block 1220); however, the determined aisle
combinations are sorted by a score S that accounts for the volume
of the ordered case units for a given aisle, the weight of the
ordered case units for a given aisle, and the closeness of aisles
included in a planned pallet (Fig. 13, Block 1330). For example,
the score S may be determined by the following equation:
ilicomb)
[ 0 0 8 5 ] S= vp
[eq. 2]
(d0+maxAisle¨minAisle)
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[ 0 8 ] where minAisle and maxAisle are the smallest and largest
aisle numbers included in a given aisle combination, and dO is
greater than 0 and is a parameter reflecting the relative
importance of aisle spread/distance (e.g., store friendliness)
versus the volume of case units in a pallet load. As can be seen
from equation 2, for a small values of dO, the aisle
spread/distance is more important than the volume of case units in
a pallet load, and for large values of dO the volume of case units
the volume of cases in a pallet load is more important than the
spread/distance between aisles assigned to the pallet load. The
determined aisle combinations (see Fig. 7, block 730) are weighted
or scored with the score S and are sorted based on the score S
(Fig. 13, Block 1330). The repeating dual loop DRL is performed
for planning pallets in the manner described above with respect to
Fig. 7, Blocks 735, 740, 745, 750, 755, 760, 765, 770 (see also
Fig. 12B, Block 1230) so as to effect optimization with respect to
the minimum number of pallets and verify/confirm optimization of
the pallet-per-aisle ratio RPA; however, for each subsequent
pallet the updated aisle combinations are again scored with the
score S and sorted based on the score S. The ordered case units
are picked and the planned pallet loads PALO are built and shipped
to the order store in the manner described above with respect to
Fig. 12B, Blocks 1240, 1250, and 1260.
[0087] Fig. 9 is an illustrative example of planned pallet loads
(e.g., pallet 1 - pallet 7) determined with the mixed mode
clustered and adjacent aisles pallet load packages distribution
method. In this illustrative example, the planned pallet loads
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are determined from an order having the aisles and respective case
unit volumes illustrated in Fig. 2. As can be seen in Fig. 9, the
first pallet load is planned from the portion of case unit volume
V2A from aisle 2 alone and all other planned pallet loads in the
store order have a volume Vcomb that is less than the expected
volume Vp of a pallet load (as described above). In accordance
with the mixed mode clustered and adjacent aisles pallet load
packages distribution method, planned pallet load 1 includes only
the volume of case units V1 assigned to aisle 1. Planned pallet
loads 2 includes the volume of case units V2B and V3 assigned to
aisles 2 and 3. Planned pallet load 4 includes the volume of case
units V4 and V7 assigned to aisles 4 and 7, noting that planned
pallet load 4 produces a break in a sequence of aisles, but this
break is not a large one as aisle 4 is but 3 aisle away from aisle
7, which conforms with the object of the mixed mode clustered and
adjacent aisles pallet load packages distribution method.
The
planned pallet load 5 includes the volumes of case units V5 and V6
assigned to aisles 5 and 6. The planned pallet load 6 includes
the volumes of case units V8-V11 assigned to aisles 8-11.
The
planned pallet load 7 includes the volume of case units V12
assigned to aisle 12.
[0088]
Referring also to Fig. 14, in some aspects of the mixed
mode clustered and adjacent aisles pallet load packages
distribution method, a maximum (or average) distance MDmax,
generally expressed in terms of a difference between aisle numbers,
between aisles for ordered case units CU assigned to any given
pallet may be specified by an order store 200. This aspect of the
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mixed mode clustered and adjacent aisles pallet load packages
distribution method is the same as that described above; however,
aisle combinations that include aisles having a distance between
aisles greater than the maximum distance MDmax are
excluded/discarded prior to sorting the list of aisle combinations
(see Fig. 14, Block 1430).
[0089] As described herein with respect to Fig. 15, in other
aspects of the mixed mode clustered and adjacent aisles pallet
load packages distribution method, a pair-wise relationship
between aisles p and q may be specified by an order store 200.
The relationship between the aisles p and q may be expressed as an
aisle affinity matrix A[p,q], where p and q belong to a set of all
aisles present in an order. The aisle affinity matrix A[p,q] is
diagonally symmetric so that A[p,q] is equal to A[q,p]. The values
of the aisle affinity matrix A[p,q] should be substantially equal
to, or close to, 1 for "store friendly" aisles such that case units
CU for these aisles should be on a same (e.g., single) pallet load.
The values of the aisle affinity matrix A[p,q] should be
substantially equal to, or close to, 0 for "unfriendly" aisles the
case units CU of which aisles should be kept apart in different
pallet loads (as mentioned above, e.g., the separation of caustic
products (e.g., laundry detergent) and food items (e.g., baby
food)). The diagonal elements of the aisle affinity matrix A[p,q]
should be equal to 1, e.g., A[p,p]=1 for each p, with the
implication that any aisle is friendly with itself.
[0090] Employing the pair-wise relationship between aisles, the
mixed mode clustered and adjacent aisles pallet load packages
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distribution method remains as described above; however, the score
S is modified as shown in the following equation:
S= = (vcom))
[0091] + p * me an(A[p , q]) [eq.
3]
Vp
[0092]
for all {p,q} belonging to a given combination of aisles.
In equation 3, the expression p is greater than or equal to 0 and
is a multiplier that shows the relative importance of pallet
volumes (e.g., minimization of the total number of pallets) and
friendliness between aisles included in a given combination of
aisles.
For smaller values of p, aisle friendliness is less
important compared to the minimization of the total number of
pallets; while fur larger values of p friendliness is more
importance compared to the minimization of the total number of
pallets.
In the manner described above (see Fig. 13), the
determined aisle combinations are scored and sorted in a descending
order according to the scoring, and starting from the first aisle
combination in the sorted list of aisle combinations, a pallet
load is planned for each sequential aisle combination until a
successful pallet load is planned, again optimizing the minimum
number of pallets and verifying/confirming optimization of the
pallet-per-aisle ratio RPA.
[0093]
Referring to Figs. 1, 3, 5, 10, 11, 12, and 15, the
pallet to order store affinity characteristic for the adjacent
aisles pallet load packages distribution method will be described
in greater detail.
The adjacent aisles pallet load packages
distribution method of pallet planning may be employed for
warehouse customers that transport ordered pallets to the store
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aisles for unloading the case units from the ordered pallets
directly to the store shelves. Here, as can be seen in Fig. 3,
each ordered pallet load PALOA, POLOA' is transported from one
aisle to another along respective transport paths 300, 302 for
unloading the case units.
The transport paths 300, 302 travel
through the aisles in a contiguous sequence of aisles (e.g., pallet
load PALOA travels through contiguous aisles 1-3 and pallet load
POLOA' travels through contiguous aisles 11-13).
[0094]
In the adjacent aisles pallet load packages distribution
method the selection of contiguous or adjacent aisles is
prioritized when planning a pallet load, while the total number of
pallets planned for any given order is minimized and excessive
splitting of aisles between pallets is substantially avoided.
Where aisles are split between two pallets, no more than one aisle
is split between the two pallets. An exemplary illustration of
pallet loads planned with a "pure" adjacent aisles pallet load
packages distribution method is shown in Fig. 10. As with the
other pallet load packages distribution methods, aisles having a
volume greater than the predetermined volume Vp of a pallet load
(or a weight greater than the maximum weight Wmax of a pallet load)
are selected and assigned to a full/whole pallet (see Fig. 5 where
aisle 2 has a volume V2 greater than the volume Vp of a pallet
load) until the volume or weight remaining in the respective aisle
is less than the volume Vp or weight Wmax. As can be seen in Fig.
10, pallet load 1 is fully consumed by a portion V2A of the volume
V2 of aisle 2. As described herein, with full pallet loads planned
from the aisles-in-excess, all remaining volumes and weights of
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the aisles in the order (e.g., volumes and weights of the case
units ordered for each respective aisle) are less than volume Vp
and weight Wmax of a pallet load. As such, each aisle has a case
units quantity that is expected to fit in a single pallet load and
in many instances combined with other case units from other aisles
in a single pallet load effecting minimization of the number of
pallets and the pallets-per-aisle ratio RPA.
[0095] In the adjacent aisles pallet load packages distribution
method the total number of pallet loads in the order and the
pallet-per-aisle ratio RPA are minimized, but to a lesser extent
compared to assigning case units CU to pallets in
contiguous/adjacent aisle sequences (e.g., each of the available
combinations of order store aisles is determined based more on a
contiguity or adjacency of the order store aisles in an available
combination and less on a maximization (either volume or weight)
of the pallet load. When planning the pallet loads according to
the adjacent aisles pallet load packages distribution method, some
aisles can be split between pallets, but only when avoiding splits
generates additional pallets, thereby increasing the overall
number of pallets planned for any given order.
[0096] If splitting of the aisle between pallet loads is not
allowed, the total number of pallets may increase. For example
Fig. 11 illustrates a store order (e.g., such as illustrated in
Fig. 2) planned with the adjacent aisles pallet load packages
distribution method without splitting case units from an aisle
between pallet loads (with the exception of any aisles-in-excess,
such as aisle 2, where a portion of the case units for each aisle-
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in-excess is consumed by a whole pallet load and the remainder of
case units are distributed among the remaining pallet loads in
accordance with the adjacent aisles pallet load packages
distribution method).
In Fig. 11, the resulting order plan
includes seven pallet loads, which is the same number of pallet
loads as the mixed mode clustered and adjacent aisles pallet load
packages distribution method, but is one more pallet load than
that of the clustered aisle pallet load packages distribution
method (noting the examples of which distribution methods are based
on the case unit order for the aisles shown in Fig. 5).
It is
also noted, as can be seen in Fig. 11, that without splitting
aisles between pallet loads more pallets than not have case unit
volumes below the maximum volume Vp of the respective pallet load,
while in both the mixed mode clustered and adjacent aisles pallet
load packages distribution method and the clustered aisle pallet
load packages distribution method (with the exception of the last
planned pallet load) have case unit volumes closer to the volume
Vp allowed for a pallet load.
[0097] To increase the average pallet volume and
reduce/minimize the number of pallets planned, while prioritizing
contiguous/adjacent aisle planning (e.g. store-friendliness),
splitting of the case units CU from some aisles is performed in
the pallet planning.
Here, the adjacent aisles pallet load
packages distribution method may be "modified" to employ
thresholds Vp0 and Vpl where:
[0098] Vp0 < Vpl < Vp [eq.
4]
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[ 0 0 9 9 ] The values of Vp0 and Vpl optimi7e the combination of
pallet volumes (and minimize the number of pallets) and the number
of split aisles. The values for Vp0 and Vpl should be reasonably
close to Vp, for example:
[0100] Vp0 -.95 * Vp [eq.
5]
[0101] and
[0102] Vpl = .98 * Vp [eq.
6]
[0103] The values of Vp0 and Vpl are generally held constant
(e.g., not changed during the pallet planning iteration loops
described herein), but may be adjusted for particular order
profiles. For example, very large case units may warrant a
reduction in Vp0 and Vpl because it is more likely that some case
units will not fit in a given pallet load, while small cases may
warrant an increase in Vp0 and Vpl as it is more likely that the
case units will fit in a given pallet load.
[0104] In the adjacent aisles pallet load packages distribution
method orders are placed by the order store 200 and the at least
one store order affinity characteristic is determined in the manner
described above with respect to Fig. 12B, Blocks 1200 and 1210.
As described above, aisles having a volume greater than the
predetermined volume Vp of a pallet load (or a weight greater than
the maximum weight Wmax of a pallet load) are selected and assigned
to a full/whole pallet. The number of pallets Np0 for the order
is determined (Fig. 15, Block 1500) by the pallet load generator
165, 165' based on the remaining case unit volume Vrem and weight
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Wrem and the expected product volume Vp and maximum weight Wmax in
one pallet load according to the following equation:
[0105] NpO = max (ceil (¨vrem), ceil (¨wrem)) [eq.
7]
Vp Wmax
[0106]
The pallet load generator 165, 165' relates the aisles
with each other (Fig. 12B, Block 1220, which in this example is a
sequential aisle relationship) and determined aisle combinations
that resolve a case unit arrangement in a pallet load (Fig. 12B,
Block 1230).
For example, the pallet load generator 165, 165'
determines aisle combinations for the "next" pallet load (Fig. 15,
Block 1505, where the "next" pallet load is the pallet load
currently being planned).
Here, the aisles are selected
sequentially (e.g., i, 1+1, 1+2 ...) and for each added aisle (Fig.
15, Block 1510) the cumulative case unit volume Vcomb and the
cumulative pallet weight Wcomb are updated (Fig. 15, Block 1515).
Where, the cumulative volume Vcomb is less than or equal to Vp0
and cumulative weight Wcomb is less than or equal to Wmax (Fig.
15, Block 1520), a next aisle in the sequence of aisles is added
to the aisle combination (Fig. 15, Block 1510), effecting
verification/confirmation of pallet-per-aisle ratio RPA
optimization.
Aisles are sequentially added to the aisle
combination until one of the cumulative volume Vcomb exceeds the
value Vp0 and the cumulative weight Wcomb exceeds the maximum
pallet load weight Wmax.
[0107]
Where one of the cumulative volume Vcomb exceeds the
value Vp0 and the cumulative weight Wcomb exceeds the maximum
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pallet load weight Wmax, the remaining product volume Vrem and
remaining product weight Wrem are updated (Fig. 15, Block 1530).
An updated estimate for the number of pallets Npl for the order is
determined by the pallet load generator 165, 165' in a manner
similar to that described above, but using the updated values of
Vrem and Wrem (i.e., the remaining volume and weight after the
last aisle selected in Block 1510 of Fig. 15 of a first nested
loop RL1 that includes blocks 1510, 1515, 1520 of Fig. 15, and
that is nested within the overall/broader loop illustrated in
blocks 1500-1580 and 1590 of Fig. 15) as follows:
[0108] Npl = max (ceil
(Vrem(upated)) ced = (Wrem(updated)))
, [eq.
8]
Vp Wmax
[0109] Where the total number of pallets Np0 determined before
the aisle selection for the next pallet load is the same as the
updated number of pallets Npl (i.e., Np0 = Np1+1, where the number
1 represents the current pallet) (Fig. 15, Block 1536), the
selection of aisles for the next pallet load is stopped and a
pallet load is planned (Fig. 15, Block 1565) from the aisle
combination effecting optimization with respect to the minimum
number of pallets.
[0110] Where the updated number of pallets Npl increases (i.e.,
Np0 < Np1+1), additional aisles in the sequence of aisles are added
to the aisle combination (Fig. 15, Block 1540) in a second nested
loop RL2 that Includes blocks 1540, 1545, 1550, 1555, 1560 of Fig.
15, and that is nested within the overall/broader loop illustrated
in blocks 1500-1580 and 1590 of Fig. 15. With the next sequential
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aisle added to the aisle combination (Fig. 19, Block 1540), the
cumulative case unit volume Vcomb and the cumulative pallet weight
Wcomb are updated (Fig. 15, Block 1545).
The remaining volume
Vrem and the remaining weight Wrem of case units in the order is
also updated (Fig. 15, Block 1550). An updated estimate for the
number of pallets Npl(updated) for the order is determined (Fig.
15, Block 1555) by the pallet load generator 165, 165' in the
manner described above (see equation 8), but using the updated
values of Vrem and Wrem determined in Block 1550 of Fig. 15. Here,
if any one of the following conditions (equations 9-11) is not
satisfied the recursive loop RL2 is repeated adding additional
aisles to the aisle combination:
[0111] Vcomb > Vpl [eq.
9]
[0112] Wcomb > Wmax [eq.
10]
[0113] or
[0114] Np0 = Np1(updated)+1 [eq.
11]
[0115] Where any one of the above conditions (equations 9-11)
is satisfied the selection of aisles for the next pallet load is
stopped and a pallet load is planned (Fig. 15, Block 1565) from
the aisle combination, again effecting optimization with respect
to the minimum number of pallets.
[0116] With the pallet load planned (Fig. 15, Block 1565),
unplanned products from the aisle combination (e.g., a split aisle
such as, e.g., aisle 6 which is split into case unit volumes V6A,
V6B and aisle 12 which is split into case unit volumes V12A, V12B)
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are added, by the pallet load generator 165, 165', to the remaining
products in the order (Fig. 15, Block 1570).
The pallet load
generator 165, 165' adds the planned pallet load (from Fig. 15,
Block 1565) to an output list of pallet loads (Fig. 15, Block 1575)
that effects the building of the pallet loads in the output list.
The pallet load generator 165, 165' determines if there are any
remaining case units CU in the order (Fig. 15, Block 1580), again
verifying/confirming optimization of the pallet-per-aisle ratio
RPA. Where there are no more case units the pallet load planning
for the order is stopped (Fig. 15, Block 1585) and the pallet loads
PALOA, PALOA' are built and shipped to the order store 200 in the
manner described above with respect to Fig. 12B, Blocks 1240, 1250,
and 1260. Where case units CU remain in the order the pallet count
of the order is updated (Fig. 15, Block 1590) and another pallet
is planned for the order in the manner described above, effecting
a minimization of the number of pallets.
[0117]
In the above adjacent aisles pallet load packages
distribution method, making the volume of selected case units
higher than the first threshold volume Vp0 may increase the
probability that at least one aisle will not be fully packed into
the pallet load currently being planned, such that a portion of
the at least one aisle will overflow into the next subsequent
pallet load that is planned. The overflow of case units from one
pallet load to the next subsequent pallet load will raise the value
of the pallet-per-aisle ratio RPA and, may lower the aisle
adjacency (e.g., an overall store-friendliness of the ordered
pallet loads). The values of Vp0 and Vpl can be adjusted, as noted
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above, to reflect importance of m1n1m17ing the total number of
pallets versus the pallet-per-aisle ratio RPA.
Higher values
(e.g., close to Vp) of both Vp0 and Vpl may reduce the expected
number of pallets, while lower values of both Vp0 and Vpl may
reduce the probability of splitting aisle between pallets (but may
increase the expected number of pallets).
[0118]
Fig. 11 illustrates a pallets loads, of an order, planned
with the adjacent aisles pallet load packages distribution method
described above. As noted above, the volumes illustrated in Fig.
11 are those same volumes corresponding to the aisles illustrated
in Fig. 5.
In accordance with the adjacent aisles pallet load
packages distribution method a portion V2A of the volume V2 of
aisle 2 consumes an entire/whole pallet load (e.g., pallet load 1)
while the remaining volume V2B of aisle 2 considered for pallet
planning in accordance with Figs. 12 and 15 (as described above).
It is noted that the volume V6 of aisle 6 is split between pallet
loads 4 and 5 while the volume V12 of aisle 12 is split between
pallet loads 6 and 7. The remaining volumes V1, V3, V4, V5, and
V7-V11 for aisles 1, 3, 4, 5, and 7-11, and the remaining volume
of aisle 2 are assigned to but one respective pallet load and each
of the pallet loads has an uninterrupted sequence of aisles
assigned to the pallet load. In this example, the total number of
pallet loads is seven (as in Fig. 10 with the pallet loads thereof
planned with a "pure" aisle adjacency, e.g., without employing the
threshold values Vp0, Vpl and the dual nested loops RL1, RL2);
however, in Fig. 11 the last pallet load (pallet load 7), has a
smaller volume compared to the last pallet load in Fig. 10, and
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may be placed on top of another pallet load in the order decreasing
the amount of floor space required to transport the ordered pallet
loads. It is noted that, generally, the "modified" adjacent aisles
pallet load packages distribution method (that allow for splitting
aisle case unit volumes between pallet loads) results in a smaller
number of planned pallet loads than the "pure" adjacent aisles
pallet load packages distribution method (that does not allow for
splitting aisle case unit volumes between pallet loads).
[0119]
Referring now to Figs. 1-4 and 16 a method for building
a pallet load PALO, in accordance with any one or more of the
clustered aisles pallet load packages distribution method, the
mixed mode clustered and adjacent aisles pallet load packages
distribution method, and the adjacent aisles pallet load packages
distribution method, will be described. Here, packages are placed
onto a pallet (see Fig. 1) to form a pallet load PALO (Fig. 16,
Block 1600).
Individual case units CU are provided from the
storage array 130, as described herein, to the automated palletizer
for forming the pallet load PALO, where the pallet load PALO
includes more than one composite layers L1-In of case units CU.
The pallet load PALO is formed of case units CU arranged in the
pallet load PALO embodying at least one pallet to order store
affinity characteristic 166, 166' (Fig. 16, Block 1610) for a
predetermined method of pallet load packages distribution at the
order store 200. As described herein, the at least one pallet to
order store affinity characteristic 166, 166' is at least one for
the clustered aisles pallet load packages distribution method, the
mixed mode clustered and adjacent aisles pallet load packages
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distribution method, and the adjacent aisles pallet load packages
distribution method at the order store.
[0120] In accordance with one or more aspects of the present
disclosure, a material handling system for handling and placing
packages onto pallets destined for an order store, the material
handling system includes: a storage array with storage spaces for
holding packages therein; an automated package transport system
communicably connected to the storage array for storing packages
within the storage spaces of the storage array and retrieving
packages from the storage spaces of the storage array; an automated
palletizer for placing packages onto a pallet to form a pallet
load, the automated palletizer is communicably connected to the
automated package transport system, the automated package
transport system is configured to provide individual packages from
the storage array to the automated palletizer for forming the
pallet load, the pallet load including more than one composite
layers of packages; and a controller operably connected to the
automated palletizer, the controller being programmed with a
pallet load generator with at least one pallet to order store
affinity characteristic, for a predetermined method of pallet load
packages distribution at the order store, the pallet load generator
being configured so that the pallet load is formed by the automated
palletizer of packages arranged in the pallet load embodying the
at least one pallet to order store affinity characteristic.
[0121] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is at least one for a clustered aisles pallet load
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packages distribution method, a mixed mode clustered and adjacent
aisles pallet load packages distribution method, and an adjacent
aisles pallet load packages distribution method at the order store.
[0122] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which relates order store aisles to each
other.
[0123] In accordance with one or more aspects of the present
disclosure, within determination of the at least one loop, order
store aisles are related to each other by at least one of an aisle
to aisle affinity characteristic and product group type to product
group type affinity characteristic.
[0124] In accordance with one or more aspects of the present
disclosure, the aisle to aisle affinity characteristic is a
distance separating one order store aisle from another order store
aisle, or a contiguity or an adjacency of one order store aisle to
another order store aisle.
[0125] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which determines available combinations of
order store aisles resolving arrangement of packages in the pallet
load.
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[ 0 1 26] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based on: a maximization of the pallet load,
or a combined maximization of the pallet load and a contiguity or
adjacency of aisles in the available combination, wherein the
maximization of pallet load is weighted higher than the contiguity
or adjacency of aisles.
[0127] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based more on a contiguity or adjacency of
order store aisles in an available combination and less on a
maximization of the pallet load.
[0128] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that the pallet load is maximized with respect
to at least one of a maximum pallet load volume and a maximum
pallet load weight.
[0129] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that the pallet load has a maximum number of
packages from a minimum number of order store aisles.
[0130] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
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characteristic so as to generate a minimum number of pallet loads
for each order store.
[0131] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that, for each pallet load destined for the order
store, the packages forming the pallet load represent a minimum
number of order store aisles.
[0132] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that, for each pallet load destined for the order
store, the resolved pallet load represents a minimum number of
order store aisles.
[0133] In accordance with one or more aspects of the present
disclosure, the pallet load generator is configured so as to
resolve each pallet load sequentially via a repeating dual loop
determination informing the at least one pallet to order store
affinity characteristic.
[0134] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a dual nested loop determination at
least one loop of which relates order store aisles to each other
or determines available combinations of order store aisles
resolving arrangement of packages in the pallet load.
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[ 0 135] Tn accordance with one or more aspects of the present
disclosure, an automated palletizer includes: an automated package
pick device capable of moving packages from a package deposit
section to a pallet to form a pallet load from the packages, the
pallet load including more than one composite layers of packages;
and a controller operably connected to the automated palletizer,
the controller being programmed with a pallet load generator with
at least one pallet to order store affinity characteristic, for a
predetermined method of pallet load packages distribution at the
order store, the pallet load generator being configured so that
the pallet load is formed by the automated palletizer of packages
arranged in the pallet load embodying the at least one pallet to
order store affinity characteristic.
[0136] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is at least one for a clustered aisles pallet load
packages distribution method, a mixed mode clustered and adjacent
aisles pallet load packages distribution method, and an adjacent
aisles pallet load packages distribution method at the order store.
[0137] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which relates order store aisles to each
other.
[0138] In accordance with one or more aspects of the present
disclosure, within determination of the at least one loop, order
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store aisles are related to each other by at least one of an aisle
to aisle affinity characteristic and product group type to product
group type affinity characteristic.
[0139] In accordance with one or more aspects of the present
disclosure, the aisle to aisle affinity characteristic is a
distance separating one order store aisle from another order store
aisle, or an contiguity or adjacency of one order store aisle to
another order store aisle.
[0140] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which determines available combinations of
order store aisles resolving arrangement of packages in the pallet
load.
[0141] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based on: a maximization of the pallet load,
or a combined maximization of the pallet load and a contiguity or
adjacency of aisles in the available combination, wherein the
maximization of pallet load is weighted higher than the contiguity
or adjacency of aisles.
[0142] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based more on a contiguity or adjacency of
order store aisles in an available combination and less on a
maximization of thc pallct load.
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[ 0 143] Tn accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that the pallet load is maximized with respect
to at least one of a maximum pallet load volume and a maximum
pallet load weight.
[0144] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that the pallet load has a maximum number of
packages from a minimum number of order store aisles.
[0145] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so as to generate a minimum number of pallet loads
for each order store.
[0146] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that, for each pallet load destined for the order
store, the packages forming the pallet load represent a minimum
number of order store aisles.
[0147] In accordance with one or more aspects of the present
disclosure, the pallet load generator resolves the pallet load in
accordance with the at least one pallet to order store affinity
characteristic so that, for each pallet load destined for the order
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store, the resolved pallet load represents a minimum number of
order store aisles.
[0148] In accordance with one or more aspects of the present
disclosure, the pallet load generator is configured so as to
resolve each pallet load sequentially via a repeating dual loop
determination informing the at least one pallet to order store
affinity characteristic.
[0149] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a dual nested loop determination at
least one loop of which relates order store aisles to each other
or determines available combinations of order store aisles
resolving arrangement of packages in the pallet load.
[0150] In accordance with one or more aspects of the present
disclosure, a method for building a pallet load includes: placing
packages onto a pallet to form a pallet load, where individual
packages are provided from a storage array to form the pallet load,
the pallet load including more than one composite layers of
packages; and wherein the pallet load is formed of packages
arranged in the pallet load embodying at least one pallet to order
store affinity characteristic for a predetermined method of pallet
load packages distribution at an order store.
[0151] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is at least one for a clustered aisles pallet load
packages distribution method, a mixed mode clustered and adjacent
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aisles pallet load packages distribution method, and an adjacent
aisles pallet load packages distribution method at the order store.
[0152] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which relates order store aisles to each
other.
[0153] In accordance with one or more aspects of the present
disclosure, within determination of the at least one loop, order
store aisles are related to each other by at least one of an aisle
to aisle affinity characteristic and product group type to product
group type affinity characteristic.
[0154] In accordance with one or more aspects of the present
disclosure, the aisle to aisle affinity characteristic is a
distance separating one order store aisle from another order store
aisle, or a contiguity or an adjacency of one order store aisle to
another order store aisle.
[0155] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which determines available combinations of
order store aisles resolving arrangement of packages in the pallet
load.
[0156] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
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aisles is determined based on: a maximization of the pallet load,
or a combined maximization of the pallet load and a contiguity or
adjacency of aisles in the available combination, wherein the
maximization of pallet load is weighted higher than the contiguity
or adjacency of aisles.
[0157] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based more on a contiguity or adjacency of
order store aisles in an available combination and less on a
maximization of the pallet load.
[0158] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that
the pallet load is maximized with respect to at least one of a
maximum pallet load volume and a maximum pallet load weight.
[0159] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that
the pallet load has a maximum number of packages from a minimum
number of order store aisles.
[0160] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so as to
generate a minimum number of pallet loads for each order store.
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[0161] Tn accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that,
for each pallet load destined for the order store, the packages
forming the pallet load represent a minimum number of order store
aisles.
[0162] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that,
for each pallet load destined for the order store, the resolved
pallet load represents a minimum number of order store aisles.
[0163] In accordance with one or more aspects of the present
disclosure, each pallet load is resolved sequentially via a
repeating dual loop determination informing the at least one pallet
to order store affinity characteristic.
[0164] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a dual nested loop determination at
least one loop of which relates order store aisles to each other
or determines available combinations of order store aisles
resolving arrangement of packages in the pallet load.
[0165] In accordance with one or more aspects of the present
disclosure, a pallet load includes: more than one composite layers
of packages stacked on a pallet base; wherein the more than one
composite layers of packages are formed of packages arranged in
thc pallct load cmbodying at lcast onc pallct to ordcr storc
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affinity characteristic for a predetermined method of pallet load
packages distribution at an order store.
[0166] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is at least one for a clustered aisles pallet load
packages distribution method, a mixed mode clustered and adjacent
aisles pallet load packages distribution method, and an adjacent
aisles pallet load packages distribution method at the order store.
[0167] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
characteristic is informed by a repeating dual loop determination
at least one loop of which relates order store aisles to each
other.
[0168] In accordance with one or more aspects of the present
disclosure, within determination of the at least one loop, order
store aisles are related to each other by at least one of an aisle
to aisle affinity characteristic and product group type to product
group type affinity characteristic.
[0169] In accordance with one or more aspects of the present
disclosure, the aisle to aisle affinity characteristic is a
distance separating one order store aisle from another order store
aisle, or a contiguity or an adjacency of one order store aisle to
another order store aisle.
[0170] In accordance with one or more aspects of the present
disclosure, the at least one pallet to order store affinity
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characteristic is informed by a repeating dual loop determination
at least one loop of which determines available combinations of
order store aisles resolving arrangement of packages in the pallet
load.
[0171] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based on: a maximization of the pallet load,
or a combined maximization of the pallet load and a contiguity or
adjacency of aisles in the available combination, wherein the
maximization of pallet load is weighted higher than the contiguity
or adjacency of aisles.
[0172] In accordance with one or more aspects of the present
disclosure, each of the available combinations of order store
aisles is determined based more on a contiguity or adjacency of
order store aisles in an available combination and less on a
maximization of the pallet load.
[0173] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that
the pallet load is maximized with respect to at least one of a
maximum pallet load volume and a maximum pallet load weight.
[0174] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that
the pallet load has a maximum number of packages from a minimum
numbcr of ordcr storc aislcs.
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[ 0 175] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so as to
generate a minimum number of pallet loads for each order store.
[0176] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that,
for each pallet load destined for the order store, the packages
forming the pallet load represent a minimum number of order store
aisles.
[0177] In accordance with one or more aspects of the present
disclosure, the pallet load is resolved in accordance with the at
least one pallet to order store affinity characteristic so that,
for each pallet load destined for the order store, the resolved
pallet load represents a minimum number of order store aisles.
[0178] In accordance with one or more aspects of the present
disclosure, each pallet load is resolved sequentially via a
repeating dual loop determination informing the at least one pallet
to order store affinity characteristicin accordance with one or
more aspects of the present disclosure, the at least one pallet to
order store affinity characteristic is informed by a dual nested
loop determination at least one loop of which relates order store
aisles to each other or determines available combinations of order
store aisles resolving arrangement of packages in the pallet
load.it should be understood that the foregoing description is
only illustrative of the aspects of the present disclosure.
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Various alternatives and modifications can he devised by tbose
skilled in the art without departing from the aspects of the
present disclosure.
Accordingly, the aspects of the present
disclosure are intended to embrace all such alternatives,
modifications and variances that fall within the scope of any
claims appended hereto.
Further, the mere fact that different
features are recited in mutually different dependent or
independent claims does not indicate that a combination of these
features cannot be advantageously used, such a combination
remaining within the scope of the aspects of the present
disclosure.
[0181] What is claimed is:
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