Warehouse layout optimization method and device, electronic equipment and readable medium

Abstract

The invention provides a warehouse layout optimization method and device, electronic equipment and a readable medium, and the method comprises the steps: taking storage and sorting ratio information and goods shelf configuration information as initial populations, and substituting the initial populations into a warehouse layout optimization model, the storage and sorting ratio information being determined according to a ratio between a storage area and a sorting area of a warehouse; solving an output result of the warehouse layout optimization model based on a differential evolution algorithm, wherein the output result comprises picking efficiency and / or replenishment efficiency; performing population updating iteration on the warehouse layout optimization model based on a population updating strategy until an iteration ending condition is met; and determining an optimal feasible solution of the warehouse layout optimization model according to an updating iteration result, wherein the optimal feasible solution comprises an optimal feasible solution of the storage-sorting ratio information and / or an optimal feasible solution of the shelf configuration information. Through the embodiment of the invention, the layout optimization reliability of the warehouse is improved, the efficiency and reliability of warehousing and ex-warehouse in the warehouse are improved, and the overstock amount of warehouse ex-warehouse is reduced.

Description

Technical Field

[0001] The present disclosure relates to the technical field of warehouse management, and in particular to a warehouse layout optimization method, device, electronic device, and readable medium. Background Art

[0002] At present, large e-commerce warehouses can be divided into storage areas and picking areas. The ratio of the storage area to the picking area determines the warehouse's efficiency per square meter. However, existing technologies lack optimization solutions for warehouse layout. Irrational warehouse zoning affects the warehouse's warehousing and outbound efficiency to a certain extent, and may even lead to a large amount of inventory backlogs and waste of storage space.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute prior art known to ordinary technicians in the field. Summary of the Invention

[0004] The purpose of the present disclosure is to provide a warehouse layout optimization method, device, electronic device and readable medium, which are used to overcome, at least to a certain extent, the technical problem of unreasonable warehouse partitioning caused by the limitations and defects of related technologies.

[0005] According to a first aspect of an embodiment of the present disclosure, a warehouse layout optimization method is provided, comprising: substituting storage-to-picking ratio information and shelf configuration information as an initialization population into a warehouse layout optimization model, the storage-to-picking ratio information being determined based on the ratio between the storage area and the picking area of ​​the warehouse; solving the output result of the warehouse layout optimization model based on a differential evolution algorithm, the output result including picking efficiency and / or replenishment efficiency; performing population update iterations on the warehouse layout optimization model based on a population update strategy until an iteration end condition is met; determining an optimal feasible solution of the warehouse layout optimization model based on a result of the update iteration, the optimal feasible solution including an optimal feasible solution for the storage-to-picking ratio information and / or an optimal feasible solution for the shelf configuration information.

[0006] In an exemplary embodiment of the present disclosure, the warehouse layout optimization model includes a warehouse model and an in-warehouse simulation model, and substituting the inventory-to-pick ratio information and shelf configuration information as an initialization population into the warehouse layout optimization model includes:

[0007] Inputting the storage-to-picking ratio information and the shelf configuration information into the library model to obtain shelf attribute information output by the library model, the shelf attribute information including at least one of the number of shelves, the area occupied by shelves, the shelf placement position, and the goods placement position;

[0008] The shelf attribute information is input into the in-warehouse simulation model for calculation, and the output result of the in-warehouse simulation model includes the picking efficiency and / or the replenishment efficiency.

[0009] In an exemplary embodiment of the present disclosure, the output result of solving the warehouse layout optimization model based on the differential evolution algorithm includes:

[0010] After the shelf attribute information is input into the in-warehouse simulation model for calculation, the output result of the in-warehouse simulation model is solved based on the differential evolution algorithm.

[0011] In an exemplary embodiment of the present disclosure, before the storage-to-picking ratio information and the shelf configuration information are substituted into the warehouse layout optimization model as the initialization population, the following steps are further included:

[0012] Determine the sales record of any SKU based on the historical records of the warehouse;

[0013] Clustering the SKUs according to the sales records;

[0014] Determining a SKU subcategory of the SKU subcategory according to a result of the clustering process;

[0015] The stock-to-pick ratio data and shelf configuration data of the SKU subclass are aggregated and respectively determined as the stock-to-pick ratio information and the shelf configuration information.

[0016] In an exemplary embodiment of the present disclosure, inputting the shelf attribute information into the in-warehouse simulation model for calculation includes:

[0017] Inputting the shelf attribute information into the in-warehouse simulation model;

[0018] Predictive calculations are performed on the simulation model in the library based on the discrete event simulation algorithm.

[0019] In an exemplary embodiment of the present disclosure, performing population update iterations on the warehouse layout optimization model based on a population update strategy until an iteration end condition is satisfied includes:

[0020] Substituting the picking efficiency and / or replenishment efficiency output by the warehouse layout optimization model into the objective function to calculate the fitness value of each population;

[0021] The fitness value of the population is updated and iterated based on the population update strategy until the iteration end condition is met.

[0022] In an exemplary embodiment of the present disclosure, iteratively updating the fitness value of the population based on the population update strategy until the iteration end condition is satisfied includes:

[0023] Determine the mutation parameters, crossover parameters and selection parameters included in the population update strategy;

[0024] The fitness value of the population is updated iteratively based on at least one parameter among the mutation parameter, the crossover parameter and the selection parameter until the iteration end condition is met.

[0025] According to a second aspect of an embodiment of the present disclosure, there is provided a warehouse layout optimization device, comprising:

[0026] a substitution module configured to substitute the storage-to-picking ratio information and the shelf configuration information as an initialization population into the warehouse layout optimization model, wherein the storage-to-picking ratio information is determined based on the ratio between the storage area and the picking area of ​​the warehouse;

[0027] a solving module configured to solve an output result of the warehouse layout optimization model based on a differential evolution algorithm, wherein the output result includes picking efficiency and / or replenishment efficiency;

[0028] An iteration module is configured to perform population update iteration on the warehouse layout optimization model based on a population update strategy until an iteration end condition is met;

[0029] A determination module is configured to determine the optimal feasible solution of the warehouse layout optimization model based on the result of the update iteration, wherein the optimal feasible solution includes the optimal feasible solution of the storage-picking ratio information and / or the optimal feasible solution of the shelf configuration information.

[0030] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory; and a processor coupled to the memory, wherein the processor is configured to execute any one of the above methods based on instructions stored in the memory.

[0031] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a program is stored, and when the program is executed by a processor, the warehouse layout optimization method as described in any one of the above items is implemented.

[0032] In an embodiment of the present disclosure, the inventory-picking ratio information and the shelf configuration information are substituted into a warehouse layout optimization model as an initialization population, and the output result of the warehouse layout optimization model is solved based on a differential evolution algorithm. The output result includes picking efficiency and / or replenishment efficiency. Then, the warehouse layout optimization model is iterated based on a population update strategy until an iteration end condition is met. Finally, the optimal feasible solution of the warehouse layout optimization model is determined according to the result of the update iteration, that is, the optimal feasible solution of the inventory-picking ratio information and / or the optimal feasible solution of the shelf configuration information are obtained, thereby improving the reliability of warehouse layout optimization, improving the efficiency and reliability of warehousing and outbound in the warehouse, and reducing the backlog of warehouse outbound.

[0033] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification, are used to explain the principles of the present disclosure. Obviously, the drawings described below are only some embodiments of the present disclosure, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0035] Figure 1 A schematic diagram showing an exemplary system architecture to which the warehouse layout optimization solution according to an embodiment of the present invention can be applied;

[0036] Figure 2 is a flow chart of a warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0037] Figure 3 is a flow chart of another warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0038] Figure 4 is a flow chart of another warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0039] Figure 5 is a flow chart of another warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0040] Figure 6 is a flow chart of another warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0041] Figure 7 is a flow chart of another warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0042] Figure 8 is a flow chart of another warehouse layout optimization method in an exemplary embodiment of the present disclosure;

[0043] Figure 9 1 is a schematic diagram of a framework of a warehouse layout optimization solution based on an in-warehouse simulation planning algorithm in an exemplary embodiment of the present disclosure;

[0044] Figure 10 is a schematic diagram of a clustering process of a warehouse layout optimization solution in an exemplary embodiment of the present disclosure;

[0045] Figure 11 is a flow chart of a warehouse layout optimization solution in an exemplary embodiment of the present disclosure;

[0046] Figure 12 is a schematic diagram of a model solving process of a warehouse layout optimization solution in an exemplary embodiment of the present disclosure;

[0047] Figure 13 is a block diagram of a warehouse layout optimization device in an exemplary embodiment of the present disclosure;

[0048] Figure 14 is a block diagram of an electronic device in an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0049] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that the present disclosure will be more comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present disclosure.

[0050] The accompanying drawings are merely schematic illustrations of the present disclosure. Identical reference numerals in the drawings denote identical or similar components, and thus their repeated descriptions will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0051] Figure 1 A schematic diagram showing an exemplary system architecture to which the warehouse layout optimization solution according to an embodiment of the present invention can be applied.

[0052] like Figure 1 As shown, system architecture 100 may include one or more terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing a communication link between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0053] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as needed. For example, the server 105 may be a server cluster consisting of multiple servers.

[0054] Users can use terminal devices 101, 102, 103 to interact with server 105 via network 104 to receive or send messages, etc. Terminal devices 101, 102, 103 can be various electronic devices with display screens, including but not limited to smartphones, tablet computers, laptop computers, desktop computers, etc.

[0055] In some embodiments, the warehouse layout optimization method provided by the embodiments of the present invention is generally executed by server 105. Accordingly, the warehouse layout optimization device is generally installed in terminal device 103 (which may also be terminal device 101 or 102). In other embodiments, certain terminals may have similar functions to the server device to execute the present method.

[0056] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.

[0057] Figure 2 is a flowchart of a warehouse layout optimization method in an exemplary embodiment of the present disclosure.

[0058] refer to Figure 2 , warehouse layout optimization methods may include:

[0059] Step S202: Substitute the storage-to-picking ratio information and shelf configuration information as an initialization population into the warehouse layout optimization model. The storage-to-picking ratio information is determined based on the ratio between the storage area and the picking area of ​​the warehouse.

[0060] Step S204: solving the output result of the warehouse layout optimization model based on the differential evolution algorithm, wherein the output result includes picking efficiency and / or replenishment efficiency.

[0061] Step S206: performing population update iterations on the warehouse layout optimization model based on the population update strategy until an iteration end condition is met.

[0062] Step S208: determining the optimal feasible solution of the warehouse layout optimization model according to the result of the update iteration, wherein the optimal feasible solution includes the optimal feasible solution of the storage-to-picking ratio information and / or the optimal feasible solution of the shelf configuration information.

[0063] In an embodiment of the present disclosure, the inventory-picking ratio information and the shelf configuration information are substituted into a warehouse layout optimization model as an initialization population, and the output result of the warehouse layout optimization model is solved based on a differential evolution algorithm. The output result includes picking efficiency and / or replenishment efficiency. Then, the warehouse layout optimization model is iterated based on a population update strategy until an iteration end condition is met. Finally, the optimal feasible solution of the warehouse layout optimization model is determined according to the result of the update iteration, that is, the optimal feasible solution of the inventory-picking ratio information and / or the optimal feasible solution of the shelf configuration information are obtained, thereby improving the reliability of warehouse layout optimization, improving the efficiency and reliability of warehousing and outbound in the warehouse, and reducing the backlog of warehouse outbound.

[0064] Below, each step of the warehouse layout optimization method is explained in detail.

[0065] In an exemplary embodiment of the present disclosure, Figure 3 As shown, the warehouse layout optimization model includes a warehouse model and an in-warehouse simulation model. Substituting the storage-to-picking ratio information and shelf configuration information as the initialization population into the warehouse layout optimization model includes:

[0066] Step S302: input the storage-to-picking ratio information and the shelf configuration information into the library model to obtain the shelf attribute information output by the library model, wherein the shelf attribute information includes at least one of the number of shelves, the area occupied by the shelves, the shelf placement and the goods placement.

[0067] In an exemplary embodiment of the present disclosure, the shelf configuration information may be a condition that describes or restricts shelf selection.

[0068] Step S304: inputting the shelf attribute information into the in-warehouse simulation model for calculation. The output result of the in-warehouse simulation model includes the picking efficiency and / or the replenishment efficiency.

[0069] In an exemplary embodiment of the present disclosure, Figure 4 As shown, the output results of solving the warehouse layout optimization model based on the differential evolution algorithm include:

[0070] Step S402: After inputting the shelf attribute information into the in-warehouse simulation model for calculation, an output result of the in-warehouse simulation model is solved based on a differential evolution algorithm.

[0071] In an exemplary embodiment of the present disclosure, the Differential Evolution Algorithm (DE) is an efficient global optimization algorithm and a swarm-based heuristic search algorithm. Each individual in the swarm corresponds to a solution vector. The evolutionary process of the differential evolution algorithm is very similar to that of the genetic algorithm, both of which include mutation, hybridization, and selection operations.

[0072] In an exemplary embodiment of the present disclosure, the elements in the shelf attribute information are calculated as individuals in a population, and a new individual is generated by summing the vector difference between any two individuals in the population with a third individual. The new individual is then compared with the corresponding individual in the contemporary population. If the fitness of the new individual is better than the fitness of the current individual, the new individual will replace the old individual in the next generation, otherwise the old individual will be preserved. Through continuous evolution, excellent individuals are retained and inferior individuals are eliminated, guiding the search to approach the optimal solution, that is, obtaining the optimal solution of the shelf attribute information.

[0073] In an exemplary embodiment of the present disclosure, Figure 5 As shown in Figure 1, before the storage-to-picking ratio information and shelf configuration information are substituted into the warehouse layout optimization model as the initial population, the following steps are also included:

[0074] Step S502: determining the sales record of any SKU based on the historical records of the warehouse.

[0075] Step S504: clustering the SKUs according to the sales records.

[0076] Step S506: determining the SKU subcategory of the SKU subcategory according to the result of the clustering process.

[0077] Step S508: Summarize the storage-to-picking ratio data and shelf configuration data of the SKU subclasses and determine them as the storage-to-picking ratio information and the shelf configuration information, respectively.

[0078] In an exemplary embodiment of the present disclosure, by clustering the SKU (Stock Keeping Unit) according to the sales records, and aggregating the stock-picking ratio data and shelf configuration data of the SKU subclasses, and respectively determining them as the stock-picking ratio information and the shelf configuration information, the number of computational tasks of the warehouse layout optimization model can be effectively reduced.

[0079] In an exemplary embodiment of the present disclosure, Figure 6 As shown, inputting the shelf attribute information into the in-warehouse simulation model for calculation includes:

[0080] Step S602: inputting the shelf attribute information into the in-warehouse simulation model.

[0081] Step S604: performing prediction calculation on the simulation model in the library based on the discrete event simulation algorithm.

[0082] In one exemplary embodiment of the present disclosure, a Discrete Event Simulation (DES) algorithm can predict system changes based on the patterns of events occurring at discrete time points in a discrete system. The in-warehouse operational system is a discrete system composed of individual events. The timing of these events can be predetermined, influenced by other events, or occur when certain conditions are met.

[0083] Based on this, compared with traditional prediction models, discrete event simulation algorithms can consider more details and uncertainties and can better establish complex models. Although picking efficiency and replenishment efficiency functions are difficult to establish through traditional methods, they can be obtained through in-warehouse simulation technology established using discrete event simulation methods.

[0084] In an exemplary embodiment of the present disclosure, Figure 7 As shown, the warehouse layout optimization model is updated and iterated based on the population update strategy until the iteration end condition is met, including:

[0085] Step S702: Substitute the picking efficiency and / or replenishment efficiency output by the warehouse layout optimization model into the objective function to calculate the fitness value of each population.

[0086] In an exemplary embodiment of the present disclosure, the optimization goal of the objective function may be to minimize the cost, but is not limited thereto.

[0087] Step S704: iteratively update the fitness value of the population based on the population update strategy until the iteration end condition is met.

[0088] In an exemplary embodiment of the present disclosure, the iteration end condition may be the number of iterations, convergence, iteration time, etc., but is not limited thereto.

[0089] In an exemplary embodiment of the present disclosure, Figure 8 As shown, updating the fitness value of the population based on the population update strategy is iterated until the iteration end condition is met, including:

[0090] Step S802: Determine the mutation parameter, crossover parameter, and selection parameter included in the population update strategy.

[0091] Step S804 , iteratively updating the fitness value of the population based on at least one parameter among the mutation parameter, the crossover parameter, and the selection parameter until the iteration end condition is satisfied.

[0092] In an exemplary embodiment of the present disclosure, a differential evolution algorithm is performed based on the "initialization-mutation-crossover-selection" mechanism, wherein the key parameters are the coefficient of variation F in the "mutation" step and the crossover probability CR in the "crossover" step. The coefficient of variation F can be set to 0.5, and the crossover probability CR is randomly generated from (0, 1), but is not limited thereto.

[0093] like Figure 9 As shown, the in-depot process planning framework based on the in-depot simulation technology mainly includes two parts: a data preprocessing module 902 and an in-depot process planning module 904.

[0094] In an exemplary embodiment of the present disclosure, warehouse operation data includes SKU outbound data, inventory data, inbound data, etc., and the in-warehouse process planning module 904 needs to be developed and operated based on the above data.

[0095] In an exemplary embodiment of the present disclosure, a new warehouse will have a template warehouse, and the historical warehouse operation data of the template warehouse will be obtained as the new warehouse data. When the old warehouse is upgraded, the historical warehouse operation data of the warehouse will be used.

[0096] In an exemplary embodiment of the present disclosure, there are hundreds of SKUs in a warehouse. If each SKU is considered as a unit for placement and shelf selection, the optimization model will have hundreds or even thousands of decision variables, greatly increasing the scale of the optimization model and the difficulty of solving it.

[0097] In an exemplary embodiment of the present disclosure, Figure 10 As shown, in the data prediction processing stage of the clustering processing process 1000 of the warehouse layout optimization solution, SKUs are clustered according to their attributes, and multiple SKUs are grouped into one SKU aggregation class for processing to reduce the model scale.

[0098] In an exemplary embodiment of the present disclosure, for upgrading old warehouses, such as Figure 10 As shown, the warehouse's SKU historical data can be obtained, including, but not limited to, historical SKU sales data, SKU inventory data, and SKU size data. Pre-classification is performed based on sales SKUs. For example, multiple categories BandA, BandB, ... are clustered using K-means++ into (BandA, Class 1), (BandA, Class 2), (BandB, Class 1), (BandB, Class 2), ..., but not limited to this.

[0099] In an exemplary embodiment of the present disclosure, for a newly created warehouse, the SKU data of the template warehouse can be obtained. Each warehouse may have hundreds of SKUs, and some may have thousands of SKUs. If each SKU is treated as an element during optimization, the optimization model will be too complex and the solution will be more difficult.

[0100] In an exemplary embodiment of the present disclosure, SKU data is clustered first, and multiple SKUs can be aggregated into one based on parameters such as sales data and size data, and then subsequent calculations are performed.

[0101] In an exemplary embodiment of the present disclosure, the main workflow in the warehouse is goods entering the warehouse - goods inspection - putting on shelves - picking - review and packaging. The main elements in a warehouse are factory buildings, goods, shelves, review and packaging tables and people. The main area in the factory building is the storage area, which generally occupies 70% of the total area. The area of ​​the storage area limits the maximum storage capacity and production capacity of the warehouse. The storage area is divided into a picking area and a preparation area, and different areas use different shelves. The picking area uses shelves with lower heights to facilitate pickers to pick goods, and the storage area uses beam shelves with higher heights to store more goods. When the shelves are selected unreasonably and the goods are not placed correctly, it will lead to low utilization of the shelves in the warehouse, low picking efficiency, increased shelf costs and personnel costs, and thus reduce the storage efficiency and production efficiency of the warehouse.

[0102] In related technologies, the storage area can store more volume per unit projected area than the picking area. Generally, based on operational experience, the ratio of the picking area to the storage area is set to 3:7, but this storage-to-picking ratio is not necessarily the optimal value.

[0103] Furthermore, storing more goods in the inventory area will improve storage efficiency per square meter, but it will reduce the number of SKUs in the picking area, increase the number of replenishment tasks, and increase the number of replenishment personnel and forklifts (replenishment requires forklifts). Furthermore, storing more goods in the picking area will reduce the number of replenishment tasks, but it will increase the warehouse area and reduce picking efficiency due to reduced storage efficiency per square meter, and increase the number of pickers.

[0104] Based on this, the present invention minimizes the warehouse cost by deciding the storage-picking ratio for each type of SKU. At the same time, the choice of shelves should also be considered during optimization.

[0105] like Figure 11 As shown, the model solving process of a warehouse layout optimization solution in an exemplary embodiment of the present disclosure includes:

[0106] Step S1102, initialize the population and input DE parameters.

[0107] Step S1104: construct a library model.

[0108] Step S1106: The warehouse simulation model calculates the picking efficiency and replenishment efficiency.

[0109] Step S1108, calculating the fitness value of each individual in the population.

[0110] Step S1110, whether the termination condition is met, if so, execute step S1112, if not, execute step S1114.

[0111] Step S1112, optimal result.

[0112] Step S1114: perform population mutation, crossover, and selection, and perform iteration.

[0113] Step S1116 , execute t=t+1, where t represents the number of loop iterations and t is a positive integer greater than or equal to 0.

[0114] Specifically, the model is solved by the differential evolution (DE) method, and the initialized population, that is, the decision variables, are substituted into the warehouse model. The warehouse model is constructed to obtain shelf data, etc. The shelf data and other data are substituted into the warehouse simulation model to obtain picking efficiency and replenishment efficiency data. The data are substituted into the algorithm model to calculate the fitness value of each population. When the termination condition is not met, the population is updated through mutation, crossover and selection methods, and it is continuously iterated until the termination condition is met.

[0115] In an exemplary embodiment of the present disclosure, the core modules are a warehouse model and an in-warehouse simulation model. The function of the warehouse model is to calculate the number and area of ​​shelves in the warehouse, and the placement of goods in the warehouse based on data such as the storage-picking ratio and shelf selection. The in-warehouse simulation model can take into account more details and uncertainties, simulate more in-warehouse indicators, input the parameters of the warehouse model into the in-warehouse simulation model, and calculate the picking efficiency and replenishment efficiency under specified storage-picking ratios and shelves.

[0116] In an exemplary embodiment of the present disclosure, Figure 12 As shown, based on the optimization model solving process 1200, it can be seen that the convergence rate of the optimal objective value based on the optimization model of the present disclosure is much better than the convergence rate of the average objective value.

[0117] In an exemplary embodiment of the present disclosure, the optimization model can quickly converge to the optimal value in the early stage of iteration, and can basically reach the optimal value in the 40th generation, with an average optimization speed of 80 seconds.

[0118] In an exemplary embodiment of the present disclosure, the optimization model of the present disclosure is compared with the model results of actual warehouses and fixed stock-to-pick ratios. Table 1 shows the area comparison, and Table 2 shows the cost comparison. According to Tables 1 and 2, it can be seen that in terms of warehouse area, the planned area determined by the stock-to-pick ratio optimization model of the four warehouses is an average of 12.9% less than the actual leased area, and 8% less than the output area of ​​the toolbox model. In terms of cost, the area of ​​the stock-to-pick ratio optimization model of the four warehouses is an average of 5% less than the daily operating cost of the toolbox model. The stock-to-pick ratio and shelf selection scheme determined based on the embodiment of the present disclosure effectively optimizes the utilization efficiency of the warehouse rental area, thereby reducing the warehouse operating costs, which is conducive to further improving the warehouse's outbound efficiency and inbound efficiency.

[0119] Table 1 Area comparison (“*” represents a number)

[0120]

[0121] Table 2 Cost comparison

[0122]

[0123] Corresponding to the above method embodiments, the present disclosure also provides a warehouse layout optimization device, which can be used to execute the above method embodiments.

[0124] Figure 13 It is a block diagram of a warehouse layout optimization device in an exemplary embodiment of the present disclosure.

[0125] refer to Figure 13 The warehouse layout optimization device 1300 may include:

[0126] The substitution module 1302 is configured to substitute the storage-to-picking ratio information and the shelf configuration information as an initialization population into the warehouse layout optimization model, wherein the storage-to-picking ratio information is determined based on the ratio between the storage area and the picking area of ​​the warehouse.

[0127] The solving module 1304 is configured to solve the output result of the warehouse layout optimization model based on a differential evolution algorithm, where the output result includes picking efficiency and / or replenishment efficiency.

[0128] The iteration module 1306 is configured to perform population update iteration on the warehouse layout optimization model based on a population update strategy until an iteration end condition is met.

[0129] The determination module 1308 is configured to determine the optimal feasible solution of the warehouse layout optimization model based on the result of the update iteration, and the optimal feasible solution includes the optimal feasible solution of the storage-picking ratio information and / or the optimal feasible solution of the shelf configuration information.

[0130] In an exemplary embodiment of the present disclosure, the substitution module 1302 is further configured to:

[0131] Inputting the storage-to-picking ratio information and the shelf configuration information into the library model to obtain shelf attribute information output by the library model, the shelf attribute information including at least one of the number of shelves, the area occupied by shelves, the shelf placement position, and the goods placement position;

[0132] The shelf attribute information is input into the in-warehouse simulation model for calculation, and the output result of the in-warehouse simulation model includes the picking efficiency and / or the replenishment efficiency.

[0133] In an exemplary embodiment of the present disclosure, the solution module 1304 is further configured to:

[0134] After the shelf attribute information is input into the in-warehouse simulation model for calculation, the output result of the in-warehouse simulation model is solved based on the differential evolution algorithm.

[0135] In an exemplary embodiment of the present disclosure, the determining module 1308 is further configured to:

[0136] Determine the sales record of any SKU based on the historical records of the warehouse;

[0137] Clustering the SKUs according to the sales records;

[0138] Determining a SKU subcategory of the SKU subcategory according to a result of the clustering process;

[0139] The stock-to-pick ratio data and shelf configuration data of the SKU subclass are aggregated and respectively determined as the stock-to-pick ratio information and the shelf configuration information.

[0140] In an exemplary embodiment of the present disclosure, the substitution module 1302 is further configured to:

[0141] Inputting the shelf attribute information into the in-warehouse simulation model;

[0142] Predictive calculations are performed on the simulation model in the library based on the discrete event simulation algorithm.

[0143] In an exemplary embodiment of the present disclosure, the iteration module 1306 is further configured to:

[0144] Substituting the picking efficiency and / or replenishment efficiency output by the warehouse layout optimization model into the objective function to calculate the fitness value of each population;

[0145] The fitness value of the population is updated and iterated based on the population update strategy until the iteration end condition is met.

[0146] In an exemplary embodiment of the present disclosure, the iteration module 1306 is further configured to:

[0147] Determine the mutation parameters, crossover parameters and selection parameters included in the population update strategy;

[0148] The fitness value of the population is updated iteratively based on at least one parameter among the mutation parameter, the crossover parameter and the selection parameter until the iteration end condition is met.

[0149] Since the functions of the apparatus 1300 have been described in detail in the corresponding method embodiments, they will not be described in detail in this disclosure.

[0150] It should be noted that although several modules or units of the device for action execution are mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be concretized in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0151] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.

[0152] Those skilled in the art will appreciate that various aspects of the present invention may be implemented as systems, methods, or program products. Therefore, various aspects of the present invention may be implemented in the following forms: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits," "modules," or "systems."

[0153] Refer to the following Figure 14 An electronic device 1400 according to this embodiment of the present invention will be described. Figure 14 The electronic device 1400 shown is merely an example and should not limit the functions and scope of use of the embodiments of the present invention.

[0154] like Figure 14 As shown, electronic device 1400 is implemented as a general-purpose computing device. Components of electronic device 1400 may include, but are not limited to, the aforementioned at least one processing unit 1410, the aforementioned at least one storage unit 1420, and a bus 1430 connecting various system components (including storage unit 1420 and processing unit 1410).

[0155] The storage unit stores program code, which can be executed by the processing unit 1410, so that the processing unit 1410 performs the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section above. For example, the processing unit 1410 can perform the method described in the embodiments of the present disclosure.

[0156] The storage unit 1420 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 14201 and / or a cache memory unit 14202 , and may further include a read-only memory unit (ROM) 14203 .

[0157] The storage unit 1420 may also include a program / utility 14204 having a set (at least one) of program modules 14205, such program modules 14205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0158] The bus 1430 may represent one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0159] Electronic device 1400 may also communicate with one or more external devices 1440 (e.g., a keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with electronic device 1400, and / or any device that enables electronic device 1400 to communicate with one or more other computing devices (e.g., a router, modem, etc.). Such communication may occur via input / output (I / O) interface 1450. Furthermore, electronic device 1400 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network such as the Internet) via network adapter 1460. As shown, network adapter 1460 communicates with other modules of electronic device 1400 via bus 1430. It should be understood that, although not shown, other hardware and / or software modules may be used in conjunction with electronic device 1400, including but not limited to microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0160] Through the description of the above embodiments, it is easy for those skilled in the art to understand that the example embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.

[0161] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, on which is stored a program product capable of implementing the aforementioned methods of this specification. In some possible implementations, various aspects of the present invention may also be implemented in the form of a program product comprising program code. When the program product is executed on a terminal device, the program code is configured to cause the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the "Exemplary Methods" section of this specification.

[0162] The program product for implementing the above-described method according to an embodiment of the present invention may be a portable compact disc read-only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited thereto. In this document, a readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0163] The program product may be implemented in any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0164] A computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0165] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0166] The program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, and the like, as well as conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a stand-alone software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0167] Furthermore, the above-described figures are merely illustrative of the processes included in the method according to exemplary embodiments of the present invention and are not intended to be limiting. It is readily understood that the processes illustrated in the above-described figures do not indicate or limit the temporal order of these processes. Furthermore, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0168] Other embodiments of the present disclosure will readily occur to those skilled in the art after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, with the true scope and spirit of the present disclosure being indicated by the claims.

Claims

1. A warehouse layout optimization method, characterized in that: include: Substituting the storage-to-picking ratio information and shelf configuration information as the initial population into the warehouse layout optimization model, wherein the storage-to-picking ratio information is determined based on the ratio between the storage area and the picking area of ​​the warehouse; Solving the output result of the warehouse layout optimization model based on a differential evolution algorithm, wherein the output result includes picking efficiency and / or replenishment efficiency; Performing population update iterations on the warehouse layout optimization model based on a population update strategy until an iteration end condition is met; An optimal feasible solution of the warehouse layout optimization model is determined according to the result of the update iteration, where the optimal feasible solution includes an optimal feasible solution of the storage-to-picking ratio information and / or an optimal feasible solution of the shelf configuration information.

2. The warehouse layout optimization method according to claim 1, characterized in that: The warehouse layout optimization model includes a warehouse model and an in-warehouse simulation model. Substituting the storage-to-picking ratio information and shelf configuration information as an initialization population into the warehouse layout optimization model includes: Inputting the storage-to-picking ratio information and the shelf configuration information into the library model to obtain shelf attribute information output by the library model, the shelf attribute information including at least one of the number of shelves, the area occupied by shelves, the shelf placement position, and the goods placement position; The shelf attribute information is input into the in-warehouse simulation model for calculation, and the output result of the in-warehouse simulation model includes the picking efficiency and / or the replenishment efficiency.

3. The warehouse layout optimization method according to claim 2, characterized in that: The output results of solving the warehouse layout optimization model based on the differential evolution algorithm include: After the shelf attribute information is input into the in-warehouse simulation model for calculation, the output result of the in-warehouse simulation model is solved based on the differential evolution algorithm.

4. The warehouse layout optimization method according to claim 1 or 2, characterized in that: Before the storage-to-pick ratio information and shelf configuration information are substituted into the warehouse layout optimization model as the initial population, the following steps are also included: Determine the sales record of any SKU based on the historical records of the warehouse; Clustering the SKUs according to the sales records; Determining the SKU subcategory of the SKU subcategory according to the result of the clustering process; The stock-to-pick ratio data and shelf configuration data of the SKU subclass are aggregated and respectively determined as the stock-to-pick ratio information and the shelf configuration information.

5. The warehouse layout optimization method according to claim 2, characterized in that: Inputting the shelf attribute information into the in-warehouse simulation model for calculation includes: Inputting the shelf attribute information into the in-warehouse simulation model; Predictive calculations are performed on the simulation model in the library based on the discrete event simulation algorithm.

6. The warehouse layout optimization method according to claim 1 or 2, characterized in that: The warehouse layout optimization model is updated and iterated based on the population update strategy until the iteration end condition is met, including: Substituting the picking efficiency and / or replenishment efficiency output by the warehouse layout optimization model into the objective function to calculate the fitness value of each population; The fitness value of the population is updated and iterated based on the population update strategy until the iteration end condition is met.

7. The warehouse layout optimization method according to claim 6, characterized in that: Iterating the fitness value of the population based on the population update strategy until the iteration end condition is met includes: Determine the mutation parameters, crossover parameters and selection parameters included in the population update strategy; The fitness value of the population is updated iteratively based on at least one parameter among the mutation parameter, the crossover parameter and the selection parameter until the iteration end condition is met.

8. A warehouse layout optimization device, characterized in that: include: a substitution module configured to substitute the storage-to-picking ratio information and the shelf configuration information as an initialization population into the warehouse layout optimization model, wherein the storage-to-picking ratio information is determined based on the ratio between the storage area and the picking area of ​​the warehouse; a solving module configured to solve an output result of the warehouse layout optimization model based on a differential evolution algorithm, wherein the output result includes picking efficiency and / or replenishment efficiency; An iteration module is configured to perform population update iteration on the warehouse layout optimization model based on a population update strategy until an iteration end condition is met; A determination module is configured to determine the optimal feasible solution of the warehouse layout optimization model based on the result of the update iteration, wherein the optimal feasible solution includes the optimal feasible solution of the storage-picking ratio information and / or the optimal feasible solution of the shelf configuration information.

9. An electronic device, characterized in that: include: Memory; as well as A processor coupled to the memory, wherein the processor is configured to execute the warehouse layout optimization method according to any one of claims 1 to 7 based on instructions stored in the memory.

10. A computer-readable storage medium having a program stored thereon, wherein when the program is executed by a processor, the warehouse layout optimization method according to any one of claims 1 to 7 is implemented.

Images