The present disclosure generally relates to material handling systems, and more particularly, to handling and placing goods onto pallets with the material handling system.
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.
The foregoing aspects and other features of the present disclosure are explained in the following description, taken in connection with the accompanying drawings, wherein:
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 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.
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 Pat. No. 8965559 issued on Feb. 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
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.
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 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.
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).
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. 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.
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
Referring also to
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.
Referring to
Referring to
Referring to
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 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 Pat. Application No. 17/091,265 filed on Nov. 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 Pat. No. 9,733,638 issued on Aug. 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.
Referring to
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.
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.
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 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
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′.
In greater detail now, and with reference still to
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 form vertical sequencers in addition to the material handling system as described in United States Pat. Application No. 17/091,265 filed on Nov. 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.
The storage array 130 includes any suitable structure that forms multiple (stacked) storage levels 130L1-130Ln (see
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 bot 110 (e.g., along a vehicle riding surface VRSU that is open and undeterministic with respect to bot 110 guidance/travel). The transfer decks 130B 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.
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.,
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 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.
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 bots 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 or sorter in a manner similar to that described in United States Pat. Application No. 16/444,592 filed on Jun. 18, 2019 and titled “Vertical Sequencer for Product Order Fulfillment” (the disclosure of which is incorporated herein by reference in its entirety).
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.
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 and to send desired bot 110 information and data including bot 110 ephemeris, status and other desired data, to the control server 120.
As seen in
Referring to
Referring to
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 average total product volume on a pallet is statistically about 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.
Generally referring to
With reference to
The pallets-per-aisle ratio RPA may be represented by a pallet-aisle binary matrix PA as illustrated in
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 maximum pallet weight Wmax as will be described in greater detail 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
The warehouse management server 2500 or the control system 120 (or any other suitable controller of the warehouse 199) receives a store order (
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′ (
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 (
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.
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. 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
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-1 = 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 corresponding number in the binary representation of the integer iterator) as follows:
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 12th iterative combination of aisles.
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 Vcomb and Wcomb exceed the values of Vp and Wmax respectively, the aisle combinations having at least one of Vcomb and Wcomb values 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.
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.
An exemplary sorted list of allowed aisle combinations ALC of ten aisles may be presented as follows:
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.
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.
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 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.
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 (
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 determination loop sequentially analyzing the aisle combination 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).
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 (
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 (
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 (
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 secondary pallet load PALO21, PALO22, PALO23 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.
Referring now to
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
where minAisle and maxAisle are the smallest and largest aisle numbers included in a given aisle combination, and d0 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 d0, the aisle spread/distance is more important than the volume of case units in a pallet load, and for large values of d0 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
Referring also to
As described herein with respect to
Employing the pair-wise relationship between aisles, the mixed mode clustered and adjacent aisles pallet load packages distribution method remains as described above; however, the score S is modified as shown in the following equation:
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 for larger values of p friendliness is more importance compared to the minimization of the total number of pallets. In the manner described above (see
Referring to
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
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.
If splitting of the aisle between pallet loads is not allowed, the total number of pallets may increase. For example
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 Vp1 where:
The values of Vp0 and Vp1 optimize the combination of pallet volumes (and minimize the number of pallets) and the number of split aisles. The values for Vp0 and Vp1 should be reasonably close to Vp, for example:
and
The values of Vp0 and Vp1 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 Vp1 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 Vp1 as it is more likely that the case units will fit in a given pallet load.
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
The pallet load generator 165, 165′ relates the aisles with each other (
Where one of the cumulative volume Vcomb exceeds the value Vp0 and the cumulative weight Wcomb exceeds the maximum pallet load weight Wmax, the remaining product volume Vrem and remaining product weight Wrem are updated (
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 Np1 (i.e., Np0 = Np1+1, where the number 1 represents the current pallet) (
Where the updated number of pallets Np1 increases (i.e., Np0 < Np1+1), additional aisles in the sequence of aisles are added to the aisle combination (
or
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 (
With the pallet load planned (
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 Vp1 can be adjusted, as noted above, to reflect importance of minimizing the total number of pallets versus the pallet-per-aisle ratio RPA. Higher values (e.g., close to Vp) of both Vp0 and Vp1 may reduce the expected number of pallets, while lower values of both Vp0 and Vp1 may reduce the probability of splitting aisle between pallets (but may increase the expected number of pallets).
Referring now to
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
In 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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. Various alternatives and modifications can be devised by those 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.
This application claims the benefit of and is a non-provisional of United States Provisional Pat. Application No. 63/288,253 filed on Dec. 10, 2021, the disclosure of which is incorporated herein by reference in its entirety.
Number | Date | Country | |
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63288253 | Dec 2021 | US |