Fresh agricultural product picking and distribution integrated scheduling method based on coevolution algorithm

Abstract

The invention provides a fresh agricultural product picking and distribution integrated scheduling method based on a coevolution algorithm, and relates to the technical field of agricultural product supply chain management and optimization, and the method comprises the steps: setting a constraint condition for a fresh agricultural product picking and distribution integrated scheduling problem; defining an agricultural product freshness loss function and a picking team working efficiency function; defining two objective functions, including an operation cost minimization function and a customer satisfaction maximization function; based on the constraint condition and the objective function, establishing a dual-objective optimization model about fresh agricultural product picking and delivery integrated scheduling; and solving the dual-objective optimization model to obtain an optimal solution set, and performing agricultural product scheduling according to picking and distribution information in the optimal solution set. According to the method, the operation cost of farmers can be remarkably reduced, the profit margin is improved, meanwhile, it is ensured that consumers obtain high-quality and high-freshness agricultural products within the demand time period, and therefore the service satisfaction degree is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of agricultural product supply chain management and optimization, and in particular to a method for integrated dispatching of fresh agricultural product picking and distribution based on a co-evolutionary algorithm. Background Art

[0002] In recent years, the continuous improvement of national living standards has prompted the public to pay more and more attention to healthy diets, and the demand for fresh organic agricultural products has increased dramatically. In this context, "community-supported agriculture" as an emerging agricultural product supply chain model has received widespread attention from the society. This model aims to provide fresh and healthy fresh agricultural products through direct connection between urban community residents and local farms, and establish an operating mechanism of risk-sharing and benefit-sharing. Under the "community-supported agriculture" model, farms provide full-process services from picking to distribution of fresh agricultural products to meet the needs of community residents, ensuring that agricultural products are picked, distributed and delivered on the same day.

[0003] However, despite the rapid development of the "community-supported agriculture" model, it also faces some practical problems that need to be solved urgently. Among them, the high freshness requirements of agricultural products and the high cost of picking logistics are two major challenges. Farms are usually located in the suburbs of cities and are geographically remote, while customers are spread all over the city, which leads to inefficient distribution and high distribution costs. In addition, the freshness of fresh agricultural products is a key factor in their value. Once picked, their freshness will gradually decrease over time.

[0004] Therefore, how to effectively integrate the harvesting and distribution processes of farms and provide consumers with fresh agricultural products with high freshness at low cost has become the key to farm survival and profitability. However, most current studies focus only on the single stage of harvesting or distribution, and few studies focus on the integrated optimization of these two stages. Summary of the invention

[0005] To this end, an embodiment of the present invention provides an integrated scheduling method for picking and delivering fresh agricultural products based on a collaborative evolutionary algorithm, which is used to solve the problem that most of the existing technologies only focus on single-stage optimization of picking or delivery of fresh agricultural products, and lack an effective solution for integrated optimization of the two stages of picking and delivery.

[0006] In order to solve the above problems, an embodiment of the present invention provides a method for integrated scheduling of picking and distribution of fresh agricultural products based on a collaborative evolutionary algorithm, the method comprising:

[0007] For the integrated scheduling problem of picking and delivering fresh agricultural products, set constraints, including farmers receiving customer orders, arranging picking teams to pick agricultural products in an optimized picking order, and arranging vehicles to deliver agricultural products from farms to customers along optimized routes and finally returning to farms;

[0008] Define the agricultural product freshness loss function and the picking team work efficiency function;

[0009] Define two objective functions, including minimizing the operating cost function and maximizing the customer satisfaction function, where the operating cost includes the picking cost, transportation cost and vehicle usage cost, and customer satisfaction is related to the freshness of the delivered agricultural products and the delivery time;

[0010] Based on the constraints and the objective function, a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution is established;

[0011] Solve the dual-objective optimization model to obtain an optimal solution set, and dispatch agricultural products according to the picking and delivery information in the optimal solution set.

[0012] Preferably, the constraints also include:

[0013] Each type of agricultural product can only be picked by one picking team;

[0014] Each picking team can only pick one type of agricultural product at a time;

[0015] Each picking team works at different rates and will become fatigued as the picking time increases;

[0016] Each agricultural product has a specific freshness loss rate, and the freshness of agricultural products decreases over time after they are picked;

[0017] Each customer may need more than one agricultural product;

[0018] Each customer has a scheduled delivery time. If the vehicle arrives later than the delivery time, there will be a delay and customer satisfaction will be reduced.

[0019] Each customer is delivered by only one vehicle;

[0020] The time it takes for the vehicle to depart from the farm is equal to the time it takes to complete the picking of the last produce that needs to be loaded;

[0021] The vehicle carrying capacity is limited, that is, the total vehicle load at any point on the route is not greater than the vehicle carrying capacity.

[0022] Preferably, the agricultural product freshness loss function is used to describe the change of freshness of agricultural products over time from picking to delivery to customers, and is expressed as:

[0023]

[0024] Where F represents the initial freshness of agricultural products, that is, the maximum freshness; θ j is the freshness loss rate of agricultural product j; F jRepresents the freshness of agricultural product j after time t.

[0025] Preferably, the picking team work efficiency function is used to reflect the fatigue effect of the picking team as the working time increases, and is expressed as:

[0026] t′ jl =t jl +λ jl ·S j ;

[0027] Where t′ jl represents the actual picking time of picking team l picking agricultural product j; t jl represents the picking time of agricultural product j by picking team l; jl represents the fatigue rate of picking team l picking agricultural product j; S j Represents the picking start time of agricultural product j.

[0028] Preferably, the minimization operation cost function is:

[0029] minf1=b·∑ j∈P\{0} ∑ l∈M Y jl +e·∑ m∈Q ∑ n∈Q ∑ h∈K Z mnk ·v mn +r·∑ m∈J ∑ h∈K Z 0mh ;

[0030] In the formula, f1 represents the operating cost, b represents the picking cost per unit time, e represents the driving cost per unit time, r represents the fixed cost of each vehicle, and Y jl represents the total picking time of picking team l for picking agricultural product j; Z mnk If vehicle h visits node m and then node n, then Z mnh =1; otherwise, Z mnh =0;v mn represents the travel time from node m to node n; Z 0mn If vehicle h departs from the farm and goes to customer m, then Z 0mh =1; otherwise, Z 0mh=0; m, n∈Q, representing the node index, where Q={0}∪J, representing the set of all nodes, 0 representing the farm, J={1, 2,…, q}, representing the customer set, and q representing the total number of customers; h∈K, representing the vehicle index, where K={1, 2,…, k}, representing the vehicle set, and k representing the number of vehicles; l∈M, representing the picking team index, where M={1, 2,…, g}, representing the picking team set, and g representing the number of picking teams; j∈P, representing the agricultural product index, where P={0, 1, 2,…, p}, representing the agricultural product set, where 0 represents the virtual agricultural product, and p represents the number of agricultural product types; P\{0} represents the agricultural product set that does not contain 0.

[0031] Preferably, the maximizing customer satisfaction function is expressed by minimizing freshness loss and delivery delay:

[0032] minf2=∑ m∈J ψ m +∑ m∈J ξ m ;

[0033] Where f2 represents freshness loss and delivery delay; ψ m Get the maximum freshness loss in agricultural products on behalf of customer m; ξ m represents the delivery delay caused by visiting customer m; m∈J, J={1, 2, …, q}, represents the customer set, and q represents the total number of customers.

[0034] Preferably, the dual-objective optimization model is expressed as:

[0035]

[0036]

[0037] ∑ n∈Q ∑ m∈J ∑ j∈P\{0} Z nmh ·d mj ≤δ, h∈K;

[0038]

[0039]

[0040] Where i, j∈P represents the agricultural product index, where P = {0, 1, 2, ..., p}, represents the agricultural product set, where 0 represents the virtual agricultural product, and p represents the number of agricultural product types; P\{0} represents the agricultural product set that does not contain 0; m, n∈Q represents the node index, where Q = {0}∪J, represents the set of all nodes, 0 represents the farm, J = {1, 2, ..., q}, represents the customer set, and q represents the total number of customers; h∈

[0041] K represents the vehicle index, where K = {1, 2, ..., k}, represents the vehicle set, and k represents the number of vehicles; l∈M represents the picking team index, where M = {1, 2, ..., g}, represents the picking team set, and g represents the number of picking teams; d mj The demand of customer m for agricultural product j; R mj If customer m needs agricultural product j, then R mj =1; otherwise, R mj =0;θ j represents the freshness loss rate of agricultural product j; t lj represents the picking time of agricultural product j by picking team l; jl represents the fatigue rate of picking team l picking agricultural product j; L m represents the delivery time required by customer m; v mn represents the travel time from node m to node n; v 0m represents the travel time from node m to the farm; δ represents the maximum load of each vehicle; B represents infinity; U jl If picking team l picks agricultural product j, then U jl =1; otherwise, U jl =0;X ijl If picking team l picks agricultural product i and then picks agricultural product j, then X ijl =1; otherwise, X ijl =0; W mh If vehicle h serves customer m, then W mh =1; otherwise, W mh =0; Z mnh If vehicle h visits node m and then node n, then Z mnh =1; otherwise, Z mnh =0; Z 0mh If vehicle h departs from the farm and goes to customer m, then Z 0mh =1; otherwise, Z 0mh =0;D h represents the time when vehicle h departs from the farm; A m represents the time when the vehicle arrives at customer m; S j represents the picking start time of agricultural product j; Y jl represents the total picking time of picking team l for picking agricultural product j; C j represents the harvesting completion time of agricultural product j; represents the freshness loss of agricultural product j when it is delivered to customer m; ψ m Get the maximum freshness loss of agricultural products on behalf of customer m; t′ jl represents the actual picking time of picking team l picking agricultural product j;m The delivery delay caused by visiting customer m.

[0042] Preferably, the method for solving the dual-objective optimization model to obtain the optimal solution set is:

[0043] The co-evolution algorithm is used to solve the dual-objective optimization model to obtain the optimal solution set, wherein the co-evolution algorithm includes:

[0044] S51: Initialize algorithm parameters, including population size N, local search probability α, local search iteration number β, number of subproblems in the decomposition method T, Q table, initial state, and maximum number of iterations;

[0045] S52: randomly generate an initial population, including solutions represented by N integer codes;

[0046] S53: Evaluate the target values ​​of N solutions in the population and store the non-dominated solution set in an external archive;

[0047] S54: An evolutionary framework for selecting populations based on their current state;

[0048] S55: The population performs evolution and updates the external archive;

[0049] S56: external archive performs evolution and updates the population;

[0050] S57: Calculate the reward value based on the change of state and update the Q table;

[0051] S58: If the maximum number of iterations is reached, terminate, otherwise execute S54;

[0052] S59: Finally output the optimal solution set obtained from the external archive.

[0053] The embodiment of the present invention further provides a fresh agricultural product picking and distribution integrated scheduling system based on a collaborative evolutionary algorithm, which is used to implement the above-mentioned fresh agricultural product picking and distribution integrated scheduling method based on a collaborative evolutionary algorithm, and specifically includes:

[0054] The constraint setting module is used to set constraints for the integrated scheduling problem of picking and delivering fresh agricultural products, including farmers receiving customer orders, arranging picking teams to pick agricultural products in an optimized picking order, and arranging vehicles to deliver agricultural products from farms to customers according to optimized routes and finally returning to farms;

[0055] Function definition module, used to define the agricultural product freshness loss function and the picking team work efficiency function;

[0056] An objective function definition module is used to define two objective functions, including minimizing the operating cost function and maximizing the customer satisfaction function, where the operating cost includes the picking cost, transportation cost and vehicle use cost, and the customer satisfaction is related to the freshness of the delivered agricultural products and the delivery time;

[0057] A dual-objective optimization model establishment module, used to establish a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution based on the constraint conditions and the objective function;

[0058] The solution and scheduling module is used to solve the dual-objective optimization model, obtain the optimal solution set, and schedule agricultural products according to the picking and distribution information in the optimal solution set.

[0059] An embodiment of the present invention also provides a computer storage medium, which stores computer software agricultural products, and the computer software agricultural products include a number of instructions for enabling a computer device to execute the above-mentioned integrated scheduling method for picking and distributing fresh agricultural products based on the collaborative evolutionary algorithm.

[0060] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0061] (1) Compared with previous studies that only focused on a certain link in the supply of fresh agricultural products, this invention innovatively integrates the two stages of picking and distribution. By implementing an integrated scheduling strategy, it effectively reduces the intermediate links and realizes direct connection between consumers and farmers. This not only enables consumers to obtain more affordable and high-quality fresh agricultural products such as vegetables and fruits, but also actively responds to the national policies of community support for agriculture and direct procurement and supply, which has important practical significance.

[0062] (2) Aiming at the problem of integrated scheduling of fresh agricultural product picking and distribution, the present invention constructs a dual-objective optimization model. This model not only comprehensively considers the operating costs generated in the supply service process, but also deeply analyzes customers' satisfaction with product freshness and delivery time. This model can not only help farmers reduce operating costs and increase profit margins, but also ensure that consumers obtain high-quality and fresh agricultural products during the demand period. Although these two goals are in conflict to a certain extent, a balance between the two can be achieved by seeking a Pareto equilibrium solution.

[0063] (3) In order to solve the dual-objective optimization model, the present invention proposes a collaborative evolutionary algorithm and designs corresponding encoding methods, crossover methods and local search strategies. At the same time, combined with reinforcement learning technology, the effectiveness of the algorithm in solving problems is further improved. In a series of examples, the algorithm was compared with three classic multi-objective algorithms to fully verify the effectiveness and superiority of the model and algorithm. The final Pareto equilibrium solution performed well in reducing costs, reducing agricultural product freshness losses and delivery delays, thereby significantly improving service satisfaction. The present invention provides a useful reference and reference for farmers and fresh food e-commerce. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] In order to more clearly illustrate the implementation cases of the present invention or the technical solutions in the prior art, the following is a brief description of the drawings required for use in the embodiments. By referring to the drawings, the features and advantages of the present invention will be more clearly understood. The drawings are schematic and should not be understood as limiting the present invention in any way. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0065] Figure 1 It is a flow chart of a method for integrated scheduling of picking and distribution of fresh agricultural products based on a collaborative evolutionary algorithm provided in an embodiment;

[0066] Figure 2 Schematic diagram of the integrated scheduling problem of fresh agricultural product picking and distribution in two stages in the embodiment;

[0067] Figure 3 is a flow chart of the co-evolution algorithm in the embodiment;

[0068] Figure 4 Schematic diagram of encoding and decoding in the embodiment;

[0069] Figure 5 A schematic diagram of a crossover method in an embodiment;

[0070] Figure 6 1 is a box plot of the IGD index obtained by the four algorithms in the embodiment on different examples, wherein (a), (b), (c), (d), (e), (f), (g), (h), and (i) are box plots of the IGD index obtained by the four algorithms on examples of 4 agricultural products, 6 agricultural products, 8 agricultural products, 10 agricultural products, 25 customers, 50 customers, 2 picking teams, 3 picking teams, and 4 picking teams, respectively;

[0071] Figure 7Figure 1 is a box plot of the Hypervolume index obtained by the four algorithms in the embodiment on different examples, where (a), (b), (c), (d), (e), (f), (g), (h), and (i) are box plots of the Hypervolume index obtained by the four algorithms on examples of 4 agricultural products, 6 agricultural products, 8 agricultural products, 10 agricultural products, 25 customers, 50 customers, 2 picking teams, 3 picking teams, and 4 picking teams, respectively;

[0072] Figure 8 A block diagram of an integrated dispatching system for picking and distributing fresh agricultural products based on a collaborative evolutionary algorithm provided in an embodiment. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0074] Embodiment 1

[0075] In order to solve the problem that most existing technologies only focus on the single-stage optimization of fresh agricultural product picking or distribution, and lack effective solutions for the integrated optimization of the two stages of picking and distribution. Figure 1 As shown, the embodiment of the present invention proposes a method for integrated scheduling of picking and distribution of fresh agricultural products based on a collaborative evolutionary algorithm, the method comprising:

[0076] S1: For the integrated scheduling problem of picking and delivering fresh agricultural products, set constraints, including farmers receiving customer orders, arranging picking teams to pick agricultural products in an optimized picking order, and arranging vehicles to deliver agricultural products from farms to customers along optimized routes and finally returning to farms;

[0077] S2: Define the agricultural product freshness loss function and the picking team work efficiency function;

[0078] S3: Define two objective functions, including minimizing the operating cost function and maximizing the customer satisfaction function, where the operating cost includes the picking cost, transportation cost and vehicle usage cost, and customer satisfaction is related to the freshness of the delivered agricultural products and the delivery time;

[0079] S4: Based on the constraint conditions and the objective function, a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution is established;

[0080] S5: Solve the dual-objective optimization model to obtain an optimal solution set, and dispatch agricultural products according to the picking and delivery information in the optimal solution set.

[0081] It can be seen from the above technical scheme that the present invention is a fresh agricultural product picking and distribution integrated scheduling method based on a co-evolutionary algorithm, which realizes the reduction of intermediate links in the fresh agricultural product supply chain and the direct connection between consumers and farmers by integrating the two stages of picking and distribution. Its technical core lies in the construction of a dual-objective optimization model, which not only takes into account the minimization of operating costs, but also takes into account the maximization of customer satisfaction, especially the sensitivity of agricultural product freshness and delivery time. In order to solve this complex model, the present invention designs a co-evolutionary algorithm, combined with reinforcement learning technology, which effectively improves the solution efficiency. The advantage of this technical solution is that it can not only significantly reduce the operating costs of farmers and increase profit margins, but also ensure that consumers obtain high-quality and high-freshness agricultural products during the demand period, thereby greatly improving service satisfaction. In addition, this method also actively responds to the policies advocated by the country such as community support for agriculture and direct procurement and direct supply, has important practical significance and application value, and provides a useful reference and reference for farmers and fresh e-commerce.

[0082] The integrated scheduling problem of fresh agricultural product picking and distribution in two stages studied in this paper is as follows: Figure 2 As shown in Figure 1, there are four decisions involved: (1) the task allocation of the picking teams, that is, allocating the picking of agricultural products to each picking team; (2) the order in which each picking team picks agricultural products; (3) the allocation of transportation vehicles to customer points, that is, allocating customer points to each transportation vehicle; and (4) the delivery route of each vehicle.

[0083] In step S1, constraints are set for the integrated scheduling problem of picking and delivering fresh agricultural products, including that farmers (farmers) receive customer orders, arrange picking teams to pick agricultural products (various vegetables and fruits) in an optimized picking order, and arrange vehicles to deliver agricultural products to customers from the farm according to the optimized route, and finally return to the farm. Constraints also include that each type of agricultural product can only be picked by one picking team; each picking team can only pick one type of agricultural product at the same time; each picking team has different work efficiency and will become fatigued as the picking time increases; each agricultural product has a specific freshness loss rate, and the freshness of agricultural products decreases over time after picking; each customer may need more than one type of agricultural product; each customer has a scheduled delivery time, and if the vehicle arrives later than the delivery time, a delay will occur and customer satisfaction will be reduced; each customer is delivered by only one vehicle; the time when the vehicle departs from the farm is equal to the time when the last type of agricultural product to be loaded is completed; the vehicle carrying capacity is limited, that is, the total vehicle load at any point on the route is not greater than the vehicle carrying capacity.

[0084] Define the model symbols, including the required collections, indexes, parameters, variables and other symbols, as shown in Table 1 below.

[0085] Table 1

[0086]

[0087]

[0088] In step S2, the agricultural product freshness loss function and the picking team work efficiency function are defined. Specifically, the agricultural product freshness loss function is used to describe the change of freshness over time in the process from picking to delivering agricultural products to customers. The freshness of agricultural products is one of the important factors affecting customer satisfaction. Therefore, this function is crucial for evaluating customer satisfaction. Generally speaking, the agricultural product freshness loss function can be expressed as a function of time. As time goes by, the freshness of agricultural products gradually decreases. Specifically, the agricultural product freshness loss function is expressed as:

[0089]

[0090] In the formula, F represents the initial freshness of agricultural products, that is, the maximum freshness; F j Represents the freshness of agricultural product j after time t.

[0091] The work efficiency function of the picking team is used to reflect the fatigue effect of the picking team as the working hours increase. The work efficiency of the picking team directly affects the picking cost and time, which in turn affects the operating cost and customer satisfaction. The work efficiency function can be expressed as a function of working time. As the working hours increase, the work efficiency of the picking team gradually decreases, that is, the fatigue effect gradually appears. Specifically, the work efficiency function of the picking team is expressed as:

[0092] t′ jl =t jl +λ jl ·S j .

[0093] In step S3, two objective functions are defined, including minimizing the operating cost function and maximizing the customer satisfaction function, where the operating cost is an important goal of the model optimization, including picking cost, transportation cost and vehicle use cost. The picking cost is affected by the work efficiency of the picking team, while the transportation cost and vehicle use cost are related to the distribution route and vehicle arrangement. Therefore, by optimizing the work efficiency of the picking team and the distribution route, the operating cost can be reduced. Specifically, the minimizing operating cost function can be expressed as:

[0094] minf1=b·∑ j∈P\{0} ∑ l∈M Y jl+e·∑ m∈Q ∑ n∈Q ∑ h∈K Z mnk ·v mn +r·∑ m∈J ∑ h∈K Z 0mh ;

[0095] Where f1 represents the operating cost.

[0096] Customer satisfaction is another important optimization objective, which is related to the freshness of the delivered produce and the delivery time. The higher the freshness of the produce and the more punctual the delivery time, the higher the customer satisfaction. The freshness of the produce is affected by the produce freshness loss function, and the delivery time is affected by the overall scheduling of the picking and distribution process. Specifically, the maximization of the customer satisfaction function is expressed by minimizing the freshness loss and delivery delay:

[0097] minf2=∑ m∈J ψ m +∑ m∈J ξ m ;

[0098] Where f2 represents freshness loss and delivery delay.

[0099] In step S4, based on the above constraints and objective functions, a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution is established, where the dual-objective optimization model is expressed as:

[0100]

[0101]

[0102] ∑ n∈Q ∑ m∈J ∑ j∈P\{0} Z nmh ·d mj ≤δ, h∈K;

[0103]

[0104]

[0105] In step S5, the above dual-objective optimization model is solved to obtain the optimal solution set (Pareto equilibrium solution), and agricultural products are dispatched according to the picking and delivery information in the optimal solution set, including determining task allocation, picking sequence, vehicle allocation and delivery routes.

[0106] Specifically, this embodiment uses a co-evolutionary algorithm to solve the dual-objective optimization model and obtain the optimal solution set, wherein the process of the co-evolutionary algorithm is as follows: Figure 3As shown, specifically including:

[0107] S51: Initialize algorithm parameters, including population size N, local search probability α, local search iteration number β, number of subproblems in the decomposition method T, Q table, initial state, and maximum number of iterations;

[0108] S52: randomly generate an initial population, including solutions represented by N integer codes;

[0109] S53: Evaluate the target values ​​of N solutions in the population and store the non-dominated solution set in an external archive;

[0110] S54: Evolutionary frameworks for selecting populations based on their current state, including dominance-based frameworks and decomposition-based frameworks;

[0111] S55: The population performs evolution and updates the external archive;

[0112] S56: external archive performs evolution and updates the population;

[0113] S57: Calculate the reward value based on the change of state and update the Q table;

[0114] S58: If the maximum number of iterations is reached, terminate, otherwise execute S54;

[0115] S59: Finally output the optimal solution set obtained from the external archive.

[0116] The present invention uses integer coding to represent the solution of the problem, including three integer strings, where integer string 1 represents the number of types of picked agricultural products assigned to each picking team; integer string 2 represents the picking order of agricultural products; integer string 3 represents the delivery order of customers. Two decisions in the picking stage are decoded by integer string 1 and integer string 2, such as Figure 4 As shown, picking team 1 is responsible for picking two agricultural products, picking agricultural products 2 and agricultural products 1 in turn, and picking team 2 is responsible for picking two agricultural products, picking agricultural products 4 and agricultural products 3 in turn. According to customer demand, vehicle loading limit and integer string 3, the two decisions in the distribution stage are decoded, as follows Figure 4 As shown, the customer demand is given. Assuming that the maximum vehicle load limit is 6, customers are assigned to the same vehicle in sequence according to the integer string 3 until the load of a vehicle reaches the upper limit, then another vehicle is rearranged, and finally 4 delivery routes are obtained.

[0117] In addition, if Figure 5As shown in the figure, in the process of using the co-evolutionary algorithm to solve the optimal solution set, integer string 1 uses the uniform crossover method, and integer string 2 and integer string 3 both use the sequential crossover method. The uniform crossover design is as follows: randomly generate the number 0 or 1, if the number 1 is generated, copy the gene from parent 1, if the number 0 is generated, copy the gene from parent 2. Then perform a repair operation, that is, calculate whether the sum of the types of agricultural products picked by each picking team is equal to the sum of the types of products on the farm. If it is greater, randomly select a picking team, subtract 1 from the number of agricultural products picked, and continue to calculate and check until the sum is exactly equal to the sum of the types of products on the farm; if it is less than, randomly select a picking team, add 1 to the number of agricultural products picked, and continue to calculate and check until the sum is exactly equal to the sum of the types of products on the farm. As shown in the figure, Figure 5 As shown, the first gene (2) of parent individual 1 and the second gene (3) of parent individual 2 are copied to the offspring individual, and then it is calculated that 2+3>4. The first picking team is randomly selected and the number of its picking species is subtracted by 1, so the integer string 1 of the offspring is finally obtained (1, 3). The sequential crossover design is as follows: two different cutting points are randomly selected, and the gene segment between the cutting points is selected as the crossover segment; the crossover segment in parent 1 is copied to the corresponding gene position of the offspring individual, and the gene of the crossover segment of parent 1 is deleted in parent 2, and the remaining genes are assigned to the offspring individuals in sequence.

[0118] In order to verify the effectiveness and superiority of the collaborative evolution algorithm (denoted as Q-CEA-K) proposed in this paper, three algorithms were selected as comparison algorithms, namely, non-dominated sorting genetic algorithm II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEA / D), and multi-objective artificial bee colony algorithm (MOABC), and they were run 20 times on different examples. The IGD index and Hypervolume index were used to measure the performance of the algorithm. It is worth noting that the smaller the IGD index value, the better the algorithm performance, while the larger the Hypervolume index value, the better the algorithm performance. Figure 6 and Figure 7 The box plots obtained on the two indicators are given. It can be seen that the performance of the co-evolutionary algorithm is better than that of the other three algorithms.

[0119] Embodiment 2

[0120] like Figure 8 As shown, the present invention provides a fresh agricultural product picking and distribution integrated scheduling system based on a collaborative evolutionary algorithm, which is used to implement the fresh agricultural product picking and distribution integrated scheduling method based on a collaborative evolutionary algorithm in the first embodiment, and specifically includes:

[0121] The constraint setting module 100 is used to set constraints for the integrated scheduling problem of picking and delivering fresh agricultural products, including farmers receiving customer orders, arranging picking teams to pick agricultural products according to an optimized picking sequence, and arranging vehicles to deliver agricultural products to customers from the farm according to an optimized route, and finally returning to the farm;

[0122] Function definition module 200, used to define the agricultural product freshness loss function and the picking team work efficiency function;

[0123] An objective function definition module 300 is used to define two objective functions, including a function of minimizing operating costs and a function of maximizing customer satisfaction, wherein the operating costs include picking costs, transportation costs, and vehicle use costs, and customer satisfaction is related to the freshness of the delivered agricultural products and the delivery time;

[0124] A dual-objective optimization model building module 400 is used to build a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution based on constraints and objective functions;

[0125] The solution and scheduling module 500 is used to solve the dual-objective optimization model, obtain the optimal solution set, and schedule agricultural products according to the picking and delivery information in the optimal solution set.

[0126] A fresh agricultural product picking and delivery integrated scheduling system based on a collaborative evolution algorithm in this embodiment is used to implement the aforementioned fresh agricultural product picking and delivery integrated scheduling method based on a collaborative evolution algorithm. Therefore, the specific implementation method of the fresh agricultural product picking and delivery integrated scheduling system based on a collaborative evolution algorithm can be seen in the embodiment part of the fresh agricultural product picking and delivery integrated scheduling method based on a collaborative evolution algorithm in the previous text. For example, the constraint setting module 100, the function definition module 200, the objective function definition module 300, the dual-objective optimization model establishment module 400, and the solution and scheduling module 500 are respectively used to implement steps S1, S2, S3, S4, and S5 in the above-mentioned fresh agricultural product picking and delivery integrated scheduling method based on a collaborative evolution algorithm. Therefore, its specific implementation method can refer to the description of the corresponding embodiments of each part. In order to avoid redundancy, it will not be repeated here.

[0127] Embodiment 3

[0128] An embodiment of the present invention provides a computer storage medium storing computer software agricultural products, wherein the computer software agricultural products include a number of instructions for enabling a computer device to execute the above-mentioned integrated scheduling method for picking and distributing fresh agricultural products based on a collaborative evolutionary algorithm.

[0129] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0130] The present application is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0131] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0132] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from them are still within the protection scope of the invention.

Claims

1. A method for integrated dispatching of fresh agricultural products picking and distribution based on a co-evolutionary algorithm, characterized in that: include: For the integrated scheduling problem of picking and delivering fresh agricultural products, set constraints, including farmers receiving customer orders, arranging picking teams to pick agricultural products in an optimized picking order, and arranging vehicles to deliver agricultural products from farms to customers along optimized routes and finally returning to farms; Define the agricultural product freshness loss function and the picking team work efficiency function; Define two objective functions, including minimizing the operating cost function and maximizing the customer satisfaction function, where the operating cost includes the picking cost, transportation cost and vehicle usage cost, and customer satisfaction is related to the freshness of the delivered agricultural products and the delivery time; Based on the constraints and the objective function, a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution is established; Solve the dual-objective optimization model to obtain an optimal solution set, and dispatch agricultural products according to the picking and delivery information in the optimal solution set.

2. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The constraints also include: Each type of agricultural product can only be picked by one picking team; Each picking team can only pick one type of agricultural product at a time; Each picking team works at different rates and will become fatigued as the picking time increases; Each agricultural product has a specific freshness loss rate, and the freshness of agricultural products decreases over time after they are picked; Each customer may need more than one agricultural product; Each customer has a scheduled delivery time. If the vehicle arrives later than the delivery time, there will be a delay and customer satisfaction will be reduced. Each customer is delivered by only one vehicle; The time it takes for the vehicle to depart from the farm is equal to the time it takes to complete the picking of the last produce that needs to be loaded; The vehicle carrying capacity is limited, that is, the total vehicle load at any point on the route is not greater than the vehicle carrying capacity.

3. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The agricultural product freshness loss function is used to describe the change of freshness over time in the process from picking agricultural products to delivering them to customers, and is expressed as: Where F represents the initial freshness of agricultural products, that is, the maximum freshness; θ j is the freshness loss rate of agricultural product j; F j Represents the freshness of agricultural product j after time t.

4. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The picking team work efficiency function is used to reflect the fatigue effect of the picking team as the working time increases, and is expressed as: t′ jl =t jl +λ jl ·S j ; Where t′ jl represents the actual picking time of picking agricultural product j by picking team l; t jl represents the picking time of agricultural product j by picking team l; jl represents the fatigue rate of picking team l picking agricultural product j; S j Represents the picking start time of agricultural product j.

5. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The minimization operation cost function is: minf1=b·∑ j∈P\{0} ∑ l∈M Y jl +e·∑ m∈Q ∑ n∈Q ∑ h∈K Z mnk ·v mn +r·∑ m∈J ∑ h∈K Z 0mh ; In the formula, f1 represents the operating cost, b represents the picking cost per unit time, e represents the driving cost per unit time, r represents the fixed cost of each vehicle, and Y jl represents the total picking time of picking team l for picking agricultural product j; Z mnk If vehicle h visits node m and then node n, then Z mnh =1; otherwise , Z mnh =0;v mn Represents the travel time from node m to node n; Z 0mn If vehicle h departs from the farm and goes to customer m, then Z 0mh =1; otherwise, Z 0mh =0; m,n∈Q, represents the node index, where Q={0}∪J, represents the set of all nodes, 0 represents the farm, J={1,2,…,q}, represents the customer set, and q represents the total number of customers; h∈K, represents the vehicle index, where K={1,2,…,k}, represents the vehicle set, and k represents the number of vehicles; l∈M, represents the picking team index, where M={1,2,…,g}, represents the picking team set, and g represents the number of picking teams; j∈P, represents the agricultural product index, where P={0,1,2,…,p}, represents the agricultural product set, where 0 represents the virtual agricultural product, and p represents the number of agricultural product types; P\{0} represents the agricultural product set that does not contain 0.

6. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The maximized customer satisfaction function is expressed by minimizing freshness loss and delivery delay: minf2=∑ m∈J ψ m +∑ m∈J x m ; Where f2 represents freshness loss and delivery delay; ψ m Get the maximum freshness loss in agricultural products on behalf of customer m; ξ m represents the delivery delay caused by visiting customer m; m∈J, J={1,2,…,q}, represents the customer set, and q represents the total number of customers.

7. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The dual-objective optimization model is expressed as: ∑ n∈Q ∑ m∈J ∑ i∈P\{0} Z nmh ·d mj ≤δ,h∈K; Where i,j∈P represents the agricultural product index, where P={0,1,2,…,p}, represents the agricultural product set, where 0 represents virtual agricultural products, and p represents the number of agricultural product types; P\{0} represents the agricultural product set that does not contain 0; m,n∈Q represents the node index, where Q={0}∪J, represents the set of all nodes, 0 represents the farm, J={1,2,…,q} represents the customer set, and q represents the total number of customers; h∈K represents the vehicle index, where K={1,2,…,K} represents the vehicle set, and k represents the number of vehicles; l∈M represents the picking team index, where M={1,2,…,g} represents the picking team set, and g represents the number of picking teams; d mj The demand of customer m for agricultural product j; R mj If customer m needs agricultural product j, then R mj =1; otherwise , R mj =0;θ j represents the freshness loss rate of agricultural product j; t lj represents the picking time of agricultural product j by picking team l; jl represents the fatigue rate of picking team l picking agricultural product j; L m represents the delivery time required by customer m; v mn Represents the travel time from node m to node n; v 0m represents the travel time from node m to the farm; δ represents the maximum load of each vehicle; B stands for infinity; U jl If picking team l picks agricultural product j, then U jl =1; otherwise , U jl =0;X ijl If picking team l picks agricultural product i and then picks agricultural product j, then X ijl =1; otherwise, X ijl =0; W mh If vehicle h serves customer m, then W mh =1; otherwise, W mh =0; Z mnh If vehicle h visits node m and then node n, then Z mnh =1; otherwise, Z mnh =0; Z 0mh If vehicle h departs from the farm and goes to customer m, then Z 0mh =1; otherwise, Z 0mh =0;D h represents the time when vehicle h departs from the farm; A m represents the time when the vehicle arrives at customer m; S j represents the picking start time of agricultural product j; Y jl represents the total picking time of picking team l for picking agricultural product j; C j represents the harvesting completion time of agricultural product j; represents the freshness loss of agricultural product j when it is delivered to customer m; ψ m Get the maximum freshness loss of agricultural products on behalf of customer m; t′ jl represents the actual picking time of picking agricultural product j by picking team l; ξ m The delivery delay caused by visiting customer m.

8. The integrated dispatching method for picking and distributing fresh agricultural products based on the collaborative evolution algorithm according to claim 1 is characterized in that: The method for solving the dual-objective optimization model to obtain the optimal solution set is: The co-evolution algorithm is used to solve the dual-objective optimization model to obtain the optimal solution set, wherein the co-evolution algorithm includes: S51: Initialize algorithm parameters, including population size N, local search probability α, local search iteration number β, number of subproblems in the decomposition method T, Q table, initial state, and maximum number of iterations; S52: randomly generate an initial population, including solutions represented by N integer codes; S53: Evaluate the target values ​​of N solutions in the population and store the non-dominated solution set in an external archive; S54: An evolutionary framework for selecting populations based on their current state; S55: The population performs evolution and updates the external archive; S56: external archive performs evolution and updates the population; S57: Calculate the reward value based on the change of state and update the Q table; S58: If the maximum number of iterations is reached, terminate, otherwise execute S54; S59: Finally output the optimal solution set obtained from the external archive.

9. A fresh agricultural product picking and distribution integrated scheduling system based on a collaborative evolutionary algorithm, characterized in that: The system is used to implement the integrated scheduling method for picking and distributing fresh agricultural products based on the collaborative evolutionary algorithm as described in any one of claims 1 to 8, specifically comprising: The constraint setting module is used to set constraints for the integrated scheduling problem of picking and delivering fresh agricultural products, including farmers receiving customer orders, arranging picking teams to pick agricultural products in an optimized picking order, and arranging vehicles to deliver agricultural products from farms to customers according to optimized routes and finally returning to farms; Function definition module, used to define the agricultural product freshness loss function and the picking team work efficiency function; An objective function definition module is used to define two objective functions, including minimizing the operating cost function and maximizing the customer satisfaction function, where the operating cost includes the picking cost, transportation cost and vehicle use cost, and the customer satisfaction is related to the freshness of the delivered agricultural products and the delivery time; A dual-objective optimization model establishment module, used to establish a dual-objective optimization model for integrated scheduling of fresh agricultural product picking and distribution based on the constraint conditions and the objective function; The solution and scheduling module is used to solve the dual-objective optimization model, obtain the optimal solution set, and schedule agricultural products according to the picking and distribution information in the optimal solution set.

10. A computer storage medium, characterized in that: The computer storage medium stores computer software agricultural products, and the computer software agricultural products include a number of instructions for enabling a computer device to execute the integrated scheduling method for picking and distributing fresh agricultural products based on a collaborative evolutionary algorithm as described in any one of claims 1 to 8.

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