Multi-objective robust optimization method for task planning of multi-agent collaborative platform production

Abstract

The application provides a multi-objective robust optimization method, system, storage medium and electronic equipment for production task planning of a multi-agent collaborative platform, and relates to the technical field of production task planning of a multi-agent collaborative platform. First, according to the production task planning resources of the multi-agent collaborative platform and a single-batch overseas order, a production task planning model with a manufacturing enterprise agent as the core is constructed under the premise of determining the sea transportation time. Then, an infinity norm ball uncertain set is designed for the sea transportation time of a large ship, a robust quadratic multi-objective model for cross-regional multi-agent production task planning is constructed, and the total delivery time deviation and total delivery cost multi-objective of the single-batch overseas order are optimized. The model considers the sea transportation plan prepared in advance by a shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, a two-stage hybrid heuristic algorithm is designed for solving, and a reasonable and feasible production task planning suggestion is given for the single-batch overseas order.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of multi-agent collaborative platform production task planning, and particularly relates to a multi-objective robust optimization method, system, storage medium and electronic device for multi-agent collaborative platform production task planning. BACKGROUND

[0002] With the rapid development of global trade and emerging markets, more and more manufacturing enterprises choose to change from traditional centralized production mode to distributed multi-factory production network. Distributed multi-factory production mode has the characteristics of geographical dispersion, which provides manufacturing enterprises with the potential to reduce costs, improve efficiency, and save energy and reduce emissions.

[0003] Compared with production task planning within a single factory, the cross-regional nature of multi-factory brings not only differences in costs of raw materials, labor, and storage, but also serious challenges to long-distance logistics. When placing an order, overseas customers usually consider their own sales and inventory situations and give an expected order date, expecting the manufacturing enterprise to provide efficient and timely product delivery services. If the delivery time of the order is far ahead of the order time of the overseas customer, it will cause expensive international port storage costs. If the delivery time of the order is far beyond the order time of the overseas customer, it will cause serious delay losses to the customer.

[0004] In the manufacturing task planning problem driven by overseas orders, the sea transportation is often ignored as one of the important influencing factors. First, the impact of sea transportation plan. The expected sea transportation plan and the long transportation lead time greatly limit the processing time of the manufacturer, affecting production allocation, task planning and storage. Second, the uncertainty of sea transportation is a common problem that seriously affects the reliability of global supply chain. When there is a deviation between the actual ship period and the announced ship period, the multi-agent such as the shipper, the manufacturer and their customers will face numerous losses due to delay. SUMMARY

[0005] (I) Technical problems to be solved

[0006] In view of the shortcomings of the prior art, the present application provides a multi-objective robust optimization method, system, storage medium and electronic device for multi-agent collaborative platform production task planning, which solves the technical problem of ignoring the influence of sea transportation time uncertainty on multi-factory production task planning.

[0007] (II) Technical solutions

[0008] To achieve the above purposes, the present application is implemented by the following technical solutions:

[0009] A multi-objective robust optimization method for multi-agent collaborative platform production task planning, comprising:

[0010] S1. Obtain production task planning resources and single-batch overseas orders from a multi-entity collaborative platform;

[0011] S2. Based on the production task planning resources of the multi-entity collaborative platform and the single batch of overseas orders, and under the premise of determining the sea freight time, construct a production task planning model with the manufacturing enterprise as the core.

[0012] S3. Based on the production task planning model, an uncertain sea transport time is represented by the ∞ norm sphere uncertainty set, and a robust squared multi-objective model for production task planning is constructed.

[0013] S4. Transform the robust squared multi-objective model into an equivalent model, solve the equivalent model, and obtain the robust optimization results of cross-regional multi-entity production task planning.

[0014] Preferably, the robust squared multi-objective model in S3 includes:

[0015] (1) The first objective function aims to minimize the L2 norm of the difference between the actual delivery date and the expected delivery date of all overseas orders in a single batch of overseas orders:

[0016]

[0017]

[0018]

[0019] (2) The second objective function aims to minimize the total delivery cost of all overseas orders in a single batch of overseas orders:

[0020]

[0021]

[0022]

[0023]

[0024]

[0025] Where J represents the set of overseas orders, J = {1, 2, ..., n}; I represents the set of factories, I = {1, 2, ..., m}; G represents the set of domestic ports, G = {1, 2, ..., p}; S represents the set of large ships, S = {1, 2, ..., q};

[0026] Assuming the uncertain parameter is the actual sea transport time δ s Inside a sphere Let ρ be the center of the sphere, ρ be the radius of the sphere, and Δ be the radius of the sphere.s is the error value; let is the matrix of actual sea shipping time of each ship, matrix s is the matrix of historical average sea shipping time of each ship, matrix is the matrix of deviation of actual sea shipping time of each ship from its historical average sea shipping time; s

[0027] There are constraints on actual sea shipping time of all large ships:

[0028]

[0029] is an s-dimensional real vector, ‖·‖ ∞ is the infinity norm of matrix; then the infinity norm ball uncertainty set of actual sea shipping time of large ships is

[0030]

[0031] a s denotes the sea shipping departure date of large ship s;

[0032] y ijs is a decision variable, taking 1 if manufacturing task of overseas order j is assigned to factory i and sea shipping task of overseas order j is assigned to large ship s; otherwise, taking 0; variable y js is a decision variable, taking 1 if sea shipping task of overseas order j is assigned to large ship s; otherwise, taking 0; d j denotes the customer due date of overseas order j;

[0033] q j denotes the product quantity of overseas order j; denotes the processing cost, storage cost, domestic transportation cost, and international sea shipping cost of overseas order j per unit product, respectively;

[0034] denotes the processing cost of overseas order j per unit product at factory i;

[0035] x iji′ is a decision variable, taking 1 if manufacturing task of overseas order j and j' is assigned to factory i and overseas order j is produced immediately after overseas order j'; otherwise, taking 0;

[0036] denotes the storage cost of overseas order j per unit product per day; denotes the storage time of overseas order j;

[0037] denotes the domestic transportation cost of factory i to port g per unit product; ​

[0038] If the large ship s departs from the port g, T sg = 1; otherwise T sg = 0;

[0039] represents the transportation cost of the large ship s per unit product;

[0040] The robust least square multi-objective model in S3 further comprises:

[0041] Constraints:

[0042]

[0043]

[0044]

[0045]

[0046]

[0047]

[0048]

[0049]

[0050]

[0051]

[0052]

[0053]

[0054]

[0055]

[0056] wherein s j , s j′ represent the actual delivery date of the overseas order j, j' respectively; M represents a very large constant;

[0057] tr ig represents the land transportation time from the factory i to the port g;

[0058] pro ij represents the processing time of the overseas order j in the factory i.

[0059] Preferably, the equivalent model in S4 comprises:

[0060] Objective One:

[0061]

[0062] Objective Two:

[0063]

[0064] Constraints:

[0065]

[0066]

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073]

[0074]

[0075]

[0076]

[0077]

[0078]

[0079]

[0080]

[0081] where b j represents the difference between the allocated ship-out date for order j and the customer's due date.

[0082] Preferably, the equivalent model is solved using a two-stage hybrid heuristic algorithm, including:

[0083] First Stage:

[0084] Simplify the equivalent model without considering the selection of processing plants and the starting processing date, design a genetic algorithm to search for a plurality of sea shipping task schemes, and encode to obtain a population containing a plurality of large ship number assignment chromosomes;

[0085] The second stage:

[0086] The last generation of the above population is taken as a partial initial population containing a plurality of large ship number assignment chromosomes, and a partial initial population containing a plurality of plant number assignment chromosomes and a partial initial population containing a plurality of starting processing date chromosomes are generated, a genetic algorithm is designed to obtain a Pareto optimal solution, and decoding is taken as a final multi-plant production task planning robust optimization result.

[0087] Preferably, the first stage specifically includes:

[0088] S411, in the equivalent model, separate out the objective and constraints related only to the large ship assignment decision variable yjs, and simplify the following sub-model:

[0089] Objective 1:

[0090]

[0091] Objective 2:

[0092]

[0093] Constraint condition:

[0094]

[0095]

[0096] S412, solve the sub-model obtained in step S411, randomly generate an initial population of the first stage including a plurality of n column chromosomes, calculate the fitness of each individual in the population, and let Gen1=0; The fitness function in this step is:

[0097] Z i = aV 1 +bV 2

[0098]

[0099] Wherein, a, b are constants, which are the weights of objective 1 and 2 respectively; f i s1 Indicates the fitness value of the i-th chromosome corresponding to the first stage; Nind1 represents the population size of the first stage;

[0100] S413, if the preset termination condition is met, arranging the current population in non-ascending order of fitness, as the last generation population Chrom1 is output; otherwise, turning to S414;

[0101] S414, copying Nind1 / 2 elite individuals to form a stud;

[0102] S415, selecting Nind1 / 2 individuals from the parent population excluding elite individuals by roulette;

[0103] S416, merging the stud and the selected population;

[0104] S417, performing two-point crossover operation;

[0105] S418, performing breeder mutation operation to generate a new generation population, and calculating the fitness of each individual in the population; Gen1=Gen1+1, returning to S413.

[0106] Preferably, the second stage specifically includes:

[0107] S421, generating an initial population of the second stage including a plurality of chromosome units each consisting of three n-column chromosomes:

[0108] The first Nind2 individuals of the last generation population Chrom1 of the first stage are used as part of the initial population of the second genetic algorithm for assigning large ship numbers to chromosome 3; wherein Nind2 represents the population size of the second stage, and Nind2<<Nind1;

[0109] A processing plant priority selection mechanism with processing and transportation costs as the judgment index is introduced to generate part of the initial population of chromosome 1 assigning factory numbers; the priority selection mechanism refers to: setting a certain selection probability to generate the initial population of chromosome 1 assigning base number chromosomes, and the principle of setting the selection probability is: the lower the sum of production and domestic land transportation costs, the higher the selection probability;

[0110] Under the ideal assumption that the storage time is 0, the starting processing date is obtained by reverse calculation from the overseas order deadline delivery date;

[0111]

[0112] S422, performing constraint verification on the chromosomes in the initial population generated in S421; if it fails, modifying it according to the preset constraint processing rule;

[0113] S423, calculate the fitness of each individual in the population, Gen2=0; wherein the fitness function of the second stage is composed of non-dominated sorting level and crowding degree, the higher the non-dominated level, the lower the crowding degree, and the higher the fitness of the chromosome individual;

[0114] S424, determine whether the preset termination condition is met, if yes, output the Pareto optimal solution in the current population and decode as the final multi-factory production task planning robust optimization result; otherwise, go to S425;

[0115] S425, retain the parent population and perform single-point crossover operation;

[0116] S426, perform breeder mutation operation;

[0117] S427, perform constraint checking on the chromosomes in the current population; if not, correct it according to the preset constraint processing rule to generate a child population containing Nind2 individuals;

[0118] S428, merge the child and the parent, calculate the fitness of each individual in the merged population, and select the top Nind2 individuals with higher fitness; Gen2=Gen2+1, and go to S424.

[0119] Preferably, for constraint one: the departure time of the large ship must be later than the time when the order arrives at the domestic port; the preset constraint processing rule specifically refers to: reducing the proportion of chromosomes that violate constraint one in the initial population through the initial population generation rule; and modifying the chromosomes that violate constraint one to chromosomes that meet constraint one through direct modification of the chromosomes.

[0120] Preferably, for constraint two: two order tasks assigned to the same processing factory cannot be processed at the same time; the preset constraint processing rule specifically refers to:

[0121] First, determine the order set to be judged, if the set is empty, end; otherwise, go to the second step;

[0122] Second, determine whether the processing time of the last overseas order and the second-to-last overseas order conflicts; if yes, go to the third step; if no, go to the fourth step;

[0123] Third, if there is no conflict, remove the last order from the order set and return to the second step; if there is a conflict, move the processing time of the last overseas order forward to adjust it to be non-conflicting, while keeping the domestic land transportation time unchanged, and the time difference in the middle is arranged to be stored in the warehouse near the processing factory, waiting for domestic transportation, remove the last order from the order set and return to the second step;

[0124] Fourthly, jump to the next processing plant or the next chromosome, repeat the first step.

[0125] A multi-agent collaborative platform production task planning multi-objective robust optimization system comprises:

[0126] An acquisition module is configured to acquire multi-agent collaborative platform production task planning resources and single-batch overseas orders.

[0127] A construction module is configured to construct, under the premise of determining sea transportation time, a production task planning model with a manufacturing enterprise agent as the core according to the multi-agent collaborative platform production task planning resources and the single-batch overseas orders.

[0128] An optimization module is configured to construct a robust quadratic multi-objective model of production task planning by representing uncertain sea transportation time through an infinity norm ball uncertainty set according to the production task planning model.

[0129] A solution module is configured to convert the robust quadratic multi-objective model into an equivalent model, solve the equivalent model, and acquire a robust optimization result of cross-regional multi-agent production task planning.

[0130] A storage medium stores a computer program for multi-agent collaborative platform production task planning multi-objective robust optimization, wherein the computer program causes a computer to execute the multi-agent collaborative platform production task planning multi-objective robust optimization method described above.

[0131] An electronic device comprises:

[0132] One or more processors;

[0133] Memory; and

[0134] 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, and the programs comprise a program for executing the multi-agent collaborative platform production task planning multi-objective robust optimization method described above.

[0135] (III) Beneficial Effects

[0136] The present application provides a multi-agent collaborative platform production task planning multi-objective robust optimization method, system, storage medium and electronic device. Compared with the prior art, the present application has the following beneficial effects:

[0137] The present application is directed to the design of the L∞ norm ball uncertain set of sea shipping time of large ships, and a robust quadratic multi-objective model of cross-regional multi-agent production task planning is constructed to optimize the total delivery time deviation and total delivery cost multi-objective of single batch overseas order. The model considers the shipping plan prepared in advance by the shipping company agent and the long and uncertain sea shipping time. In addition, the model is converted into an equivalent model, and a two-stage hybrid heuristic algorithm is designed to solve it, and a reasonable and feasible production task planning suggestion is given for single batch overseas order. BRIEF DESCRIPTION OF DRAWINGS

[0138] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0139] Figure 1 A block diagram of a multi-objective robust optimization method of multi-agent collaborative platform production task planning is provided for the embodiments of the present application.

[0140] Figure 2 A delivery process case schematic diagram of a single batch overseas order group is provided for the embodiments of the present application.

[0141] Figure 3 An important process variable schematic diagram taking the delivery process of a single overseas order as the main line is provided for the embodiments of the present application.

[0142] Figure 4 A flowchart of a two-stage hybrid heuristic algorithm is provided for the embodiments of the present application.

[0143] Figure 5 A coding schematic diagram of 1 n-column chromosome in the first stage is provided for the embodiments of the present application.

[0144] Figure 6 A coding schematic diagram of a chromosome unit composed of 3 n-column chromosomes in the second stage is provided for the embodiments of the present application.

[0145] Figure 7 A corresponding relationship schematic diagram between each chromosome of a chromosome unit and the decision variables of an equivalent model is provided for the embodiments of the present application.

[0146] Figure 8 A flowchart of the genetic algorithm in the first stage is provided for the embodiments of the present application.

[0147] Figure 9 A flowchart of the genetic algorithm in the second stage is provided for the embodiments of the present application.

[0148] Figure 10 The generation rule schematic diagram of the initial population in the second stage genetic algorithm provided by the embodiment of the present application. DETAILED DESCRIPTION

[0149] In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application are described clearly and completely. Obviously, the described embodiments are some embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the protection scope of the present application.

[0150] The technical problem of ignoring the influence of sea transportation time uncertainty on multi-factory production task planning is solved by providing a multi-objective robust optimization method, system, storage medium and electronic device for production task planning of a multi-agent collaborative platform.

[0151] The technical solution in the embodiments of the present application is as follows to solve the above technical problem:

[0152] As shown in Figure 1 The embodiments of the present application design an infinity norm ball uncertain set for sea transportation time of a large ship, construct a robust quadratic multi-objective model for production task planning of a cross-regional multi-agent, and optimize total delivery time deviation and total delivery cost multi-objective of a single batch of overseas orders. The model considers the sea transportation plan prepared in advance by a shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, and a two-stage hybrid heuristic algorithm is designed to solve it, and a reasonable and feasible production task planning suggestion is given for a single batch of overseas orders.

[0153] In order to better understand the above technical solution, the above technical solution will be described in detail in combination with the drawings in the specification and specific embodiments.

[0154] Embodiment:

[0155] The embodiments of the present application provide a multi-objective robust optimization method for production task planning of a multi-agent collaborative platform, comprising:

[0156] S1, acquiring production task planning resources of a multi-agent collaborative platform and a single batch of overseas orders;

[0157] S2, constructing a production task planning model with a manufacturing enterprise agent as the core on the premise of determining the sea transportation time according to the production task planning resources of the multi-agent collaborative platform and the single batch of overseas orders;

[0158] S3, according to the production task planning model, the uncertain sea shipping time is expressed by an infinity norm ball uncertainty set, and a robust quadratic multi-objective model of the production task planning is constructed;

[0159] S4, the robust quadratic multi-objective model is converted into an equivalent model, the equivalent model is solved, and a robust optimization result of the cross-regional multi-agent production task planning is obtained.

[0160] In the embodiment of the application, the robust quadratic multi-objective model considers the sea shipping plan prepared in advance by the shipping company subject and the long and uncertain sea shipping time; in addition, the model is converted into an equivalent model, and a two-stage hybrid heuristic algorithm is designed to solve it, and a reasonable and feasible production task planning suggestion is given for a single batch of overseas orders.

[0161] The steps of the above technical solutions will be described in detail below:

[0162] First, the multi-factory production task planning problem described in the embodiment of the application needs to be supplemented and explained as follows:

[0163] Taking a single batch of overseas order group as the optimization object, each overseas order needs to pass through four key links of domestic production, domestic warehousing, domestic logistics and international logistics, and finally be delivered to overseas customers. A single batch of overseas order group refers to a unit composed of orders placed by multiple overseas users within the same or similar time period. Each overseas order only contains one type of demand product model and only corresponds to one overseas user, and each customer will propose an expected order delivery time to the manufacturing enterprise. Each overseas order in the domestic production link is referred to as a manufacturing task of the overseas order, and each order manufacturing task can only be assigned to one factory for processing. Each factory has an independent flow shop and has the ability to process any overseas order manufacturing task, and the processing quality is the same, and each overseas order manufacturing task can be assigned to any factory. A factory can only process one overseas order manufacturing task at the same time and is not allowed to be preempted or paused. Each factory is equipped with a warehouse for storing finished products. The sea shipping task of each overseas order is completed by a unique large ship, and each large ship has a specified domestic departure port, shipping time and sea shipping time. The time when the product of the overseas order arrives at the international destination port is regarded as the delivery time of the order. Based on the above premise, an example of the delivery process of a single batch of overseas order group is shown in Figure 2 .

[0164] A single batch of overseas order groups of a manufacturing enterprise contains n overseas orders, represented by the set J = {1, 2, …, n}, which can be allocated to m distributed factories for processing, and the factory set is represented as I = {1, 2, …, m}. The single batch order group needs to complete four core links of manufacturing tasks, storage tasks, land transportation tasks and sea transportation tasks from the placement of the order, and finally reaches the overseas customer to realize the delivery of the order. Taking the delivery process of a single overseas order as the main line, the important process variable relationship is as shown in Figure 3 . Considering the difference in production capacity between different factories, the processing time of the manufacturing task of the overseas order is different in different factories. The processing time of the manufacturing task of the overseas order j is represented by , which is related to the allocated factory. The starting processing time of the manufacturing task of the overseas order j is represented by s j , and the ending processing time is represented by c j . The storage time of the overseas order in the warehouse after completing the manufacturing task in the factory is represented by . Due to the difference in geographical location between multiple factories, the land transportation task time of the overseas order is represented by , which refers to the transportation time between the allocated factory of the overseas order and the domestic departure port of the selected large ship, and is directly related to the allocated factory i and large ship s. The sea transportation resources are limited, and the shipping company makes and publishes the ship schedule in advance, including the departure time and sea transportation time of the large ship. The departure time of the large ship selected by the overseas order j is represented by sh j , and the sea transportation time is represented by . The overseas order corresponds to one overseas customer, and the expected delivery time of each overseas customer is represented by d j .

[0165] In step S1, the multi-agent collaborative platform production task planning resources and the single batch of overseas orders are obtained;

[0166] The multi-agent collaborative platform production task planning resources and the single batch of overseas orders specifically involve the following symbol system:

[0167] Set:

[0168] Collection Description J Overseas order collection, J = {1, 2,..., n} I Factory collection, I = {1, 2,..., m} G Domestic port collection, G = {1, 2,..., p} S Large ship collection, S = {1, 2,..., q}

[0169] Decision variable

[0170]

[0171] Related parameters

[0172]

[0173] In step S2, the multi-agent collaborative platform produces task planning resources and single-batch overseas orders, and constructs a production task planning model with the manufacturing enterprise agent as the core on the premise of determining the sea transportation time.

[0174] In this step, the sea transportation link is mainly considered, and a multi-objective deterministic model of production task planning with the manufacturing enterprise agent as the core is proposed. The single-batch overseas order group is collaboratively planned, and the total on-time delivery of overseas orders and the total delivery cost of overseas orders are optimized.

[0175] The deterministic model includes:

[0176] Objective one is to minimize the total difference between the actual delivery date of all overseas orders in the single-batch overseas order group and the expected delivery date of the overseas customer, that is, to maximize the total on-time delivery of all overseas orders, to minimize the overseas warehouse cost and delay cost of overseas orders to the greatest extent, and to meet the needs of customers:

[0177]

[0178]

[0179]

[0180]

[0181]

[0182]

[0183]

[0184] wherein formula (2) represents that the actual delivery date of each overseas order is the start processing date s j , processing days , order warehouse storage days , land transportation days , and sea transportation days . Formula (3) overseas order processing days are related to the product type of the overseas order j, and are proportional to the processing capacity of the assigned factory i and the product quantity. Formula (4) represents that the overseas order j warehouse storage time is the departure date of the selected large ship minus the end processing date of the overseas order j. The most ideal case is that the overseas order j warehouse storage time is 0, which means that the overseas order j warehouse storage time manufacturing task is completed at the base price, and is transported to the domestic port to catch the large ship that departs on the same day. Formula (6) represents that the overseas order j land transportation days depend on the factory assigned to the overseas order j and the departure port of the selected large ship.

[0185] Objective II, which is to minimize the total delivery cost of all overseas orders in a single batch of overseas orders group, the delivery cost of each overseas order is composed of production cost, warehouse storage cost, domestic land transportation cost and international sea transportation cost.

[0186]

[0187]

[0188]

[0189]

[0190]

[0191] wherein, formula (9) represents the unit product processing cost of overseas order j related to the factory i allocated to the overseas order j and the type of the product itself. Formula (10) represents the unit product unit time warehouse storage cost of overseas order j and the product storage time. Formula (11) represents the unit product domestic land transportation cost of overseas order j related to the factory allocated to the overseas order j and the selected large ship's departure port; formula (12) represents the unit product international sea transportation cost of overseas order j, related to the selected large ship. The transportation cost of the large ship is set to increase with the decrease of the sea transportation time within a fixed distance.

[0192] Constraints:

[0193]

[0194]

[0195]

[0196]

[0197]

[0198]

[0199]

[0200]

[0201]

[0202]

[0203]

[0204]

[0205]

[0206]

[0207] where constraint (13) means that there is no precedence relationship between any two order tasks; constraint (14) means that there is a unique order task before each order task and only one factory is assigned; constraint (15) means that there is a unique order task after each order task and only one factory is assigned; constraint (16) means that there is only one first order task or no order task is assigned to each factory; constraint (17) means that there is only one last order task or no order task is assigned to each factory; constraint (18) means that for any factory, any order task cannot be in front of and behind another order task at the same time; constraint (19) means that for any order task, there is the same number of order tasks in front of and behind it; constraint (20) means the value constraint of the decision variable when no task is assigned to a factory; constraint (21) means that the start processing time of each order task is greater than 0; constraint (22) means that any two order tasks of the same factory cannot be processed at the same time; constraint (23) means that for any task, only one factory can be selected for processing and only one large ship can be selected for overseas transportation; constraint (24) means the one-to-one correspondence between two decision variables; constraint (25) means that the selected shipping time of each order task must be greater than the time when the order task arrives at the domestic port; and constraint (26) means the value range of the decision variable.

[0208] In step S3, according to the production task planning model, an infinity norm ball uncertainty set is used to represent the uncertain sea transportation time, and a robust quadratic multi-objective model of production task planning is constructed.

[0209] Generally, in the actual delivery process of order tasks, the overseas transportation time is affected by many factors such as sea weather, wind direction, and sea waves Compared with the pre-planned shipping schedule, there is often a deviation. The shipping schedule is extremely inaccurate in transportation and difficult to grasp. The change of overseas transportation time may make the original manufacturing task planning scheme deviate from the optimal solution. Therefore, a robust optimization method is used to solve the production task planning problem under the condition of uncertain sea transportation time. Robust optimization considers the best solution in the worst case, which represents a conservative view. The obtained optimization solution is not necessarily optimal, but when the uncertain parameters are disturbed, the solution is still feasible.

[0210] For the uncertain parameter of large ship sea transport time, this embodiment of the invention designs an ∞-norm sphere uncertainty set and establishes a robust squared multi-objective model for joint task planning of production and logistics tasks.

[0211] The ∞-norm sphere uncertainty set: assuming the uncertainty parameter is the actual sea transport time δ s Inside a sphere Let ρ be the center of the sphere, ρ be the radius of the sphere (the maximum standard deviation of the time deviation of all large ships' maritime transport), and Δ be the radius of the sphere. s Let be the error value (the deviation between the ship's length *s* and the historical average sea transport time); Matrix δ = [δ1, δ2, ..., δ s [A matrix representing the actual sea travel time for each ship] This is a matrix representing the historical average sea travel time for each ship, where matrix Δ = [Δ1, Δ2, ..., Δ]. s [ ] is the deviation matrix of the actual sea travel time of each ship from its historical average sea travel time;

[0212] There are constraints on the actual sea travel time for all large ships:

[0213]

[0214] δ is an s-dimensional real vector, ||·|| ∞ It is the ∞ norm of the matrix, i.e., the largest singular value. The value of the parameter ρ is given in advance, reflecting that the standard deviation of the shipping time deviation of all large ships does not exceed ρ. When ρ = 0, this is a deterministic problem, indicating that the shipping time of all large ships takes the standard value; when ρ > 0, it indicates that the shipping time of all large ships deviates from the standard value to some extent. The value of ρ is adjusted according to the decision-maker's risk preference. Therefore, the ∞ norm spherical uncertainty set of the actual shipping time of large ships is expressed as:

[0215]

[0216] The robust squared multi-objective model includes:

[0217] Objective 1 is to minimize the sum of squares of the differences between the actual delivery dates and the expected delivery dates of all overseas orders in a single batch of overseas orders, taking the worst-case scenario of shipping time deviations. In the original deterministic model, considering the original parameter of the large vessel's shipping time... In case of uncertainty, the parameters Adjusted to δ s Furthermore, the uncertain parameter of sea transport time exists only in formula (7) of the objective function 1 of the original deterministic model.

[0218] The degree of on-time delivery of all products of Target 1 is described by the matrix ∞-norm, i.e., the first objective function of minimizing the 2-norm of the difference between the actual delivery date and the expected delivery date of all overseas orders in a single batch of overseas order group:

[0219]

[0220]

[0221]

[0222] Target 2, which is the minimum total delivery cost of all overseas orders in a single batch of overseas order group, the delivery cost of each overseas order is composed of production cost, warehouse storage cost, domestic land transportation cost and international sea transportation cost; i.e., the second objective function of minimizing the total delivery cost of all overseas orders in a single batch of overseas order group:

[0223]

[0224]

[0225]

[0226]

[0227]

[0228] Constraints:

[0229]

[0230]

[0231]

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[0243] In step S4, the robust quadratic multi-objective model is converted into an equivalent model, the equivalent model is solved, and a robust optimization result of the cross-regional multi-agent production task planning is obtained.

[0244] The robust quadratic multi-objective model is converted into an equivalent model:

[0245] Objective one is

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[0247] Objective two is

[0248]

[0249] Constraint condition:

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[0266] Among them, b j This represents the ship's dispatch date assigned to order j minus the customer's delivery date.

[0267] For the objective function (29), where:

[0268]

[0269] The maximum value can be reached.

[0270] Right now:

[0271]

[0272]

[0273] The above analysis proves that the transformed equivalent model is solvable. Accordingly, the embodiments of this invention employ the following... Figure 4 The method shown for solving the equivalent model based on a two-stage hybrid heuristic algorithm includes:

[0274] Phase 1:

[0275] Without considering the choice of processing plant and the start date of processing, the equivalent model is simplified, and a genetic algorithm is designed to search for multiple maritime task schemes, and a population containing multiple chromosomes with assigned ship numbers is obtained. Figure 5 An example is given of one n-column chromosome for the first stage, where the position number of each column represents the overseas order number, and the value of each column represents the number of the overseas order that selected the large ship.

[0276] Phase Two:

[0277] The last generation of the above population is used as a partial initial population containing multiple chromosomes for assigning ship numbers, and a partial initial population containing multiple chromosomes for assigning factory numbers and multiple chromosomes for processing start dates are generated. Figure 6 An example is given for the second stage, consisting of a chromosome unit composed of 3 chromosomes and n columns. In a chromosome unit, the position number of each row represents the overseas order number. The first chromosome represents the processing plant allocation for each overseas order, the second chromosome represents the start date of the order task at the corresponding processing plant, and the third chromosome represents the shipping vessel allocation for each overseas order. The correspondence between each chromosome and the decision variables of the equivalent model is as follows: Figure 7 As shown, a genetic algorithm is designed to obtain the Pareto optimal solution, and the solution is decoded as the final robust optimization result for multi-factory production task planning.

[0278] wherein the objective of the first stage is to solve the assignment of large ship decision variables y ijs related solution, search a group of multi-order shipping task schemes with high on-time delivery rate and good sea shipping economic benefits; for example, Figure 8 as shown, specifically comprising:

[0279] S411, in the equivalent model, the large ship decision variable y ijs of the original model is removed by containing the information of the base, and the decision variable is adjusted to y js , and it is known that The objective and constraint related to the assignment of large ship decision variable y js can be separated, and the following sub-model is simplified:

[0280] Objective 1:

[0281]

[0282] Objective 2:

[0283]

[0284] Constraint condition:

[0285]

[0286]

[0287] S412, solve the sub-model obtained in step S411, randomly generate an initial population including multiple n columns of chromosomes in the first stage, calculate the fitness of each individual in the population, and let Gen1=0; The fitness function in this step is:

[0288] Z i =aV 1 +bV 2 (76)

[0289]

[0290] Wherein a, b are constants, which are the weights of objective 1 and 2 respectively; f i s1 represents the fitness value of the first stage corresponding to the i-th chromosome; Nind1 represents the population size of the first stage;

[0291] S413, if the preset termination condition is met, arrange the current population in non-ascending order according to the fitness, and output the last generation population Chrom1; Otherwise, go to S414;

[0292] S414, copy Nind1 / 2 elite individuals to form a group;

[0293] S415, roulette wheel is used to select Nind1 / 2 individuals from the parent population after removing the elite individual;

[0294] S416, merging the stallion and the selected population;

[0295] S417, performing two-point crossover operation;

[0296] This step uses a two-point crossover strategy, randomly sets two crossover points in the individual chromosome, and then performs partial gene exchange between the two crossover points.

[0297] S418, performing breeder mutation operation to generate a new generation population, and calculating the fitness of each individual in the population; Gen1 = Gen1 + 1, return to S413.

[0298] This step uses breeder GA mutation strategy, uses compression rate (MutShrink) and gradient division (Gradient) to control the mutation distance, the greater the compression rate and the fewer the gradient division, the greater the mutation amplitude and the stronger the global search ability.

[0299] The second stage designs an equivalent model of a multi-objective robust optimization model for solving the multi-plant production task planning under uncertain sea transportation time; as shown in Figure 9 , specifically comprising:

[0300] S421, as Figure 10 shown, generating an initial population of the second stage including a plurality of chromosome units composed of three n-column chromosomes:

[0301] First, the first Nind2 individuals of the last generation population Chrom1 of the first stage are used as the initial population of the second stage genetic algorithm for assigning the number of large ships to chromosome 3; wherein Nind2 represents the population size of the second stage, and it should be noted that Nind1 is much larger than Nind2.

[0302] Second, a processing and transportation cost-based processing plant priority selection mechanism is introduced to generate the initial population of chromosome 1 assigning the number of plants; the priority selection mechanism is that a certain selection probability is set to generate the initial population of chromosome 1 assigning the number of bases, and the selection probability is set according to the principle that the lower the sum of production and domestic land transportation cost, the higher the selection probability.

[0303] In detail, first, for each export order, the processing plants are sorted from small to large according to the sum of production and domestic land transportation cost C pro+str , and the processing plant priority order matrix Nind2 rows and n columns. For example, for an order j, the processing plant priority matrix In the jth column, the first row is the processing plant with the lowest production and domestic land transportation cost, the second row is the processing plant with the second lowest cost, and so on. Therefore, for all export orders, C pro+str The lowest processing plant is in the first row of the processing plant priority matrix The position number in the processing plant priority matrix

[0304] The first Nind2 / 2 individuals of the initial population of chromosome 1 are set to the first row of each processing plant priority matrix In the last Nind2 / 2 individuals, a certain probability P is set for each export order to select a processing plant in P is set to [0.5, 0.33, 0.25, 0.2, 0.167, 0.14, 0.125, 0.11, 0.1, 0.09, …], representing a probability of 0.5 to select the first row of the processing plant corresponding to the order in , a probability of 0.33 to select the second row, and so on. The smaller the position number of the base in , the smaller the probability of being selected.

[0305] Step 3, under the ideal assumption that the storage time is 0, the starting processing date is obtained by backtracking from the overseas order deadline;

[0306]

[0307] The above three steps will generate an initial population of three decision variables; the three initial populations are related to each other by the chromosome individual number, i.e. the chromosomes in the three populations with the same individual number form a chromosome unit.

[0308] S422, constraint checking is performed on the chromosomes in the initial population generated in S421; if it does not pass, it is modified according to the preset constraint processing rule;

[0309] (1) For constraint one: the departure time of the large ship must be later than the time when the order arrives at the domestic port; the preset constraint processing rule is: through the initial population generation rule, the proportion of chromosomes in the initial population that violate constraint one is reduced (corresponding to step S422); by directly modifying the chromosomes, the chromosomes that violate constraint one are modified to chromosomes that meet constraint one (corresponding to subsequent step S427).

[0310] (2) For constraint two: two order tasks assigned to the same processing plant cannot be processed at the same time; the preset constraint processing rule is:

[0311] The first step is to determine the set of orders to be evaluated. If the set is empty, the process ends; otherwise, proceed to the second step.

[0312] The second step is to determine whether there is a conflict between the processing time of the last overseas order and the second-to-last overseas order; if so, skip to the third step; if not, skip to the fourth step.

[0313] Third, if there is no conflict, remove the last order from the order set and return to the second step; if there is a conflict, move the processing time of the last overseas order forward to make it non-conflicting, while keeping the domestic land transportation time unchanged. The time difference is used to store the order in a warehouse near the processing plant, waiting for domestic transportation. Remove the last order from the order set and return to the second step.

[0314] The fourth step is to move on to the next processing plant or the next chromosome and repeat the first step.

[0315] Specific examples Figure 9 As shown, in this embodiment of the invention, a CV matrix is ​​set to determine whether a population violates constraint one. The CV matrix is ​​a matrix that stores the degree to which individuals in the population violate each constraint, including the number of rows corresponding to the number of chromosomes and the number of columns corresponding to the number of constraint conditions.

[0316] S423. Calculate the fitness of each individual in the population, and let Gen2 = 0; where the fitness function in the second stage consists of the non-dominated ordination level and the crowding degree. The higher the non-dominated ordination level, the lower the crowding degree, and the higher the fitness of the chromosome individual.

[0317] S424. Determine whether the preset termination condition is met. If it is met, output the Pareto optimal solution in the current population and decode it as the final robust optimization result of the multi-factory production task planning. Otherwise, proceed to S425.

[0318] S425. Preserve the parent population and perform a single-point crossover operation;

[0319] In this step, only chromosome 1 (assigned to the base numbered chromosome) and chromosome 3 (assigned to the large ship numbered chromosome) in the chromosome unit are subjected to single-point crossover operation. In the two chromosome individuals that have undergone crossover operation, one crossover point is randomly set in each chromosome for splitting, and the genes on the right side of the crossover point are exchanged, thus obtaining two different chromosomes.

[0320] S426. Perform breeder mutation operation;

[0321] This step only allocates base numbers to chromosomes 1 in the chromosome unit and large ship numbers to chromosomes 3, and performs breeder GA mutation operation on the chromosomes respectively, and uses compression rate (MutShrink) and gradient division (Gradient) to control the mutation distance, the greater the compression rate and the fewer the gradient divisions, the greater the mutation range and the stronger the global search ability.

[0322] Chromosome 2 in the chromosome unit starts the processing date, and no crossover and mutation operation is performed, and chromosome 1 and chromosome 3 after the crossover and mutation are completed are used as known conditions to calculate the starting processing date of chromosome 2 by using the formula in step S421.

[0323] S427, constraint checking is performed on the chromosomes in the current population, if not passed, correction is performed according to a preset constraint processing rule, a child population containing Nind2 individuals is generated;

[0324] S428, the child and the parent are merged, the fitness of each individual in the merged population is calculated, the first Nind2 individuals with higher fitness are selected, Gen2 is set to Gen2+1, and S424 is entered.

[0325] The embodiment of the application provides a multi-target robust optimization system for multi-agent collaborative platform production task planning, comprising:

[0326] An acquisition module is configured to acquire multi-agent collaborative platform production task planning resources and single-batch overseas orders.

[0327] A construction module is configured to construct a production task planning model with a manufacturing enterprise agent as a core under the premise of determining sea transportation time according to the multi-agent collaborative platform production task planning resources and the single-batch overseas orders.

[0328] An optimization module is configured to represent uncertain sea transportation time by an infinity norm ball uncertainty set according to the production task planning model, and construct a robust quadratic multi-target model for production task planning.

[0329] A solution module is configured to convert the robust quadratic multi-target model into an equivalent model, solve the equivalent model, and acquire a robust optimization result for cross-regional multi-agent production task planning.

[0330] The embodiment of the application provides a storage medium storing a computer program for multi-target robust optimization of multi-agent collaborative platform production task planning, wherein the computer program enables a computer to execute the multi-target robust optimization method for multi-agent collaborative platform production task planning.

[0331] The embodiment of the application provides an electronic device, comprising:

[0332] one or more processors;

[0333] a memory; and

[0334] 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, and the programs include a multi-objective robust optimization method for performing the multi-agent collaborative platform production task planning as described above.

[0335] It can be understood that the multi-agent collaborative platform production task planning multi-objective robust optimization system, storage medium and electronic equipment provided by the embodiments of the present application correspond to the multi-agent collaborative platform production task planning multi-objective robust optimization method provided by the embodiments of the present application, and the relevant content explanation, examples and beneficial effects of the method can refer to the corresponding parts in the multi-agent collaborative platform production task planning multi-objective robust optimization method, which will not be repeated here.

[0336] In summary, compared with the prior art, the present application has the following beneficial effects:

[0337] The embodiments of the present application design an infinity norm ball uncertain set for large ship sea transportation time, construct a robust quadratic multi-objective model of cross-regional multi-agent production task planning, and optimize the total delivery time deviation and total delivery cost multi-objective of a single batch of overseas orders. The model considers the shipping plan prepared in advance by the shipping company agent and the long and uncertain sea transportation time. In addition, the model is converted into an equivalent model, and a two-stage hybrid heuristic algorithm is designed to solve it, and a reasonable and feasible production task planning suggestion is given for a single batch of overseas orders.

[0338] It should be noted that in this paper, relational terms such as first and second are used merely to distinguish one entity or action from another entity or action, without necessarily requiring or implying that there is any such actual relationship or order between these entities or actions. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or equipment including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or equipment. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or equipment including the element.

[0339] The above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the present application; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that the technical solutions recorded in the foregoing examples can be modified, or some technical features can be replaced by equivalent features; and these modifications or replacements 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 application.

Claims

1. A multi-objective robust optimization method for production task planning in a multi-agent collaborative platform, characterized in that, include: S1. Obtain production task planning resources and single-batch overseas orders from a multi-entity collaborative platform; S2. Based on the production task planning resources of the multi-entity collaborative platform and the single batch of overseas orders, and under the premise of determining the sea freight time, construct a production task planning model with the manufacturing enterprise as the core. S3. Based on the production task planning model, an uncertain sea transport time is represented by the ∞ norm sphere uncertainty set, and a robust squared multi-objective model for production task planning is constructed. S4. Transform the robust squared multi-objective model into an equivalent model, solve the equivalent model, and obtain the robust optimization results of cross-regional multi-agent production task planning. The robust squared multi-objective model in S3 includes: (1) The first objective function aims to minimize the 2-norm of the difference between the actual delivery date and the expected delivery date of all overseas orders in a single batch of overseas orders: (2) The second objective function aims to minimize the total delivery cost of all overseas orders in a single batch of overseas orders: in, This represents a collection of overseas orders. ; Represents a set of factories. ; This represents a collection of domestic ports. ; This indicates the assembly of large ships. ; Assuming uncertain parameters, actual sea transport time Inside a sphere For the center of the ball, Let the radius of the sphere be . Let be the error value; ;matrix This is a matrix representing the actual sea travel time for each ship. This is a matrix representing the historical average sea travel time for each ship. This is a matrix showing the deviation between the actual sea travel time of each ship and its historical average sea travel time. There are constraints on the actual sea travel time for all large ships: Let s be an s-dimensional real vector. If the norm of the matrix is ​​∞, then the spherical uncertainty set of the actual sea travel time of the large ship with the ∞ norm is expressed as: Indicates a large ship s The sea freight departure date; As a decision variable, if overseas orders j The manufacturing tasks are assigned to the factory i And overseas orders j The maritime transport tasks were assigned to large ships s， Set to 1; otherwise set to 0; variable If overseas orders j The maritime transport tasks were assigned to large ships s， Select 1; otherwise select 0. Indicates overseas orders j The customer's order date; Indicates overseas orders j The number of products; These represent overseas orders. j Processing cost, warehousing cost, domestic land transportation cost, and international sea transportation cost per unit product; Indicates overseas orders j At the factory i Processing cost per unit product; As a decision variable, if overseas orders j and Manufacturing tasks are all assigned to the factory. i And overseas orders j After production, overseas orders followed. If the result is 1, then 0; otherwise, 0. Indicates overseas orders j Daily warehousing cost per unit of product; Indicates overseas orders j Storage time; Indicates factory i to port g Domestic transportation costs per unit of product between; If a large ship s At the port g Set off, ;otherwise ; Indicates a large ship s Transportation cost per unit of product; The robust squared multi-objective model in S3 also includes: Constraints: in, , These represent overseas orders. j、 The start date of processing; This represents a very large constant; Indicates factory i to port g Land transport time; Indicates overseas orders j At the factory i Processing time.

2. The multi-objective robust optimization method for production task planning in a multi-entity collaborative platform as described in claim 1, characterized in that, The equivalent model in S4 includes: Objective 1: Objective Two: Constraints: in, This represents the ship's dispatch date assigned to order j minus the customer's delivery date.

3. The multi-objective robust optimization method for production task planning in a multi-entity collaborative platform as described in claim 2, characterized in that, The equivalent model is solved using a two-stage hybrid heuristic algorithm, including: Phase 1: Without considering the choice of processing plant and the start date of processing, the equivalent model is simplified, and a genetic algorithm is designed to search for multiple maritime task schemes, and a population containing multiple chromosomes with assigned ship numbers is obtained. Phase Two: The last generation of the above population is used as a partial initial population containing chromosomes with multiple ship number assignments. A partial initial population containing chromosomes with multiple factory number assignments and a partial initial population containing chromosomes with multiple start processing dates are also generated. A genetic algorithm is designed to obtain the Pareto optimal solution, and the solution is decoded as the final robust optimization result for multi-factory production task planning.

4. The multi-objective robust optimization method for production task planning in a multi-entity collaborative platform as described in claim 3, characterized in that, The first stage specifically includes: S411. In the equivalent model, separate only the decision variables related to the allocation of the large ship. Based on the relevant objectives and constraints, the following sub-models are simplified: Objective 1: Objective Two: Constraints: S412. Solve the sub-model obtained in step S411, randomly generate an initial population for the first stage consisting of multiple n columns of chromosomes, calculate the fitness of each individual in the population, and let... The fitness function in this step is: in, , where are constants, and represent the weights of objectives one and two, respectively; Indicates the first The fitness value for the first stage corresponding to each chromosome; Indicates the population size in the first stage; S413. If the preset termination condition is met, the current population is sorted in non-ascending order according to fitness and output as the last generation population Chrom1; otherwise, proceed to S414. S414, Copy A stallion is composed of elite individuals; S415. Using roulette wheel selection, select from the parent population after removing elite individuals. individual; S416. Merge stallions with the selected population; S417, Perform a two-point crossover operation; S418. Perform the breeder mutation operation to generate a new generation of population and calculate the fitness of each individual in the population; let Return to S413.

5. The multi-objective robust optimization method for production task planning in a multi-entity collaborative platform as described in claim 3 or 4, characterized in that, The second stage specifically includes: S421. Generate the initial population for the second stage, consisting of multiple chromosome units composed of 3 n columns of chromosomes: The first two individuals of the last generation of the first stage population (Chrom1) are used as the initial population for assigning ship numbers on chromosome 3 in the second stage genetic algorithm; where Nind2 represents the population size in the second stage, and Nind2... Nind ; A processing plant priority selection mechanism is introduced, using processing and transportation costs as the criterion, to generate a partial initial population of chromosome 1 with assigned plant numbers. This priority selection mechanism means that a certain selection probability is set to generate the initial population of chromosome 1 with assigned base numbers. The principle for setting the selection probability is that the lower the sum of production and domestic land transportation costs, the higher the selection probability is set for the factory. Under the ideal assumption of zero storage time, the start date of processing is obtained by working backward from the overseas order's deadline for delivery. S422. Perform constraint verification on the chromosomes in the initial population generated in S421; if it fails, correct it according to the preset constraint processing rules. S423. Calculate the fitness of each individual in the population, let In the second stage, the fitness function consists of the non-dominated ranking level and the crowding degree. The higher the non-dominated ranking level and the lower the crowding degree, the higher the fitness of the chromosome individual. S424. Determine whether the preset termination condition is met. If it is met, output the Pareto optimal solution in the current population and decode it as the final robust optimization result of the multi-factory production task planning. Otherwise, proceed to S425. S425. Preserve the parent population and perform a single-point crossover operation; S426. Perform breeder mutation operation; S427. Perform constraint verification on the chromosomes in the current population; if it fails, correct it according to the preset constraint processing rules and generate a progeny population containing Nind2 individuals. S428. Merge offspring and parents, calculate the fitness of each individual in the merged population, and select the first two individuals with higher fitness (Nind2). Let... Switch to S424.

6. The multi-objective robust optimization method for production task planning in a multi-entity collaborative platform as described in claim 5, characterized in that, (1) Regarding constraint one: the departure time of the large ship must be later than the time when the order arrives at the domestic port; the preset constraint processing rule specifically refers to: reducing the proportion of chromosomes that violate constraint one in the initial population through the generation rule of the initial population; and correcting chromosomes that violate constraint one to chromosomes that conform to constraint one by directly modifying chromosomes. And / or (2) Regarding constraint two: two order tasks assigned to the same processing plant cannot be processed simultaneously; the preset constraint processing rules specifically refer to: The first step is to determine the set of orders to be evaluated. If the set is empty, the process ends; otherwise, proceed to the second step. The second step is to determine whether there is a conflict in the processing time between the last overseas order and the second-to-last overseas order in the order set. If yes, skip to step three; if no, skip to step four. The third step is to move the processing time of the last overseas order forward to avoid conflict, while keeping the domestic land transportation time unchanged. The time difference is used to store the order in a warehouse near the processing plant, waiting for domestic transportation. The last order is then removed from the order set to update the order set. Return to the second step. The fourth step is to move on to the next processing plant or the next chromosome and repeat the first step.

7. A multi-objective robust optimization system for production task planning in a multi-entity collaborative platform, applied to the multi-objective robust optimization method as described in claim 1, characterized in that, include: The acquisition module is used to acquire production task planning resources and single-batch overseas orders from the multi-entity collaborative platform. The construction module is used to build a production task planning model with the manufacturing enterprise as the core, based on the production task planning resources and single batch overseas orders of the multi-entity collaborative platform, and under the premise of determining the sea freight time. The optimization module is used to construct a robust squared multi-objective model for production task planning based on the production task planning model, by representing uncertain sea transport time through the ∞ norm sphere uncertainty set. The solution module is used to transform the robust squared multi-objective model into an equivalent model, solve the equivalent model, and obtain the robust optimization results of cross-regional multi-agent production task planning.

8. A storage medium, characterized in that, It stores a computer program for multi-objective robust optimization of production task planning for a multi-entity collaborative platform, wherein the computer program causes the computer to execute the multi-objective robust optimization method for production task planning of a multi-entity collaborative platform as described in any one of claims 1 to 6.

9. 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 multi-objective robust optimization method for planning production tasks on a multi-agent collaborative platform as described in any one of claims 1 to 6.

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