Cross-industry multi-agent manufacturing task planning method for overseas small batch orders

Abstract

The application provides a cross-industry multi-agent manufacturing task planning method, system, storage medium and electronic equipment for overseas small-batch orders, and relates to the technical field of manufacturing task planning. The application comprises the following steps: acquiring task planning resources and overseas small-batch orders; constructing a CIMA-MTP model according to the task planning resources and overseas small-batch orders, with the aim of minimizing the comprehensive cost of all overseas small-batch orders; and solving the CIMA-MTP model by using an epga algorithm to obtain the task planning result of the overseas small-batch orders. The product demand of overseas small-batch orders is small, the number is large, and the transportation situation is complex. In order to enable manufacturing enterprises to reasonably plan a large number of overseas small-batch orders, the CIMA-MTP model is established, and the genetic algorithm after optimization is used for solving, which is beneficial to finding a near-optimal solution.

Description

Technical Field

[0001] This invention relates to the field of manufacturing task planning technology, and specifically to a cross-industry, multi-entity manufacturing task planning method, system, storage medium, and electronic device for small-batch overseas orders. Background Technology

[0002] With the increasing trend of globalization, more and more manufacturing companies are shifting from centralized production networks to decentralized production networks in order to achieve better production quality and customer satisfaction. Under the decentralized production network, how to arrange order planning and task allocation is a common problem that companies need to face.

[0003] Current research mainly focuses on three aspects: multi-factory production and scheduling, production and transportation planning, and batch processing of small-batch orders. However, manufacturing companies' orders are divided into domestic and overseas orders. Domestic order processing methods are relatively mature, while overseas orders, involving sea freight, receive relatively less attention. Small-batch orders are formed due to various reasons, such as small quantities of leftover stock being shipped separately or orders from overseas customers requiring smaller quantities of products.

[0004] Although the quantity of each product in an overseas small-batch order is small, the number of orders is huge, resulting in a massive overall manufacturing demand. Furthermore, overseas small-batch orders involve multiple manufacturing tasks at various stages of the supply chain, such as production tasks, land transportation tasks, LCL (Less than Container Load) tasks, and sea transportation tasks. Therefore, reasonable and efficient cross-industry, multi-entity manufacturing task planning for overseas small-batch orders is crucial for the efficient operation of manufacturing enterprises. Summary of the Invention

[0005] (a) Technical problems to be solved

[0006] To address the shortcomings of existing technologies, this invention provides a cross-industry, multi-entity manufacturing task planning method, system, storage medium, and electronic device for overseas small-batch orders, solving the technical problem of how to rationally and efficiently plan tasks for overseas small-batch orders.

[0007] (II) Technical Solution

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A cross-industry, multi-entity manufacturing task planning method for small-batch overseas orders, characterized by comprising:

[0010] S1. Obtain cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders;

[0011] S2. Based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, construct the CIMA-MTP model with the goal of minimizing the overall cost of all overseas small-batch orders;

[0012] S3. Use the EPGA algorithm to solve the CIMA-MTP model and obtain the task planning results for overseas small-batch orders.

[0013] Preferably, the CIMA-MTP model in S2 includes:

[0014] The objective function is to minimize the overall cost of all small-batch overseas orders.

[0015] minC=C pro +C sto +C tra +C P

[0016]

[0017]

[0018]

[0019]

[0020]

[0021]

[0022]

[0023] Where i represents overseas small-batch orders i, i∈I={1,2,...,n}, and the total number of orders is n; j represents production base j, j∈J={1,2,...,m}, and the total number of production bases is m; t represents shipping port t, r∈R={1,2,...,g}, and the total number of ports is g; k represents sea freight route k, k∈K={1,2,...,l}, and the total number of routes is l;

[0024] C represents the total cost;

[0025] C pro For production costs; x ijk As a decision variable, if small-batch orders i are allocated to production base j and then to shipping route k, then x ijk Select 1 otherwise select 0; q i Let i be the required quantity of products in the small batch order. The unit production cost of the product at production base j;

[0026] Csto For storage costs; c h The cost per unit volume stored per unit time; Let be the product of the storage volume and storage time of all small-batch order products at any production base j;

[0027] C tra For transportation costs; if the port of shipment for sea freight option k is r, then ρ rk Select 1 if the value is 1, otherwise select 0. Land transportation costs from production base J to port of shipment R; For small batch order i, the container volume; Let k be the unit sea freight cost for sea freight option k.

[0028] C P To incur penalties; c p The penalty cost per unit of time; Estimated delivery time for sea freight option k; For customers with small batch orders, delivery time requirements are specified.

[0029] For small batch order i, the outbound time; Let k be the loading time for sea transport plan; The land transportation time from production base J to shipping port T;

[0030] The unit production time of the product at production base j; For small-batch orders, the start time of production at production base j;

[0031] μ represents the container volume per unit of product.

[0032] Preferably, the CIMA-MTP model in S2 further includes:

[0033] Constraints:

[0034] (1) The production base j to which any small batch order i is assigned belongs to the same region as the shipping port r corresponding to the sea freight scheme k to which it is assigned;

[0035]

[0036] If the production base j is close to the shipping port r, then α jr Select 1, otherwise = 0

[0037] (2) Each small batch order can only be assigned to one production base and one shipping solution;

[0038]

[0039] (3) The total production demand of all small-batch orders allocated to the same production base shall not exceed its remaining production capacity;

[0040]

[0041] Where, N j The remaining production capacity of production base j;

[0042] (4) The total volume of all small-batch orders allocated to the same production base shall not exceed the remaining warehouse capacity of that base;

[0043]

[0044] Among them, H j The remaining warehouse capacity of production base j;

[0045] (5) The total container volume of all small-volume orders allocated to the same shipping scheme shall not exceed the remaining available volume of that scheme;

[0046]

[0047] in, Let K be the remaining available volume for sea transport scheme k.

[0048] Preferably, S3 specifically includes:

[0049] S31. According to the CIMA-MTP model, initialize a population of N individuals using the coding rules, where each row of the chromosome represents the number of the production base and shipping scheme to which all small-batch orders are assigned;

[0050] S32. If the maximum number of iterations is reached, stop, decode the optimal individual in the population, and obtain the allocation planning result of the overseas small batch orders; otherwise, continue execution; where the fitness function is the reciprocal of the objective function of the CIMA-MTP model.

[0051] S33. Perform statistical analysis on the current population and record its best individual;

[0052] S34. Independently select N parent individuals from the current population;

[0053] S35. Based on the pre-set crossover probability, independently perform two-point crossover operations on N parent bodies;

[0054] S36. Based on the preset mutation probability, independently perform integer value mutations on N crossover individuals;

[0055] S37. Merge the current generation's parent population and the population obtained from crossover mutation to obtain a population of size 2N.

[0056] S38. Select N individuals from the 2N population to obtain a new generation population, and return to S32.

[0057] Preferably, in step S38, a direct copy selection method based on fitness ranking is used to obtain a new generation of population. Specifically, this means first calculating the fitness of all individuals in the merged population after the parent population and crossover mutation are obtained, sorting them from largest to smallest fitness, and then selecting the top N individuals for population update.

[0058] A cross-industry, multi-entity manufacturing task planning system for small-batch overseas orders includes:

[0059] The acquisition module is used to acquire cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders;

[0060] The module is used to build a CIMA-MTP model based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, with the goal of minimizing the overall cost of all overseas small-batch orders;

[0061] The solution module is used to solve the CIMA-MTP model using the EPGA algorithm to obtain the task planning results for small-batch overseas orders.

[0062] A storage medium storing a computer program for cross-industry multi-entity manufacturing task planning for small-batch overseas orders, wherein the computer program causes a computer to execute the cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described above.

[0063] An electronic device, comprising:

[0064] One or more processors;

[0065] Memory; and

[0066] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described above.

[0067] (III) Beneficial Effects

[0068] This invention provides a cross-industry, multi-entity manufacturing task planning method, system, storage medium, and electronic device for small-batch overseas orders. Compared with existing technologies, it has the following advantages:

[0069] Beneficial effects:

[0070] This invention includes acquiring cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders; constructing a CIMA-MTP model based on these resources and the goal of minimizing the overall cost of all overseas small-batch orders; and solving the CIMA-MTP model using the EPGA algorithm to obtain the task planning results for the overseas small-batch orders. Addressing the characteristics of complex overseas small-batch manufacturing task requirements, cross-industry manufacturing service capabilities, and coupled manufacturing processes, this invention establishes a CIMA-MTP model for overseas small-batch orders and employs an optimized genetic algorithm for solving it, facilitating the search for near-optimal solutions and helping manufacturing enterprises centrally process large numbers of overseas small-batch orders. Attached Figure Description

[0071] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 A block diagram illustrating a cross-industry, multi-entity manufacturing task planning method for small-batch overseas orders, provided as an embodiment of the present invention.

[0073] Figure 2 This is a schematic diagram of a manufacturing task planning scenario for small-batch overseas orders provided by an embodiment of the present invention;

[0074] Figure 3 This invention provides a timeline for the completion of small-batch overseas orders.

[0075] Figure 4 A schematic diagram of EPGA encoding provided for an embodiment of the present invention;

[0076] Figure 5 This is a schematic diagram of a two-point intersection method provided in an embodiment of the present invention;

[0077] Figure 6 This is a schematic diagram of an integer value mutation method provided in an embodiment of the present invention. Detailed Implementation

[0078] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention are described clearly and completely. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0079] This application provides a cross-industry, multi-entity manufacturing task planning method, system, storage medium, and electronic device for overseas small-batch orders, solving the technical problem of how to reasonably and efficiently allocate and plan overseas small-batch orders.

[0080] The technical solution in this application is to solve the above-mentioned technical problems, and the general idea is as follows:

[0081] like Figure 1 As shown, this embodiment of the invention includes acquiring cross-industry multi-agent manufacturing task planning resources and overseas small-batch orders; based on the cross-industry multi-agent manufacturing task planning resources and overseas small-batch orders, constructing a collaborative manufacturing task allocation planning (CIMA-MTP) model with the objective of minimizing the overall cost of all overseas small-batch orders; and using the enhanced elite-preserving genetic algorithm (EPGA) to solve the CIMA-MTP model to obtain the manufacturing task planning results for overseas small-batch orders. Addressing the characteristics of complex overseas small-batch manufacturing task requirements, cross-industry manufacturing service capabilities, and coupled manufacturing processes, a CIMA-MTP model for overseas small-batch orders is established, and an optimized genetic algorithm is used to solve it, which facilitates finding near-optimal solutions and helps manufacturing enterprises centrally process a large number of overseas small-batch orders.

[0082] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0083] Example:

[0084] This invention provides a cross-industry, multi-entity manufacturing task planning method for small-batch overseas orders, including:

[0085] S1. Obtain cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders;

[0086] S2. Based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, construct the CIMA-MTP model with the goal of minimizing the overall cost of all overseas small-batch orders;

[0087] S3. Use the EPGA algorithm to solve the CIMA-MTP model and obtain the manufacturing task planning results for overseas small-batch orders.

[0088] This invention addresses the challenges of complex overseas small-batch manufacturing tasks, cross-industry manufacturing service capabilities, and coupled manufacturing processes. It establishes a CIMA-MTP model for overseas small-batch orders and employs an optimized genetic algorithm to solve the problem, which facilitates finding near-optimal solutions and helps manufacturing enterprises centrally process large numbers of overseas small-batch orders.

[0089] The following will detail each step of the above technical solution:

[0090] First, it is necessary to provide supplementary explanation of the manufacturing task planning problem involved in the embodiments of the present invention, as follows:

[0091] A manufacturing company has n small-batch overseas orders. These orders can be categorized into production tasks, land transportation tasks, LCL (Less than Container Load) tasks, and sea freight tasks at various stages of the supply chain. Production tasks need to be allocated to m production bases. Land transportation tasks are assigned to vehicles based on the base and port of shipment. LCL and sea freight tasks need to be assigned to sea freight plans to ultimately achieve overseas delivery of all orders. The n small-batch overseas orders all have the same destination port, the m production bases are distributed in different regions, and there are a total of K sea freight plans. These K sea freight plans are abstractions of the LCL and booking operations for small-batch orders. Each sea freight plan includes information such as outbound time (also referred to as shipping time), loading time, estimated delivery time, available volume, sea freight cost, and port of shipment. A cross-industry, multi-entity manufacturing task planning scenario for small-batch overseas orders is as follows: Figure 2 As shown.

[0092] For the same small-batch overseas order, it will not be split. All small-batch overseas orders selected from the same production base will begin production at the same time. After production is completed, if the shipping date is approaching, the goods will be transported to the port for container loading; otherwise, they will be stored in the local finished goods warehouse, incurring storage costs. Since the time for warehousing and consolidation is very small compared to the overall order completion time, it can be ignored. Therefore, the production line completion time equals the product warehousing time. The completion timeline for small-batch overseas orders is as follows: Figure 3 As shown.

[0093] In step S1, cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders are obtained.

[0094] The cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders specifically involve the following symbol system:

[0095] Input parameters

[0096]

[0097]

[0098] Decision variables

[0099]

[0100] In step S2, based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, a CIMA-MTP model is constructed with the objective of minimizing the overall cost of all overseas small-batch orders.

[0101] The CIMA-MTP model in S2 includes:

[0102] The objective function is to minimize the overall cost of all small-batch overseas orders.

[0103] minC=C pro +C sto +C tra +C P

[0104]

[0105]

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Where C represents the comprehensive cost; C pro For production costs; C sto For storage costs; C is the product of the storage volume and storage time of all small-batch order products at any production base j; tta For transportation costs; C P To incur penalties and costs.

[0112] The CIMA-MTP model in S2 also includes:

[0113] Constraints:

[0114] (1) The production base j to which any small batch order i is assigned belongs to the same region as the shipping port t corresponding to the sea freight scheme k assigned to it;

[0115]

[0116] (2) Each small batch order can only be assigned to one production base and one shipping solution;

[0117]

[0118] (3) indicates that the total production demand of all small-batch orders allocated to the same production base does not exceed its remaining production capacity;

[0119]

[0120] (4) The total volume of all small-batch orders allocated to the same production base shall not exceed the remaining warehouse capacity of that base;

[0121]

[0122] (5) The total container volume of all small-volume orders allocated to the same shipping scheme shall not exceed the remaining available volume of that scheme;

[0123]

[0124] In step S3, the CIMA-MTP model is solved using the EPGA algorithm to obtain the allocation planning results for small-batch overseas orders; specifically, this includes:

[0125] S31. According to the CIMA-MTP model, initialize a population of N individuals using the coding rules, where each row of the chromosome represents the number of the production base and shipping scheme to which all small-batch orders are assigned.

[0126] According to the genetic algorithm, one chromosome represents one solution. Chromosomes use a multi-level integer encoding method, such as... Figure 4 As shown.

[0127] exist Figure 4 In the encoding shown, each row of the chromosome represents the number of the production base and shipping plan selected for each small batch order. The chromosome length is twice the number of small batch orders. This means that small batch order 1 was assigned to production base 1 and shipping plan 6, small batch order 2 was assigned to production base 3 and shipping plan 5, and so on, with the nth small batch order assigned to production base 2 and shipping plan 11.

[0128] S32. If the maximum number of iterations is reached, stop, decode the optimal individual in the population, and obtain the allocation planning result of the overseas small batch orders; otherwise, continue execution; where the fitness function is the reciprocal of the objective function of the CIMA-MTP model.

[0129] The objective function described above is the sum of costs for all small-batch orders after selecting production bases and shipping options. According to the genetic algorithm, higher individual fitness is better, while lower total cost is better. Therefore, the reciprocal method is used to transform the reciprocal of the objective function of feasible chromosomes into a fitness function value. At this point, the smaller the objective function value, the larger the fitness value, and the better the solution represented.

[0130] S33. Perform statistical analysis on the current population and record its best individual.

[0131] S34. Independently select N mothers from the current population.

[0132] S35. Perform crossover operations independently on N parent cells according to the pre-set crossover probability.

[0133] The two-point crossover method involves randomly selecting two positions as one before and one after the chromosome to determine the gene segment and then exchanging the corresponding gene segments between the two chromosomes.

[0134] Crossover, by altering the gene sequence of chromosomes through the exchange of gene segments between them, can increase population diversity and improve the global search capability of genetic algorithms. Here, a two-point crossover method is used. Two random positions are selected as one before and one after the chromosome, gene segments are determined, and the corresponding gene segments on the two chromosomes are exchanged. For example... Figure 5 As shown, the base allocation for small batch orders 2, 3, and 4, and the sea freight plan for small batch order 1 are randomly selected as the chosen genes. The positions of these two sets of genes are swapped to obtain two offspring chromosomes.

[0135] S36. Based on the preset mutation probability, independently mutate N individuals after crossover.

[0136] Among them, the mutation method using integer value mutation specifically refers to randomly determining a gene for allocating a production base and a gene for allocating a shipping route on the chromosome, and then mutating them within the corresponding range.

[0137] Mutation operations generate new chromosomes by altering genes or gene locations within chromosomes, increasing population diversity and preventing the algorithm from getting trapped in local optima. Here, an integer-value mutation method is used. Two gene segments are randomly selected from the chromosomes: one chosen by a production base, and the other by a shipping route. Mutations are then performed within their respective ranges. For example... Figure 6 As shown, the chromosome performs real-valued mutations on the production base of small batch order 3, that is, it regenerates the production base selection number of small batch order 3.

[0138] S37. Merge the current generation's parent population and the population obtained from crossover mutation to obtain a population of size 2N.

[0139] When selecting the population to be crossovered, a tournament selection method is used. Each time, a certain number of individuals are removed from the population (sampling with replacement), and the best one is selected to enter the offspring population. When selecting the next generation from the 2N population, a direct copying selection method based on fitness sorting is used. First, the fitness of all individuals in the merged population is calculated using the parent population and crossover mutations. These individuals are then sorted in descending order of fitness. The top N individuals are then selected for population updates. This ensures that the best individual is inherited by the next generation and gives the algorithm better global search capabilities, avoiding getting trapped in local optima.

[0140] S38. Select N individuals from the 2N population to obtain a new generation population, and return to S32.

[0141] Among them, the direct copy selection method based on fitness ranking is used to obtain the new generation population. Specifically, the fitness of all individuals in the merged population is calculated by first calculating the fitness of the parent population and crossover mutation, sorting them from largest to smallest fitness, and then selecting the top N individuals for population update.

[0142] To further illustrate that the EPGA algorithm outperforms other algorithms in finding near-optimal solutions to the CIMA-MTP model, the specific details are as follows:

[0143] This paper simulates a typical manufacturing company receiving small-batch orders from overseas, which need to be distributed across five production bases for processing. A shipping plan is assigned to each small-batch order. First, a problem instance SET1 is generated for orthogonal experiments on algorithm parameter combinations. Then, solutions are found for problems of different sizes under the optimal parameter combinations. All experiments are conducted on a laptop equipped with an AMD Ryzen 5 3550H Gfx (2.10GHz) processor, using PyCharm 2021 for both programming and numerical experiments.

[0144] a) Data generation

[0145] To test the performance of the algorithm in solving the manufacturing task planning problem for small-batch overseas orders of different sizes, the basic parameters and parameter ranges of the manufacturing task planning problem for small-batch overseas orders were set to generate experimental data of different sizes, as shown in Table 1.

[0146] Each small-batch order includes the required product quantity and delivery time, and assigns a production base and shipping plan. The production base data includes parameters such as unit production cost, unit production time, production capacity, warehouse capacity, small-batch order production start time, and a port proximity matrix. Each shipping plan includes parameters such as available LCL volume, ocean freight cost, loading time, estimated delivery time, and designated port of shipment. The proximity relationships between production bases and shipping ports are shown in Table 2, where 1 indicates the base and port of shipment are in the same region, and 0 indicates they are far apart.

[0147] Table 1. Basic parameters and range for data generation

[0148]

[0149] Table 2. Proximity Relationship between Production Bases and Shipping Ports

[0150]

[0151]

[0152] b) Case Study

[0153] To verify the effectiveness of the EPGA algorithm in solving the problem and to facilitate viewing the results, a 10*5*20 example was generated and solved, which consists of 10 small-batch overseas orders, allocated to 5 production bases and 20 sea freight options. The solution results are shown in Table 3. The optimal cost is 14238.25 yuan and the solution time is 69.56 seconds.

[0154] Table 3 Solution results of EPGA algorithm

[0155] small batch orders from overseas Production base Shipping solutions 1 2 18 2 2 2 3 4 14 4 4 14 5 4 14 6 2 2 7 4 14 8 4 19 9 4 14 10 4 14

[0156] This invention provides a cross-industry, multi-entity manufacturing task planning system for small-batch overseas orders, comprising:

[0157] The acquisition module is used to acquire cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders;

[0158] The module is used to build a CIMA-MTP model based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, with the goal of minimizing the overall cost of all overseas small-batch orders;

[0159] The solver module is used to solve the CIMA-MTP model using the EPGA algorithm to obtain the manufacturing task planning results for small-batch overseas orders.

[0160] This invention provides a storage medium storing a computer program for manufacturing task planning of small-batch overseas orders based on EPGA, wherein the computer program enables a computer to execute the cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described above.

[0161] This invention provides an electronic device, comprising:

[0162] One or more processors;

[0163] Memory; and

[0164] One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described above.

[0165] It is understood that the cross-industry multi-entity manufacturing task planning system, storage medium, and electronic device for overseas small-batch orders provided in the embodiments of the present invention correspond to the cross-industry multi-entity manufacturing task planning method for overseas small-batch orders provided in the embodiments of the present invention. The explanations, examples, and beneficial effects of the relevant contents can be referred to the corresponding parts of the cross-industry multi-entity manufacturing task planning method for overseas small-batch orders, and will not be repeated here.

[0166] In summary, compared with existing technologies, it has the following beneficial effects:

[0167] This invention includes acquiring cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders; constructing a CIMA-MTP model based on these resources and the goal of minimizing the overall cost of all overseas small-batch orders; and solving the CIMA-MTP model using the EPGA algorithm to obtain the allocation planning results for overseas small-batch orders. Addressing the characteristics of complex overseas small-batch manufacturing task requirements, cross-industry manufacturing service capabilities, and coupled manufacturing processes, this invention establishes a CIMA-MTP model for overseas small-batch orders and employs an optimized genetic algorithm for solving it, facilitating the search for near-optimal solutions and helping manufacturing enterprises centrally process large numbers of overseas small-batch orders.

[0168] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0169] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A cross-industry, multi-entity manufacturing task planning method for small-batch overseas orders, characterized in that, include: S1. Obtain cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders; S2. Based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, construct the CIMA-MTP model for the allocation planning of collaborative manufacturing tasks with the goal of minimizing the overall cost of all overseas small-batch orders. S3. The enhanced elite-preserving genetic algorithm (EPGA) is used to solve the CIMA-MTP model and obtain the task planning results for overseas small-batch orders. Wherein, S3 includes: S31. Initialize according to the CIMA-MTP model using the encoding rules. A population of individuals, where each line of chromosomes represents the number of all small-batch orders assigned to the production site and shipping route; S32. If the maximum number of iterations is reached, stop, decode the optimal individual in the population, and obtain the allocation planning result of the overseas small batch orders; otherwise, continue execution; where the fitness function is the reciprocal of the objective function of the CIMA-MTP model. S33. Perform statistical analysis on the current population and record its best individual; S34. Independently select N parent individuals from the current population; S35. Based on the pre-set crossover probability, independently perform two-point crossover operations on N parent bodies; S36. Based on the preset mutation probability, independently perform integer value mutations on N crossover individuals; S37. Merge the current generation's parent population and the population obtained from crossover mutation to obtain a population of size 2N. S38. Select N individuals from the 2N population to obtain a new generation population, and return to S32; The CIMA-MTP model in S2 includes: The objective function is to minimize the overall cost of all small-batch overseas orders. in, Indicates small batch orders from overseas , The total number of orders is ; Indicates production base , The total number of production bases is ; Indicates the port of shipment , The total number of ports is ; Indicates shipping plan , The total number of schemes is ; For comprehensive costs; For production costs; For decision variables, if small batch orders Assigned to production base And allocated to the shipping plan ,but Select 1 if the value is 1, otherwise select 0. For small batch orders The demand for products in China; For products at the production base The unit production cost; For storage costs; The cost per unit volume stored per unit time; For any production base The product of the storage volume and storage time of all small-batch order products; For transportation costs; if sea freight is an option The port of shipment is ,but Select 1 if the value is 1, otherwise select 0. For production base j To the port of shipment r Land transportation costs; For small batch orders The container volume; For shipping solutions The unit cost of ocean freight; To incur penalties; The penalty cost per unit of time; Shipping solutions k The estimated delivery time; For small batch orders The customer requires a specific delivery time; For small batch orders Outbound time; For shipping solutions The loading time; For production base To the port of shipment Land transport time; For products at the production base The unit production time; For small batch orders at the production base The start time of production; The container volume per unit of product; The CIMA-MTP model in S2 also includes: Constraints: (1) Any small batch order The allocated production base and the shipping plan assigned to it Corresponding port of shipment Belonging to the same region; Among them, if the production base With the port of shipment If it is close, then Select 1 if the value is 1, otherwise select 0. (2) Each small-batch order can only be assigned to one production base and one shipping plan; (3) The total production demand of all small-batch orders allocated to the same production base shall not exceed its remaining production capacity; in, For production base j The remaining production capacity; (4) The total volume of all small-batch orders allocated to the same production base shall not exceed the remaining warehouse capacity of that base; in, For production base The remaining warehouse capacity; (5) The total container volume of all small-batch orders allocated to the same shipping scheme shall not exceed the remaining available volume of that scheme; in, For shipping solutions The remaining available volume.

2. The cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described in claim 1, characterized in that, In S38, a direct copy selection method based on fitness ranking is used to obtain a new generation of population. Specifically, the fitness of all individuals in the merged population is calculated by first calculating the fitness of the parent population and crossover mutation, sorting them from largest to smallest fitness, and then selecting the top N individuals for population update.

3. A cross-industry multi-entity manufacturing task planning system for small-batch overseas orders, applied to the cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described in claim 1, characterized in that... include: The acquisition module is used to acquire cross-industry, multi-entity manufacturing task planning resources and overseas small-batch orders; The module is used to build a CIMA-MTP model based on the cross-industry multi-entity manufacturing task planning resources and overseas small-batch orders, with the goal of minimizing the overall cost of all overseas small-batch orders; The solution module is used to solve the CIMA-MTP model using the EPGA algorithm to obtain the allocation planning results for small-batch overseas orders.

4. A storage medium, characterized in that, It stores a computer program for cross-industry multi-entity manufacturing task planning for small-batch overseas orders, wherein the computer program causes the computer to execute the cross-industry multi-entity manufacturing task planning method for small-batch overseas orders as described in claim 1 or 2.

5. An electronic device, characterized in that, include: One or more processors; Memory; as well as One or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the programs including a method for performing cross-industry multi-entity manufacturing task planning for small-batch overseas orders as described in claim 1 or 2.

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