CN108345952A - Generate set single method, apparatus, electronic equipment and readable storage medium storing program for executing - Google Patents
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Abstract
The present invention provides a kind of method, apparatus, electronic equipment and readable storage medium storing program for executing generating set list, can reduce the travel distance of order sorting.Set is singly the merging of predetermined quantity order, the method includes：(a) pending order collection is obtained；(b) each order for treating order-processing concentration, calculates the travel distance of the order；(c) it selects the maximum order of the travel distance single as set, is concentrated from pending order and delete selected order, and 1 is set as merging to count；(d) it is equal to predetermined quantity when merging to count, then returns to the set list and go to step (a) and start to generate that next set is single, and otherwise repeatedly following step (e) is counted until merging equal to predetermined quantity；(e) it is single and described merge that count is incremented to be singly updated to the newly set that there is maximum walking to save by the walking saving for calculating separately formed new order after the set list concentrates each order to merge with pending order for the set.
Description
Technical Field
The present invention relates to the field of computer and computer software technologies, and in particular, to a method and an apparatus for generating an aggregated sheet, an electronic device, and a readable storage medium.
Background
Order picking operation is one of main operation processes of the logistics center, picking operation cost occupies most of operation cost of the logistics center, and improving picking efficiency is one of effective ways for reducing picking cost of the logistics center. Before customer orders are picked, the orders are reasonably batched, each batch of orders form an aggregate order and are simultaneously finished in one picking process, so that the picking time can be remarkably saved, the goods picking efficiency is improved, and the goods picking cost of a logistics center is reduced.
Order batch is to carry out batch sorting operation by gathering a plurality of orders into one batch, and the purpose is to shorten the distance and time of average walking and carrying when a picker sorts orders. If the same commodity type in each batch of orders is collected and picked, then the commodities are classified to each customer order to form batch picking, so that the average walking and carrying distance during picking is shortened, the time for repeatedly searching for the storage position is shortened, and the picking efficiency can be improved.
Orders need to be combined into a collection list in batches, the order combination problem is an uncertainty problem, and the current problems to be solved mainly include an accurate algorithm and a heuristic algorithm. The precise algorithm mainly solves the batch results by matching a mixed integer programming method with an assumption of fixed batch size, determines similarity according to the repetition degree of items among orders, and aims to maximize the batches of similar orders, but the precise algorithm is not suitable for an actual picking system due to large calculation amount. Currently, heuristic algorithms are used more often. Heuristic algorithms refer to the insertion of orders that have not been assigned to an aggregated sheet into an existing partially formed aggregated sheet according to some criteria.
The currently common algorithm for order picking is: the method comprises the steps of firstly, carrying out physical position classification on a certain number of orders, collecting the orders in a local area together to form an initial central point based on a single picking channel, and then sequentially expanding the orders from the central point channel to channels on two sides of the central point channel until the number of the orders meeting a collection order is screened out. The detailed steps are as follows:
step 1: channel concentration ranking: and calculating the concentration of all channels according to a concentration calculation formula, and sequencing. For example, in an order distribution diagram of each storage location of a certain warehouse shown in fig. 1, there are 4 channels 1-4, and each channel includes 5 storage locations in the same column. Taking the first channel as an example, the first channel includes 5 reservoirs a0, a2, a4, a6, and A8, and the concentration ratio of the first channel is: (6+3+5+2+1)/5.0, and similarly, the concentration ratio of the fourth channel is: (10+ 7)/5.0;
step 2: judging whether the conditions of the collection list are met: checking whether each channel can independently form an aggregate list (the constraint condition can be orders, commodity number, total volume, total weight, combination strategy and the like) or not according to the concentration ratio from high to low, if so, forming the aggregate list, otherwise, executing the following step 7;
and step 3: generating a collection sheet: generating a collection list for the orders meeting the collection list conditions, and updating the orders and the commodity quantity of each channel;
and 4, step 4: updating channel concentration: recalculating the channel concentration ratio for the rest channels;
and 5: judging whether all channels are traversed or not;
step 6: checking whether residual orders exist or not, if not, ending the process, and if so, continuing to screen;
and 7: expanding a channel: according to the channel concentration degree, a channel with the highest concentration degree is used as a starting point, one channel is expanded to each of the two sides, and whether orders of the three channels meet the generation conditions of the collection list or not is calculated;
and 8: and if the generation condition of the collection sheet is met, executing the step 3, otherwise, sequentially expanding two channels to two sides by taking the channel with the highest concentration as a starting point according to the channel concentration, and sequentially analogizing the three channels … … until all orders generate the collection sheet.
The existing method of using a single center point plus the center point to extend to the two side channels results in a relatively large distance for the picker to walk, for example, a collection sheet includes 20 orders:
1. in the case that the generated aggregate sheet is satisfied for a plurality of storage site combinations in one channel, the existing algorithm does not consider order picking of the storage sites but only considers order picking in the channel, so that the situation that each storage site picks a part of orders to generate the aggregate sheet exists. Fig. 2 is another schematic diagram of the distribution of orders in each storage space of a warehouse, where there are 14 orders with goods located in storage space a0, 3 orders with goods located in storage space a2, and 7 orders with goods located in storage space a 4. The existing algorithm will pick 10 orders of a0, 6 orders of a2, and 4 orders of a4 first to form a first aggregate, and then pick 14 orders of a0, 4 orders of a2, and 2 orders of a4 to form a next aggregate; in fact, picking 20 orders of A0 first forms an aggregate order, picking 4 orders of A0, 10 orders of A2, and 6 orders of A4 form the next aggregate order, and the picker moves less distance;
2. for the case that a plurality of storage sites in one channel are required to form an aggregate sheet, the existing algorithm does not consider optimizing the walking distance by combining order picking orders of different storage sites. As shown in fig. 3, another schematic diagram of the order distribution of each storage position in a warehouse is shown, where the number of orders of goods located in each storage position is shown in the figure. The existing algorithm would pick 14 orders for A0 first, 3 orders for A2, 3 orders for A4 forming the first aggregate, and then pick 4 orders for A4 forming the next aggregate. In fact, since the storage sites a0 and a2 in the traverse channel cannot form the collection list, the storage sites a4 need to be traversed to form the collection list, which means that the three storage sites a0, a2 and a4 need to be traversed to form the collection list, and it is better to select the storage site with the largest distance value from the channel entrance in the collection list formed this time. That is, the more preferable method is to pick 10 orders of a0, 3 orders of a2, and 7 orders of a4 first to form the first aggregate, and the next aggregate only needs to traverse 4 orders of a 0;
3. the existing algorithm only considers that one channel with the maximum concentration is selected as an initial channel each time according to the order concentration of the channels, and then the initial channel is expanded to two sides to form an aggregation list. In fact, if a plurality of channels are selected to expand to form collection sheets with different distance values after being sorted according to the concentration ratio, the collection sheet with the minimum distance is selected to sort the collection sheets after distance comparison, and the total walking distance is relatively small. As shown in fig. 1, since the concentration of 1 and 4 channels (same as 17/5.0) is the same, according to the existing algorithm, a total of 17 orders of 1 channel are selected first, then 3 orders of 3 channel B0 storage sites are selected to form an aggregate list, and the remaining 17 orders of 4 channels form an aggregate list. Actually, because the concentration ratios of the channel 1 and the channel 4 are the same, if the channel 1 and the channel 4 are compared to form the collection sheet, the distance for walking is much shorter than the distance for walking when the channel 1 and the channel 3 are combined into the collection sheet and then the channel 4 forms the collection sheet;
4. when picking more commodity orders with longer distance, the existing collection sheet generation algorithm increases the total walking distance because fewer commodity orders on the same route are not picked simultaneously.
In summary, the conventional collection sheet generation method results in a relatively large walking distance of the order picker, which affects the order picking efficiency and increases the cost of the order picking operation.
Disclosure of Invention
In view of this, the present invention provides a method, an apparatus, an electronic device and a readable storage medium for generating an aggregate order, which can reduce the walking distance of order picking performed by a picker, improve the efficiency of order picking, and save logistics expenses.
To achieve the above object, according to one aspect of the present invention, there is provided a method of generating an aggregation sheet.
A method of generating an aggregated listing, the aggregated listing being a compilation of a predetermined number of orders, each order indicating goods to be picked in a given warehouse, different goods being located in different bins of the warehouse, the distance between bins being known and being maintained in a system database of the warehouse, the method comprising: (a) acquiring an order set to be processed; (b) for each order in the set of orders to be processed, calculating the walking distance of the order according to the system database, wherein the walking distance is the sum of the distances between every two storage positions related to the order; (c) selecting the order with the largest walking distance in the to-be-processed order set as a collection order, deleting the selected order from the to-be-processed order set, and setting the merging count to be 1; (d) when the merge count equals the predetermined number, returning to the collection sheet and proceeding to step (a) to begin generating a next collection sheet, otherwise repeating step (e) below until the merge count equals the predetermined number; (e) respectively calculating the walking distance of the order formed by combining the aggregate sheet with each order in the set of to-be-processed orders, determining one order in the set of to-be-processed orders, so that the order and a new aggregate sheet after combining the aggregate sheet have the maximum walking saving compared with the rest orders in the set of to-be-processed orders, updating the aggregate sheet to the new aggregate sheet, deleting the determined order from the set of to-be-processed orders, and adding 1 to the combination count, wherein the walking saving is defined as: for an order M, the walking saving P of a new collection sheet S formed by combining the order M and the collection sheet N is calculated according to the following format: p is (M travel distance + N travel distance-S travel distance)/S travel distance.
Optionally, the warehouse is divided into areas, each area comprising a plurality of bins, wherein the distance between bins is the distance between the centre points of the areas where the bins are located.
Optionally, calculating the walking distance of each order comprises: mapping the goods to be picked indicated in each order to a storage site related to the order; sorting all the related storage sites according to a preset sequence; and sequentially calculating paths between every two sorted storage positions and calculating the sum to obtain the walking distance of each order.
According to another aspect of the present invention, an apparatus for generating an aggregated sheet is provided.
An apparatus for generating an aggregated listing, the aggregated listing being a compilation of a predetermined number of orders, each order indicating goods to be picked in a given warehouse, different goods being located in different bins of the warehouse, the distance between bins being known and being maintained in a system database of the warehouse, the apparatus comprising: the order set acquisition module is used for acquiring an order set to be processed; the walking distance calculation module is used for calculating the walking distance of each order in the to-be-processed order set according to the system database, wherein the walking distance is the sum of the distances between every two storage positions related to the order; the collection sheet generation module is used for selecting the order with the largest walking distance in the to-be-processed order set as a collection sheet, deleting the selected order from the to-be-processed order set, and setting the merging count to be 1; a judging module, configured to return the order sheet and turn to the order set obtaining module to start generating a next order sheet when the merge count is equal to the predetermined number, and otherwise, jump to an order sheet updating module and repeat execution until the merge count is equal to the predetermined number; an order collection updating module, configured to calculate a walking distance of an order formed after the order collection is merged with each order in the to-be-processed order set, respectively, determine one order in the to-be-processed order set, so that the order and a new order collection merged after the order collection is merged with the order collection has a maximum walking saving compared to other orders in the to-be-processed order set, update the order collection to the new order collection, delete the determined order from the to-be-processed order collection, and add 1 to the merging count, where the walking saving is defined as: for an order M, the walking saving P of a new collection sheet S formed by combining the order M and the collection sheet N is calculated according to the following format: p is (M travel distance + N travel distance-S travel distance)/S travel distance.
Optionally, the warehouse is divided into areas, each area comprising a plurality of bins, wherein the distance between bins is the distance between the centre points of the areas where the bins are located.
Optionally, the walking distance calculating module is further configured to: mapping the goods to be picked indicated in each order to a storage site related to the order; sorting all the related storage sites according to a preset sequence; and sequentially calculating paths between every two sorted storage positions and calculating the sum to obtain the walking distance of each order.
According to yet another aspect of the present invention, an electronic device is provided.
An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the processor to cause the processor to perform the method of generating a menu of collections provided by the present invention.
According to still another aspect of the present invention, a readable storage medium is provided.
A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of generating a playslip provided by the present invention.
According to the technical scheme of the invention, starting from the angle of path selection, the order with the largest walking distance is determined as the initial collection sheet, and the order screening condition which is the largest as the collection sheet is saved according to the walking of the order after each order to be distributed and the initial collection sheet are combined, so that the repeated picking of goods stored on the storage positions on the same path is reduced, the total walking distance of all order picking is shortened, the order picking efficiency is improved, and the logistics expenditure is saved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
FIG. 1 is a schematic diagram of an order distribution for storage locations in a warehouse according to the prior art;
FIG. 2 is a schematic diagram of another prior art order distribution for each bin of a warehouse;
FIG. 3 is a schematic diagram of another prior art order distribution for each bin of a warehouse;
FIG. 4 is a schematic diagram of the main steps of a method of generating a collection sheet according to an embodiment of the present invention;
FIG. 5 is a flow chart of an implementation of an embodiment of the present invention;
FIG. 6 is a schematic diagram of the main modules of an apparatus for generating a collection sheet according to an embodiment of the present invention;
fig. 7 is a hardware configuration diagram of an electronic device of a method of generating a collection sheet according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Aiming at the problems in the prior art, the invention provides a path saving algorithm, and the purpose of shortest total travel distance for order picking is achieved by reasonably combining orders based on distance data between storage sites of a warehouse. In the invention, from the aspect of path selection, data modeling is carried out on storage sites in a warehouse and the distance between the storage sites, each order is mapped to the distance matrix and used for calculating the walking distance of each order, then the orders are combined to obtain the walking saving after the orders are combined, and the order combination with the largest walking saving is selected from the walking saving to obtain the collection list.
In an embodiment of the invention, a distance matrix is generated when data modeling the distances between the bin locations of the warehouse. The storage capacity and the calculated amount can be comprehensively considered, and a large number of storage sites are combined in a partitioning mode, so that the dimension of the distance matrix is reduced. For example: according to actual conditions, after the storage sites of each channel in the warehouse are evenly divided by 6, the adjacent storage sites are combined into areas, distance matrixes of every two storage sites are generated through route simulation software (such as Matlab, Ansys, Multisim and the like) according to historical picking data, and the distance values of the two storage sites in the distance matrixes are the practical and feasible shortest walking distance of the central points of the two corresponding areas.
Fig. 4 is a schematic diagram of main steps of a method for generating a collection sheet according to an embodiment of the present invention. In the present invention, an aggregate order is a combination of a predetermined number of orders, each order indicating a good to be picked in the warehouse, the different goods being located in different bins of the warehouse, the distance between the bins being known and stored in a system database of the warehouse. As shown in fig. 4, the method for generating a collection sheet of the present invention mainly includes the following steps S41 to S45.
Step S41: acquiring an order set to be processed;
step S42: the walking distance of each order is calculated. For each order in the set of pending orders mentioned in step S41, a walking distance for the order is calculated from the system database, wherein the walking distance is the sum of the distances between each two of all storage locations involved in the order. A distance matrix between pre-generated storage sites is stored in a system database;
step S43: an initial collection sheet is determined. Selecting the order with the largest walking distance in the order set to be processed as a collection order, deleting the selected order from the order set to be processed, and setting the merging count to be 1;
step S44: and (5) generating and judging the collection sheet. When the merge count equals the predetermined number, return to the menu and go to step S41 to begin generating the next menu, otherwise repeat step S45 until the merge count equals the predetermined number. Each collection ultimately incorporates a predetermined number of orders, where the predetermined number may be set according to the warehouse order quantity, such as 20;
step S45: and updating the collection list. The walking distance of the order formed by combining the aggregate in step S43 with each order in the set of to-be-processed orders is calculated, one order in the set of to-be-processed orders is determined such that the new aggregate after the order is combined with the aggregate has the largest walking savings as compared to the rest of the orders in the set of to-be-processed orders, the aggregate is updated to the new aggregate, the determined order is deleted from the set of to-be-processed orders, and the combination count is increased by 1. Wherein, walking saving is defined as: for an order M, the walking saving P of a new collection sheet S formed by combining the order M and the collection sheet N is calculated according to the following format: p is (M travel distance + N travel distance-S travel distance)/S travel distance.
Since the capacity of a warehouse will generally be large, the warehouse is divided into areas, each area comprising a plurality of bays, wherein the distance between the bays is the distance between the centre points of the areas where the bays are located. The distance in the present invention refers to the shortest walking distance that is practically feasible.
Under normal business background, an order will contain multiple commodities, and the storage locations of each commodity may be different, so that each order itself has a walking distance between the storage locations of all the commodities inside. According to the embodiment of the present invention, the step S42 of calculating the walking distance of each order may be specifically performed according to the following procedure:
step S421: mapping the goods to be picked indicated in each order to the storage sites related to the order;
step S422: all involved bin points are sorted in a predetermined order. For example: the storage sites can be numbered numerically and sorted according to the size of the number, and the like;
step S423: and sequentially calculating paths between every two sorted storage positions and calculating the sum to obtain the walking distance of each order.
Fig. 5 is a flow chart of an implementation of an embodiment of the present invention. As shown in fig. 5, the embodiment of the present invention mainly includes steps S51 to S59 as follows.
Step S51: acquiring an order set to be processed;
step S52: calculating the walking distance of each order in the set of orders to be processed;
step S53: determining an initial collection sheet, wherein the initial collection sheet refers to an order with the largest walking distance in a to-be-processed order set;
step S54: updating the order set to be processed, and deleting the selected initial collection list from the order set to be processed;
step S55: judging whether the initial collection list meets the collection list generation condition, for example: whether the number of orders included in the initial collection sheet satisfies the number of orders required for generating the collection sheet, and the like. When the condition is satisfied, performing step S56, otherwise performing step S58;
step S56: generating a current determined initial collection list into a collection list;
step S57: judging whether an order is left to be processed or not through the order set to be processed, if so, jumping to the step S51, otherwise, finishing the generation flow of the collection sheet;
step S58: and respectively combining the initial collection sheet and each to-be-processed order into a new collection sheet, and calculating the walking saving of each new collection sheet. When calculating the walking saving of a new collection list, firstly merging and sequencing the commodity storage positions of a single order to be processed and the commodity storage positions of the current initial collection list, calculating the walking distance of the new collection list, and then calculating the walking saving of the new collection list;
step S59: and updating the initial collection list into a new collection list with the largest walking saving.
Through the above steps S51 to S59, the orders can be combined to generate the aggregate sheets by calculating the walking savings, and the total walking distance for all the aggregate sheets to be picked can be minimized.
Fig. 6 is a schematic diagram of main blocks of an apparatus for generating an aggregate sheet according to an embodiment of the present invention. The orders are a merger of a predetermined number of orders, each order indicating goods to be picked in a given warehouse, different goods being located in different bins in the warehouse, the distance between bins being known and stored in a system database of the warehouse. As shown in fig. 6, the apparatus 60 for generating an aggregate sheet of the present invention mainly includes an order set acquisition module 61, a walking distance calculation module 62, an aggregate sheet generation module 63, a judgment module 64, and an aggregate sheet update module 65.
The order set obtaining module 61 is used for obtaining an order set to be processed; the walking distance calculating module 62 is configured to calculate, for each order in the set of to-be-processed orders, a walking distance of the order according to the system database, where the walking distance is a sum of distances between every two storage locations related to the order; the order aggregation generation module 63 is configured to select an order with the largest walking distance in the order set to be processed as an order aggregation, delete the selected order from the order set to be processed, and set a merge count to 1; the judging module 64 is configured to return to the aggregate list and transfer to the order set obtaining module 61 to start generating a next aggregate list when the merge count is equal to the predetermined number, otherwise, jump to the aggregate list updating module 65 and repeat execution until the merge count is equal to the predetermined number; the aggregate update module 65 is configured to calculate a walking distance of an order formed after the aggregate is merged with each order in the to-be-processed order set, determine one order in the to-be-processed order set, so that the order has a maximum walking saving compared to the rest orders in the to-be-processed order set, and update the aggregate into the new aggregate, delete the determined order from the to-be-processed order set, and add 1 to the merged count, where the walking saving is defined as: for an order M, the walking saving P of a new collection sheet S formed by combining the order M and the collection sheet N is calculated according to the following format: p is (M travel distance + N travel distance-S travel distance)/S travel distance.
In the invention, the warehouse is divided into areas, each area comprises a plurality of storage positions, and the distance between the storage positions is the distance between the central points of the areas where the storage positions are located.
Wherein, the walking distance calculating module 62 may be further configured to: mapping the goods to be picked indicated in each order to a storage site related to the order; sorting all the related storage sites according to a preset sequence; and sequentially calculating paths between every two sorted storage positions and calculating the sum to obtain the walking distance of each order.
The invention also provides an electronic device and a readable storage medium according to the embodiment of the invention.
The electronic device of the present invention includes: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the processor to cause the processor to perform the method of generating a menu of collections provided by the present invention.
The non-transitory computer-readable storage medium of the present invention stores computer instructions for causing the computer to perform the method of generating a playslip provided by the present invention.
Fig. 7 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention. As shown in fig. 7, the electronic apparatus 70 includes: one or more processors 71 and a memory 72, one processor 71 being exemplified in fig. 7. The memory 72 is a non-transitory computer readable storage medium provided by the present invention.
The electronic device of the method of generating a collection sheet may further include: an input device 73 and an output device 74.
The processor 71, the memory 72, the input device 73 and the output device 74 may be connected by a bus or other means, as exemplified by the bus connection in fig. 7.
The memory 72, as a non-transitory computer-readable storage medium, may be used for storing non-transitory software programs, non-transitory computer-executable programs, and modules, such as program instructions/modules corresponding to the method for generating an aggregate sheet in the embodiment of the present invention (for example, the order set obtaining module 61, the walking distance calculating module 62, the aggregate sheet generating module 63, the judging module 64, and the aggregate sheet updating module 65 shown in fig. 6). The processor 71 executes various functional applications of the server and data processing by executing non-transitory software programs, instructions and modules stored in the memory 72, namely, implements the method of generating the aggregated sheet in the above method embodiment.
The memory 72 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created from use of the device that generates the aggregate sheet, and the like. Further, the memory 72 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 72 optionally includes memory located remotely from the processor 71, and these remote memories may be connected over a network to the device that generates the aggregated sheets. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 73 may receive input numeric or character information and generate key signal inputs related to user settings and function controls of the device that generates the aggregated sheet. The output device 74 may include a display device such as a display screen.
The one or more modules are stored in the memory 72 and, when executed by the one or more processors 71, perform the method of generating an aggregated sheet of any of the method embodiments described above.
The product can execute the method provided by the embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, starting from the angle of path selection, the order with the largest walking distance is determined as the initial collection sheet, the order screening condition which is the largest as the collection sheet is saved according to the walking of the order after each order to be distributed and the initial collection sheet are combined, and the repeated picking of goods stored on the storage positions on the same path is reduced, so that the total walking distance of all order picking is shortened, the order picking efficiency is improved, and the logistics cost is reduced.
According to the embodiment of the invention, the distance between the storage sites of the storehouse is calculated based on big data dimension reduction, so that the error can be effectively reduced, the accuracy is improved, and the calculation efficiency can be improved. The method for generating the collection sheet meets the requirement on the timeliness of generation of the collection sheet, can be automatically realized through iteration, and avoids the problem that manual exhaustion is difficult to expand to a large number of orders, so that the method for generating the collection sheet can be popularized and applied to service scenes of receiving orders in a plurality of warehouses and multiple storehouses.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method of generating an aggregated listing, the aggregated listing being a compilation of a predetermined number of orders, each order indicating goods to be picked in a given warehouse, different goods being located in different bins of the warehouse, the distance between bins being known and being maintained in a system database of the warehouse, the method characterized by comprising:
(a) acquiring an order set to be processed;
(b) for each order in the set of orders to be processed, calculating the walking distance of the order according to the system database, wherein the walking distance is the sum of the distances between every two storage positions related to the order;
(c) selecting the order with the largest walking distance in the to-be-processed order set as a collection order, deleting the selected order from the to-be-processed order set, and setting the merging count to be 1;
(d) when the merge count equals the predetermined number, returning to the collection sheet and proceeding to step (a) to begin generating a next collection sheet, otherwise repeating step (e) below until the merge count equals the predetermined number;
(e) respectively calculating the walking distance of the order formed by combining the aggregate sheet with each order in the set of to-be-processed orders, determining one order in the set of to-be-processed orders, so that the order and a new aggregate sheet after combining the aggregate sheet have the maximum walking saving compared with the rest orders in the set of to-be-processed orders, updating the aggregate sheet to the new aggregate sheet, deleting the determined order from the set of to-be-processed orders, and adding 1 to the combination count, wherein the walking saving is defined as: for an order M, the walking saving P of a new collection sheet S formed by combining the order M and the collection sheet N is calculated according to the following format:
p is (M travel distance + N travel distance-S travel distance)/S travel distance.
2. The method according to claim 1, wherein the warehouse is divided into areas, each area comprising a plurality of bins, wherein the distance between bins is the distance between the centre points of the areas where the bins are located.
3. The method of claim 1, wherein calculating the distance traveled for each order comprises:
mapping the goods to be picked indicated in each order to a storage site related to the order;
sorting all the related storage sites according to a preset sequence;
and sequentially calculating paths between every two sorted storage positions and calculating the sum to obtain the walking distance of each order.
4. An apparatus for generating an aggregate order, the aggregate order being a compilation of a predetermined number of orders, each order indicating goods to be picked in a given warehouse, different goods being located in different bins of the warehouse, the distance between bins being known and stored in a system database of the warehouse, the apparatus comprising:
the order set acquisition module is used for acquiring an order set to be processed;
the walking distance calculation module is used for calculating the walking distance of each order in the to-be-processed order set according to the system database, wherein the walking distance is the sum of the distances between every two storage positions related to the order;
the collection sheet generation module is used for selecting the order with the largest walking distance in the to-be-processed order set as a collection sheet, deleting the selected order from the to-be-processed order set, and setting the merging count to be 1;
a judging module, configured to return the order sheet and turn to the order set obtaining module to start generating a next order sheet when the merge count is equal to the predetermined number, and otherwise, jump to an order sheet updating module and repeat execution until the merge count is equal to the predetermined number;
an order collection updating module, configured to calculate a walking distance of an order formed after the order collection is merged with each order in the to-be-processed order set, respectively, determine one order in the to-be-processed order set, so that the order and a new order collection merged after the order collection is merged with the order collection has a maximum walking saving compared to other orders in the to-be-processed order set, update the order collection to the new order collection, delete the determined order from the to-be-processed order collection, and add 1 to the merging count, where the walking saving is defined as: for an order M, the walking saving P of a new collection sheet S formed by combining the order M and the collection sheet N is calculated according to the following format: p is (M travel distance + N travel distance-S travel distance)/S travel distance.
5. The apparatus according to claim 4, wherein the warehouse is divided into areas, each area comprising a plurality of bins, wherein the distance between bins is the distance between the centre points of the areas where the bins are located.
6. The apparatus of claim 4, wherein the walking distance calculation module is further configured to:
mapping the goods to be picked indicated in each order to a storage site related to the order;
sorting all the related storage sites according to a preset sequence;
and sequentially calculating paths between every two sorted storage positions and calculating the sum to obtain the walking distance of each order.
7. An electronic device, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the one processor to cause the at least one processor to perform the method of any one of claims 1-3.
8. A non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-3.
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