CN114186903B - Warehouse selection method, device, computer equipment and storage medium - Google Patents
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Abstract
本申请涉及一种仓库选品方法、装置、计算机设备和存储介质。通过获取仓储品订单数据以及仓储成本数据；根据仓储品订单数据确定第一待选仓储品；根据仓储品订单数据以及仓储成本数据，通过基于优化目标的多目标优化算法，确定第二待选仓储品，优化目标为降低订单发货拆单率和仓储品持货成本，并满足仓库自有的约束限制；根据第一待选仓储品以及第二待选仓储品进行仓库选品。本申请先确定第一待选仓储品，而后基于多目标规划进一步筛选第二待选仓储品，并进行仓库选品。可以在仓库选品时中同时权衡降低订单发货拆单率和仓储品持货成本，并满足仓库自有的约束限制，能够在满足多种约束条件的情况下，灵活地给出仓库选品结果。
The present application relates to a warehouse product selection method, device, computer equipment and storage medium. By obtaining warehouse product order data and warehouse cost data; determining the first warehouse product to be selected according to the warehouse product order data; determining the second warehouse product to be selected according to the warehouse product order data and the warehouse cost data through a multi-objective optimization algorithm based on the optimization target, the optimization target is to reduce the order delivery split rate and the warehouse product holding cost, and meet the warehouse's own constraints; warehouse selection is performed according to the first warehouse product to be selected and the second warehouse product to be selected. The present application first determines the first warehouse product to be selected, and then further screens the second warehouse product to be selected based on multi-objective planning, and performs warehouse selection. It is possible to simultaneously balance the reduction of the order delivery split rate and the warehouse product holding cost, and meet the warehouse's own constraints when selecting warehouse products, and can flexibly give warehouse selection results while meeting multiple constraints.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and apparatus for selecting articles in a warehouse, a computer device, and a storage medium.
Background
With the rapid development of computer technology and electronic commerce, consumer market segments are becoming more and more evident, and the concept of demand customization is becoming increasingly popular. The number of SKUs (Stock Keeping Unit, stock units) managed in the corporate supply chain has increased explosively. Taking the car industry as an example, a supply company may offer hundreds of thousands of SKUs to customers, but it is difficult to place all of the items in an area warehouse due to the large number of long end pin volume items and the volume constraints of the area warehouse/lead warehouse. Which SKUs to place in the warehouse can generally be determined by a warehouse choice optimization method to achieve both customer needs.
Warehouse choice optimization is generally performed by a heuristic method based on optimized bill disassembly rate, and the method can ensure high correlation among the selected SKUs, but can not meet the corresponding cargo holding cost of the SKUs and the demand of instantly meeting the rate.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a warehouse item selection method, apparatus, computer device, and storage medium that can satisfy both the cost of holding a good and the immediate satisfaction rate.
A warehouse item selection method, the method comprising:
acquiring warehouse article order data and warehouse cost data;
Determining a first to-be-selected warehouse item according to the warehouse item order data;
determining a second warehouse to be selected through a multi-objective optimization algorithm based on an optimization objective according to the warehouse order data and the warehouse cost data, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse goods holding cost and meet the constraint limit of the warehouse;
And carrying out warehouse selection according to the first to-be-selected warehouse product and the second to-be-selected warehouse product.
In one embodiment, the determining the first candidate warehouse item according to the warehouse item order data includes:
acquiring storage article classification demand data, and determining a storage article classification method according to the storage article classification demand data;
acquiring classification data corresponding to the warehouse goods classification method in the warehouse goods order data;
and determining a first to-be-selected storage article in various storage articles of the storage article order data according to the classification data.
In one embodiment, the determining, according to the order data of the warehouse goods and the warehouse cost data, the second to-be-selected warehouse goods through a multi-objective optimization algorithm based on the optimization objective includes:
carrying out population initialization on the warehouse goods to be selected according to the optimization target to obtain an initial solution and a target function;
determining the pareto grade of the initial solution according to the objective function result corresponding to the initial solution through rapid non-dominant sorting;
carrying out iterative loop processing on the initial solution through a genetic algorithm, and obtaining a loop processing result after a preset loop time;
And determining an optimal pareto solution set from the cyclic processing result, and obtaining a second to-be-selected warehouse product according to the optimal pareto solution set.
In one embodiment, the determining, by fast non-dominant sorting, the pareto level of the initial solution according to the objective function result corresponding to the initial solution includes:
through the rapid non-dominant ranking, when an individual in the initial solution does not meet a constraint condition, assigning the individual a pareto grade corresponding to a violation value of the constraint condition.
In one embodiment, the performing iterative loop processing on the initial solution by using a genetic algorithm, and after a preset loop, obtaining a loop processing result includes:
acquiring a child solution according to the initial solution;
Combining the initial solution and the child solution to obtain a combined solution, and starting iterative loop processing;
obtaining elite solutions in the combined solutions through rapid non-dominant sorting and congestion degree calculation;
Taking the elite solution as an initial solution, returning to the step of obtaining a child solution according to the initial solution, and accumulating iteration cycle times;
and after the accumulated circulation turns reach the preset turns, taking elite solution after the last circulation treatment as a circulation treatment result.
In one embodiment, the determining the second candidate warehouse product according to the pareto optimal solution set includes:
Acquiring a single resolution target corresponding to each solution in the pareto optimal solution set;
And taking the solution with the minimum splitting rate target as a second to-be-selected warehouse product corresponding to the pareto optimal solution set.
A warehouse option device, the device comprising:
The data acquisition module is used for acquiring warehouse goods order data and warehouse cost data;
the warehouse article searching module is used for determining a first warehouse article to be selected according to the warehouse article order data;
The article selecting and distributing module is used for determining a second article to be selected according to the article order data and the article storage cost data through a multi-objective optimization algorithm based on an optimization objective, wherein the optimization objective is to reduce the order delivery disassembly rate and the article storage cost and meet the self constraint limit of the warehouse;
And the warehouse article selecting module is used for carrying out warehouse article selecting according to the first to-be-selected warehouse article and the second to-be-selected warehouse article.
In one embodiment, the warehouse entry search module is specifically configured to:
acquiring storage article classification demand data, and determining a storage article classification method according to the storage article classification demand data;
acquiring classification data corresponding to the warehouse goods classification method in the warehouse goods order data;
and determining a first to-be-selected storage article in various storage articles of the storage article order data according to the classification data.
A computer device comprising a memory storing a computer program and a processor which when executing the computer program performs the steps of:
acquiring warehouse article order data and warehouse cost data;
Determining a first to-be-selected warehouse item according to the warehouse item order data;
determining a second warehouse to be selected through a multi-objective optimization algorithm based on an optimization objective according to the warehouse order data and the warehouse cost data, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse goods holding cost and meet the constraint limit of the warehouse;
And carrying out warehouse selection according to the first to-be-selected warehouse product and the second to-be-selected warehouse product.
A computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:
acquiring warehouse article order data and warehouse cost data;
Determining a first to-be-selected warehouse item according to the warehouse item order data;
determining a second warehouse to be selected through a multi-objective optimization algorithm based on an optimization objective according to the warehouse order data and the warehouse cost data, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse goods holding cost and meet the constraint limit of the warehouse;
And carrying out warehouse selection according to the first to-be-selected warehouse product and the second to-be-selected warehouse product.
The warehouse article selecting method, the warehouse article selecting device, the computer equipment and the storage medium are used for acquiring warehouse article order data and warehouse cost data, determining a first warehouse article to be selected according to the warehouse article order data, determining a second warehouse article to be selected according to the warehouse article order data and the warehouse cost data through a multi-target optimizing algorithm based on an optimizing target, wherein the optimizing target is used for reducing the order delivery disassembly rate and the warehouse article holding cost and meeting the constraint limit of a warehouse, and carrying out warehouse article selecting according to the first warehouse article to be selected and the second warehouse article to be selected. The method comprises the steps of determining a first warehouse to be selected, then further screening a second warehouse to be selected based on multi-objective planning, and carrying out warehouse selection. The method can simultaneously balance and reduce the order delivery disassembly rate and the storage article holding cost in the process of selecting the storage articles, meet the constraint limit of the storage, and flexibly give the result of selecting the storage articles.
Drawings
FIG. 1 is a diagram of an application environment for a warehouse option method in one embodiment;
FIG. 2 is a flow diagram of a warehouse option method in one embodiment;
FIG. 3 is a schematic flow chart illustrating a sub-process of step 203 in FIG. 2 according to one embodiment;
FIG. 4 is a schematic flow chart illustrating a sub-process of step 205 in FIG. 2 according to one embodiment;
FIG. 5 is a block diagram of a warehouse option device in one embodiment;
Fig. 6 is an internal structural diagram of a computer device in one embodiment.
Detailed Description
The present application will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present application more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
The warehouse selecting method provided by the application can be applied to an application environment shown in figure 1. Wherein the terminal 102 communicates with the warehouse option server 104 via a network. The terminal 102 may send the warehouse item order data and the warehouse cost data corresponding to the warehouse item server 104, so as to perform warehouse item selection through the warehouse item server 104, the warehouse item server 104 obtains the warehouse item order data and the warehouse cost data, determines a first to-be-selected warehouse item according to the warehouse item order data, determines a second to-be-selected warehouse item according to the warehouse item order data and the warehouse cost data through a multi-objective optimization algorithm based on an optimization objective, and performs warehouse item selection according to the first to-be-selected warehouse item and the second to-be-selected warehouse item. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smartphones, tablet computers, and portable wearable devices, and the warehouse option server 104 may be implemented by a separate server or a server cluster composed of a plurality of servers. In another embodiment, the warehouse article selecting method is particularly used in the field of logistics transportation and is used for identifying illegal actions of logistics personnel. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The repository option server 104 may be implemented by a stand-alone server or a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, a warehouse option method is provided, and the method is applied to the warehouse option server 104 in fig. 1 for illustration, and includes the following steps:
step 201, acquiring warehouse order data and warehouse cost data.
Wherein, when a warehouse is newly built or scaled up, the selection of products stored in the warehouse, i.e. warehouse options, needs to be considered. Typically, the general class of goods stored in a warehouse is determined by business, i.e. business planning determines which general class or classes of goods, such as food, clothing or electronic products, etc., can be stored. Whereas the warehouses in the warehouse may be stored in the warehouse in SKU units. The warehouse article order data specifically refers to the data of the existing order corresponding to the warehouse article in the warehouse. The existing orders contain data such as the quantity of the warehouses corresponding to the single warehouses to be supplied by the warehouse for selecting the warehouses, the supply time of the warehouses and the like. The warehouse cost data specifically refers to the stock holding cost of all warehoused articles in unit time, and the stock holding cost is called stock cost, and refers to various expenses generated in the process of ordering, purchasing and storing the stock, cost loss caused by stock shortage and the like. The warehouse item order data and the warehouse cost data corresponding to the warehouse item may specifically refer to warehouse item order data and warehouse cost data that have been determined in a future period of time.
Specifically, when a worker on the terminal 102 side wishes to perform a warehouse selection on a warehouse or a warehouse cluster, the warehouse selection may be implemented by the warehouse selection server 104. Specifically, staff may collect warehouse order data and warehouse cost data corresponding to the warehouses over a period of time in the future, and then implement warehouse options by warehouse option server 104. In another embodiment, the staff on the terminal 102 side may also perform warehouse selections based on historical data, at which time the staff may provide warehouse order data and warehouse cost data over a period of time to the warehouse selection server 104, and control the warehouse selections based on the historical data.
Step 203, determining a first to-be-selected warehouse item according to the warehouse item order data.
The first to-be-selected warehouse product may specifically be a hot-sold product.
Specifically, warehouse option server 104 may determine which SKUs are hot items based on the order data for the warehouse items, thereby determining the first to-be-selected warehouse item that must be selected as a warehouse item based on the hot SKUs, and then proceeding with subsequent warehouse options
Step 205, determining a second warehouse to be selected according to the warehouse order data and the warehouse cost data through a multi-objective optimization algorithm based on an optimization objective, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse holding cost, and meet the constraint limit of the warehouse itself.
Step 207, warehouse selection is performed according to the first warehouse to be selected and the second warehouse to be selected.
The multi-objective optimization algorithm specifically means that a plurality of optimization objectives exist at the same time, and the optimal optimization problem is achieved as much as possible in a given area based on the plurality of optimization objectives. In general, the sub-objectives under the multi-objective optimization are contradictory, and the improvement of one sub-objective may cause the performance of another sub-objective or other sub-objectives to be reduced, so that it is impossible to simultaneously achieve the optimal solution for a plurality of sub-objectives, and only the coordination and compromise between them can be processed. The order splitting means that the same order is supplied by different warehouses, and the reduction of the order splitting rate is one of main requirements of warehouse options. The application reduces the order delivery disassembly rate and the warehouse goods holding cost, meets the constraint limit of the warehouse itself as an optimization target to carry out multi-target optimization, and can determine which warehouse goods besides the first candidate warehouse goods need to be selected into the warehouse, and the selected warehouse goods are the second warehouse goods to be selected. And then, carrying out warehouse selection according to the first warehouse item to be selected and the second warehouse item to be selected, and storing the warehouse items into a warehouse according to result data corresponding to the warehouse items.
Specifically, in one specific embodiment, the method constructs an objective function by reducing the order delivery and disassembly rate and the warehouse goods holding cost, constructs a constraint function by taking constraint limits meeting the self-owned constraint of the warehouse as an optimization target, and solves the multi-target optimization problem of warehouse goods through a genetic algorithm. After the final pareto optimal solution set is obtained, a second to-be-selected warehouse item in the item distribution result is determined from the pareto optimal solution set through preset optimization constraint, then the item can be optimized based on the item distribution result, an optimal warehouse item selection scheme is determined, and the requirements of the disassembly rate and the goods holding cost can be simultaneously ensured.
The warehouse article selecting method comprises the steps of obtaining warehouse article order data and warehouse cost data, determining a first warehouse article to be selected according to the warehouse article order data, determining a second warehouse article to be selected according to the warehouse article order data and the warehouse cost data through a multi-objective optimization algorithm based on an optimization objective, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse article holding cost, meet the constraint limit of a warehouse, and perform warehouse article selecting according to the first warehouse article to be selected and the second warehouse article to be selected. The method comprises the steps of determining a first warehouse to be selected, then further screening a second warehouse to be selected based on multi-objective planning, and carrying out warehouse selection. The method can simultaneously balance and reduce the order delivery disassembly rate and the storage article holding cost in the process of selecting the storage articles, meets the self constraint limit of the storage, and can flexibly give out the storage article selection result under the condition of meeting various constraint conditions.
In one embodiment, as shown in FIG. 3, step 203 comprises:
Step 302, acquiring warehouse goods classification requirement data, and determining a warehouse goods classification method according to the warehouse goods classification requirement data.
Step 304, obtaining classification data corresponding to the warehouse goods classification method in the warehouse goods order data.
Step 306, determining a first to-be-selected warehouse item in various warehouse items of the warehouse item order data according to the classification data.
The warehouse goods classification requirement data specifically refers to data required for determining how to classify warehouse goods to be selected. The warehouse goods classification requirement data is specifically used for determining a warehouse goods classification method. The warehouse article classification method comprises ABC (Activity Based Classification) inventory classification management method. ABC inventory classification management refers to classifying inventory items into three levels of particularly important inventory (class A), generally important inventory (class B) and unimportant inventory (class C) according to the quantity of varieties and occupied funds, and then managing and controlling the inventory items respectively aiming at different levels. The XYZ classification rule classifies with the accuracy of annual sales prediction, namely, the X class finished product prediction accuracy is highest, the inventory can be properly regulated down, the Y class in the middle can store a certain amount of inventory, the Z class is least accurate, and the higher inventory is necessary to be considered. The principle of XYZ classification is to reduce the total inventory by reducing the inventory of finished products that is relatively easy to predict, but not to break. In addition, ABC/XYZ analysis combining the two is included, and the types of the warehouse products are analyzed through a matrix. The warehouse classification requirement data is determined according to the selected classification method, and is generally sales or sales.
Specifically, warehouse pickles server 104 needs to first obtain the classification needs of the user to determine which method to use to determine the types of SKUs corresponding to the warehouse pickles categories. The order data is then searched for the classification data required by these classifications. For example, by using an ABC classification method, determining which warehouse items in the warehouse item order data belong to the A-type SKU, and then using the A-type SKU as a first warehouse item to be selected. In this embodiment, by determining the storage article classification requirement data first and then determining the classification method based on the requirement, and extracting the classification requirement data from the order, classification is performed and the first storage article to be selected is determined, so that the selected storage article can be classified according to the requirement, and the classification accuracy is ensured.
In one embodiment, as shown in FIG. 4, step 205 includes:
and step 401, initializing the population of the warehouse to be selected according to the optimization target, and obtaining an initial solution and an objective function.
Step 403, determining the pareto grade of the initial solution according to the objective function result corresponding to the initial solution through the rapid non-dominant sorting.
And 405, performing iterative loop processing on the initial solution through a genetic algorithm, and acquiring a loop processing result after a preset loop time.
Step 407, determining a pareto optimal solution set from the cyclic processing result, and obtaining a second to-be-selected warehouse product according to the pareto optimal solution set.
The method can solve the multi-objective optimization problem through a rapid non-dominant ordering genetic algorithm. The goal of the fast non-dominant ordering is to divide pareto grades according to the objective function result of the initial solution. And then, obtaining an optimized pareto optimal solution set from a subsequent cyclic processing result through iterative processing of a genetic algorithm, thereby determining a second to-be-selected warehouse.
Specifically, after determining the first candidate warehouse item which must be selected into the warehouse in the warehouse order data, the second candidate warehouse item which needs to be placed into the warehouse needs to be selected from the rest warehouse items, and at this time, the problem of multi-objective optimization can be constructed and solved through a rapid non-dominant sorting genetic algorithm.
Input parameters and decision variables may be constructed based on the warehouse order data and warehouse cost data, S representing the number of alternative SKUs, S representing a particular SKU. H represents the cost of holding all SKUs per unit time, h= (H1, H2,..hs). Order quantity, and the specific order is represented by O. D, the demand average per unit time for all SKUs, d= (D1, D2., ds). cap, the maximum number of categories that can be allocated to the regional bin, i.e., the category constraint of the regional bin. And r, providing an instant demand satisfaction rate provided by the SKU stored in the warehouse, namely, how much total demand can be covered by the SKU of the option result on average. The decision variable is x= (X1, X2,..xs), where xi=1 means the warehouse selection SKUi, otherwise xi=0.
At this time, the objective function constructed to reduce the order delivery split rate and the warehouse goods holding cost is specifically:
1) The objective function of the split ratio is minimized. The goal of minimizing the split rate is translated to maximizing the order quantity of SKUs that co-occur in the same order, which can be understood as the relevance of the choice SKUs. Designing a SKU co-occurrence matrix C, C ij, represents the number of orders containing both SKU i and SKU j, and C ii represents the number of orders containing SKU i. In addition, assume an order SKU matrix R O×S, where R os =1 if and only if s e o, else R os =0. Then SKU co-occurrence matrix c=r T R. Maximizing the co-occurrence frequency of SKUs is equivalent to minimizing the reciprocal of the co-occurrence frequency. From the decision variables, the minimized objective function can be expressed as:
2) Minimizing shipping cost objective function
SKU holding costs the holding costs per unit time of average inventory, according to input parameters and decision variables, the objective function can be expressed as:
While still requiring the constraints inherent in the warehouse to be met. The constraints of the choice typically include the upper limit of the warehouse's volume/class and the immediate demand satisfaction rate of the warehouse's SKU, both constraints can be expressed as follows.
SKU class upper limit constraint
SKU instant satisfaction rate constraint
According to a predetermined population number N required by the genetic algorithm, N initial solutions (X1, X2,.., XN) are created, each of which satisfies a constraint. And simultaneously, respectively calculating two corresponding objective function values for each initial solution. And then calculating the corresponding pareto grade through the rapid non-dominant sorting. And carrying out iterative loop processing on the initial solution through the steps of population merging, congestion degree calculation, elite retention strategy and the like. And after the preset cycle of circulation, a final circulation result is obtained. The last generation of solutions of the loop are a set of possible outcomes for each individual. From which individuals with pareto ranks 1, i.e. the optimal solution set of pareto, may be selected. And selecting a second to-be-selected warehouse article from the rest warehouse articles based on the pareto optimal solution. In this embodiment, the warehouse item selecting problem of the multi-objective planning is solved by the rapid non-dominant sorting genetic algorithm, so that the efficiency and accuracy of the multi-objective planning solving can be ensured, and the second warehouse item to be selected can be more effectively selected.
In one embodiment, step 403 includes assigning, by rapid non-dominant ranking, pareto grades to individuals corresponding to violation values of the constraint when the individual does not satisfy the constraint in the initial solution.
Specifically, the goal of the fast non-dominant ranking is to divide pareto grades according to the objective function result of the initial solution. The pareto optimal solution of the multi-objective problem is defined as, for minimizing the multi-objective optimization problem, if there are two objective components f 1 (X) and f 2 (X), if any decision variables X a and X b, then X a dominates X b：①f1(Xa)≤f1(Xb) and f2(Xa)≤f2(Xb);②f1(Xa)<f1(Xb) or f 2(Xa)<f2(Xb is satisfied.
A decision variable is said to be a non-dominant solution if, for that decision variable, there are no other decision variables that can dominate it. The Pareto grade is defined as that in a group of solutions, the non-dominant solution Pareto grade is defined as 1, the non-dominant solution is deleted from the solution set, the Pareto grade of the rest solutions is defined as 2, and the Pareto grade of all solutions in the solution set can be obtained by analogy. Thus, in the choice question, when an individual X does not meet the constraint due to the existence of the constraint, the pareto level thereof is defined as the violation value of the constraint.
In one embodiment, step 405 includes obtaining a child solution according to the initial solution, merging the initial solution and the child solution to obtain a merged solution, starting iterative loop processing, obtaining elite solutions in the merged solution through rapid non-dominant sorting and congestion degree calculation, returning the elite solutions as the initial solution to the step of obtaining the child solution according to the initial solution, accumulating iterative loop rounds, and taking the elite solutions after the last loop processing as a loop processing result after the accumulated loop rounds hit a preset round.
Specifically, when iterative solution is performed through a genetic algorithm, for an initial solution, a set of child solutions with the number equal to the number of the initial solution population can be obtained from the initial solution through standard selection, crossover, mutation operators and other modes. And then if the iteration algebra of the genetic algorithm reaches the prediction round. When the population is not reached, entering an iterative loop processing process of a genetic algorithm, wherein in the iterative processing process, the population combination is to combine the current parent population and the offspring population into an integral population with the population number of 2*N, and the crowding degree calculation is used for determining an operator with better fitness of which individual is considered to be better when the pareto grades of the two individuals are the same, and the individual fitness with higher crowding degree is considered to be better. After the rapid non-dominant sorting and the crowding degree calculation are carried out, the individuals with high pareto grade (1 is the highest) and large crowding degree can be preferentially selected to be used as elites to enter the next generation, the individuals sorted according to the two indexes are added into the next generation one by one until the population number reaches N, and the solution set at the moment is elite solution. After iteration is performed for a preset round, elite solution after the last cycle of processing can be used as a cycle processing result. I.e. the result of the last cyclic treatment. The round of the cyclic processing can be specifically determined according to the conditions of precision, warehouse capacity, class constraint of the selected products and the like. In the embodiment, the pareto optimal solution in the rapid non-dominant sorting process is obtained through a genetic algorithm, so that the solving precision can be effectively improved, and the accuracy of warehouse selection is ensured.
In one embodiment, step 405 includes taking the solution with the smallest resolution target as the second candidate warehouse product corresponding to the pareto optimal solution set.
Specifically, after the collection of the last generation elite solutions is obtained, each individual in the collection is a viable result. From which individuals with pareto ranks 1, i.e. the optimal solution set of pareto, may be selected. Meanwhile, according to service experience or service requirements, a solution with the smallest resolution target in the solution set can be generally selected as a final option result Xopt. In the embodiment, the solution with the minimum splitting rate target is used as the second to-be-selected warehouse article corresponding to the pareto optimal solution set, so that the service requirement of the actual warehouse article is met, and the fit degree of the warehouse article and the service process is ensured.
It should be understood that, although the steps in the flowcharts of fig. 2-4 are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in fig. 2-4 may include multiple steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor does the order in which the steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the steps or stages in other steps or other steps.
In one embodiment, as shown in fig. 5, there is provided a warehouse option device comprising:
The data acquisition module 502 is configured to acquire warehouse goods order data and warehouse cost data;
a bin search module 504, configured to determine a first bin to be selected according to bin order data;
The item allocation module 506 is configured to determine, according to the item order data and the item cost data, a second item to be selected through a multi-objective optimization algorithm based on an optimization objective, where the optimization objective is to reduce an order delivery resolution and an item holding cost, and satisfy a constraint of a warehouse itself;
and the warehouse item selecting module 508 is used for selecting warehouse items according to the first to-be-selected warehouse item and the second to-be-selected warehouse item.
In one embodiment, the bin searching module 504 is specifically configured to obtain bin classification requirement data, determine a bin classification method according to the bin classification requirement data, obtain classification data corresponding to the bin classification method in the bin order data, and determine a first to-be-selected bin from all types of bins in the bin order data according to the classification data.
In one embodiment, the item allocation module 506 is specifically configured to perform population initialization on items to be selected according to an optimization objective to obtain an initial solution and an objective function, determine a pareto grade of the initial solution according to an objective function result corresponding to the initial solution through rapid non-dominant sorting, perform iterative loop processing on the initial solution through a genetic algorithm, obtain a loop processing result after a preset loop round, determine a pareto optimal solution set from the loop processing result, and obtain a second item to be selected according to the pareto optimal solution set.
In one embodiment, the choice assigning module 506 is specifically configured to assign, by rapid non-dominant ranking, pareto grades corresponding to violation values of the constraint to individuals when the individual in the initial solution does not satisfy the constraint.
In one embodiment, the choice distribution module 506 is specifically configured to obtain a child solution according to the initial solution, combine the initial solution and the child solution to obtain a combined solution, start an iterative loop process, obtain an elite solution in the combined solution through rapid non-dominant sorting and congestion degree calculation, return the elite solution to the step of obtaining the child solution according to the initial solution as the initial solution, accumulate iteration loop rounds, and use the elite solution after the last loop process as a loop process result after the accumulated loop rounds are hit to a preset round.
In one embodiment, the item allocation module 506 is specifically configured to obtain a single resolution target corresponding to each solution in the pareto optimal solution set, and use a solution with the minimum single resolution target as the second to-be-selected warehouse item corresponding to the pareto optimal solution set.
The specific definition of the warehouse option device can be referred to as the definition of the warehouse option method hereinabove, and will not be repeated here. The various modules in the warehouse option device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store warehouse option data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a warehouse option method.
It will be appreciated by those skilled in the art that the structure shown in FIG. 6 is merely a block diagram of some of the structures associated with the present inventive arrangements and is not limiting of the computer device to which the present inventive arrangements may be applied, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In one embodiment, a computer device is provided comprising a memory and a processor, the memory having stored therein a computer program, the processor when executing the computer program performing the steps of:
acquiring warehouse article order data and warehouse cost data;
determining a first to-be-selected warehouse product according to the warehouse product order data;
Determining a second warehouse to be selected through a multi-objective optimization algorithm based on an optimization objective according to the warehouse order data and the warehouse cost data, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse goods holding cost and meet the self constraint limit of the warehouse;
and carrying out warehouse selection according to the first to-be-selected warehouse and the second to-be-selected warehouse.
In one embodiment, the processor when executing the computer program further performs the steps of obtaining bin classification requirement data, determining a bin classification method according to the bin classification requirement data, obtaining classification data corresponding to the bin classification method in the bin order data, and determining a first to-be-selected bin in various bins of the bin order data according to the classification data.
In one embodiment, the processor further performs the steps of initializing a population of the warehouse to be selected according to the optimization target to obtain an initial solution and an objective function, determining the pareto grade of the initial solution according to an objective function result corresponding to the initial solution through rapid non-dominant sorting, performing iterative loop processing on the initial solution through a genetic algorithm, obtaining a loop processing result after a preset loop turn, determining a pareto optimal solution set from the loop processing result, and obtaining a second warehouse to be selected according to the pareto optimal solution set.
In one embodiment, the processor when executing the computer program further implements the step of assigning, by the fast non-dominant ranking, pareto grades to individuals corresponding to violation values of the constraint when the individual in the initial solution does not meet the constraint.
In one embodiment, the processor further performs the steps of obtaining a child solution from the initial solution, merging the initial solution with the child solution to obtain a merged solution, starting the iterative loop process, obtaining elite solutions in the merged solution by fast non-dominant sorting and congestion degree calculation, returning the elite solutions as the initial solution to the step of obtaining the child solution from the initial solution, accumulating iterative loop rounds, and taking the elite solutions after the last loop process as a loop process result after the accumulated loop rounds are hit to a preset round.
In one embodiment, the processor further realizes the steps of acquiring a single resolution target corresponding to each solution in the pareto optimal solution set when executing the computer program, and taking the solution with the minimum single resolution target as a second to-be-selected warehouse product corresponding to the pareto optimal solution set.
In one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring warehouse article order data and warehouse cost data;
determining a first to-be-selected warehouse product according to the warehouse product order data;
Determining a second warehouse to be selected through a multi-objective optimization algorithm based on an optimization objective according to the warehouse order data and the warehouse cost data, wherein the optimization objective is to reduce the order delivery disassembly rate and the warehouse goods holding cost and meet the self constraint limit of the warehouse;
and carrying out warehouse selection according to the first to-be-selected warehouse and the second to-be-selected warehouse.
In one embodiment, the computer program when executed by the processor further performs the steps of obtaining bin classification requirement data, determining a bin classification method according to the bin classification requirement data, obtaining classification data corresponding to the bin classification method in the bin order data, and determining a first to-be-selected bin in each bin of the bin order data according to the classification data.
In one embodiment, the computer program when executed by the processor further comprises the steps of initializing a population of the warehouse to be selected according to an optimization target to obtain an initial solution and an objective function, determining the pareto grade of the initial solution according to an objective function result corresponding to the initial solution through rapid non-dominant sorting, performing iterative loop processing on the initial solution through a genetic algorithm, obtaining a loop processing result after a preset loop, determining a pareto optimal solution set from the loop processing result, and obtaining a second warehouse to be selected according to the pareto optimal solution set.
In one embodiment, the computer program when executed by the processor further implements the step of assigning, by the fast non-dominant ranking, pareto grades to individuals corresponding to violation values of the constraint when the individual in the initial solution does not meet the constraint.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a child solution from the initial solution, merging the initial solution with the child solution to obtain a merged solution, starting the iterative loop process, obtaining elite solutions in the merged solution by fast non-dominant ordering and congestion degree calculation, returning the elite solutions as the initial solution to the step of obtaining the child solution from the initial solution, accumulating iterative loop rounds, and taking the elite solutions after the last loop processing as a loop processing result after the accumulated loop rounds are hit to a preset round.
In one embodiment, the computer program when executed by the processor further implements the steps of obtaining a single resolution target corresponding to each solution in the pareto optimal solution set, and taking the solution with the minimum single resolution target as a second to-be-selected warehouse product corresponding to the pareto optimal solution set.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile memory may include Read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, or the like. Volatile memory can include random access memory (RandomAccessMemory, RAM) or external cache memory. By way of illustration, and not limitation, RAM can take many forms, such as static random access memory (StaticRandomAccessMemory, SRAM) or dynamic random access memory (DynamicRandomAccessMemory, DRAM), among others.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the application, which are described in detail and are not to be construed as limiting the scope of the application. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the application, which are all within the scope of the application. Accordingly, the scope of protection of the present application is to be determined by the appended claims.
Claims (10)
1. A warehouse item selection method, the method comprising:
acquiring warehouse article order data and warehouse cost data;
acquiring storage article classification demand data, and determining a storage article classification method according to the storage article classification demand data;
acquiring classification data corresponding to the warehouse goods classification method in the warehouse goods order data;
Determining a first to-be-selected warehouse article in various warehouse articles of the warehouse article order data according to the classification data;
Constructing input parameters and decision variables based on the warehouse article order data and the warehouse cost data;
According to the input parameters and the decision variables, respectively constructing an objective function for minimizing the order delivery disassembly rate and an objective function for minimizing the storage cost of the storage, and meeting the self constraint limit of the storage;
initializing a population based on the constraint limit of the warehouse, and creating an initial solution of the population quantity required by a genetic algorithm;
determining the pareto grade of the initial solution according to the objective function result corresponding to the initial solution through rapid non-dominant sorting;
carrying out iterative loop processing on the initial solution through a genetic algorithm, and obtaining a loop processing result after a preset loop time;
determining a pareto optimal solution set from the cyclic processing result;
Acquiring a single resolution target corresponding to each solution in the pareto optimal solution set;
taking the solution with the minimum splitting rate target as a second to-be-selected warehouse product corresponding to the pareto optimal solution set;
And carrying out warehouse selection according to the first to-be-selected warehouse product and the second to-be-selected warehouse product.
2. The method of claim 1, wherein the warehouse item classification method comprises ABC inventory classification management and XYZ classification.
3. The method of claim 1, wherein determining the pareto rank of the initial solution from the objective function result corresponding to the initial solution by fast non-dominant ordering comprises:
through the rapid non-dominant ranking, when an individual in the initial solution does not meet a constraint condition, assigning the individual a pareto grade corresponding to a violation value of the constraint condition.
4. The method of claim 1, wherein the performing iterative loop processing on the initial solution by the genetic algorithm, and after a preset loop, obtaining a loop processing result includes:
acquiring a child solution according to the initial solution;
Combining the initial solution and the child solution to obtain a combined solution, and starting iterative loop processing;
obtaining elite solutions in the combined solutions through rapid non-dominant sorting and congestion degree calculation;
Taking the elite solution as an initial solution, returning to the step of obtaining a child solution according to the initial solution, and accumulating iteration cycle times;
and after the accumulated circulation turns reach the preset turns, taking elite solution after the last circulation treatment as a circulation treatment result.
5. A warehouse selection device, the device comprising:
The data acquisition module is used for acquiring warehouse goods order data and warehouse cost data;
The storage article searching module is used for acquiring storage article classification requirement data, determining a storage article classification method according to the storage article classification requirement data, acquiring classification data corresponding to the storage article classification method in the storage article order data, and determining a first storage article to be selected from various storage articles in the storage article order data according to the classification data;
The system comprises a storage product order data, storage cost data, a selection distribution module, an objective function, a population initialization, an initial solution, an iterative loop processing, a cyclic processing result, a single resolution objective, a second storage product to be selected, wherein the storage product allocation module is used for constructing input parameters and decision variables based on the storage product order data and the storage cost data, respectively constructing an objective function for minimizing order delivery and single resolution and an objective function for minimizing storage product holding cost according to the input parameters and the decision variables, meeting the constraint limit of a storage self-body;
And the warehouse article selecting module is used for carrying out warehouse article selecting according to the first to-be-selected warehouse article and the second to-be-selected warehouse article.
6. The apparatus of claim 5, wherein the warehouse classification method comprises ABC inventory classification management and XYZ classification.
7. The apparatus of claim 5, wherein the option distribution module is specifically configured to:
through the rapid non-dominant ranking, when an individual in the initial solution does not meet a constraint condition, assigning the individual a pareto grade corresponding to a violation value of the constraint condition.
8. The apparatus of claim 5, wherein the option distribution module is further configured to:
acquiring a child solution according to the initial solution;
Combining the initial solution and the child solution to obtain a combined solution, and starting iterative loop processing;
obtaining elite solutions in the combined solutions through rapid non-dominant sorting and congestion degree calculation;
Taking the elite solution as an initial solution, returning to the step of obtaining a child solution according to the initial solution, and accumulating iteration cycle times;
and after the accumulated circulation turns reach the preset turns, taking elite solution after the last circulation treatment as a circulation treatment result.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 4 when the computer program is executed.
10. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 4.
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