Multi-farm picking and distribution integrated scheduling method based on improved artificial bee colony algorithm

Abstract

This invention provides a multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm, belonging to the field of agricultural product supply chain management and optimization technology. The method includes setting constraints for the multi-farm harvesting and delivery integrated scheduling problem; defining an agricultural product freshness loss function; defining two objective functions, including minimizing operating costs and maximizing customer satisfaction; establishing a bi-objective optimization model for multi-farm harvesting and delivery integrated scheduling based on the constraints and objective functions; solving the bi-objective optimization model to obtain the optimal solution set; and scheduling agricultural products based on the harvesting and delivery information in the optimal solution set. This invention effectively integrates the harvesting and delivery links, reduces operating costs, ensures agricultural product freshness, and improves service satisfaction, providing an innovative and efficient solution for the agricultural product supply chain under the community-supported agriculture model.

Description

Technical Field

[0001] This invention relates to the field of agricultural product supply chain management and optimization technology, and in particular to a multi-farm harvesting and distribution integrated scheduling method based on an improved artificial bee colony algorithm. Background Technology

[0002] In recent years, with the continuous improvement of living standards, people have paid increasing attention to healthy eating, leading to a surge in demand for fresh and organic agricultural products. As an emerging agricultural supply chain model, "Community Supported Agriculture" (CSA) has attracted considerable attention. This model aims to establish direct connections between urban community residents and local farms, forming a cooperative relationship based on shared risks and benefits, with the goal of providing residents with fresh and healthy agricultural products. Under this framework, farms provide a one-stop service from "harvest to delivery," ensuring that fresh agricultural products are harvested, delivered, and arrived on the same day.

[0003] However, while the "community-supported agriculture" (CSA) model has developed rapidly, it also faces challenges such as the difficulty of preserving agricultural products and high harvesting costs. Because farms are typically located on the outskirts of cities and customers are widely distributed, delivery efficiency is low and delivery costs are high. Furthermore, the freshness of fresh produce is a key factor determining its value; once harvested, its freshness gradually decreases over time. In addition, to meet large-scale demand, multiple farms often cooperate and operate jointly. Therefore, how to effectively integrate the harvesting and delivery processes of multiple farms to provide high-freshness agricultural products at low cost has become a critical issue that urgently needs to be addressed.

[0004] However, current research largely focuses on single stages of harvesting or distribution, with insufficient exploration of comprehensive optimization for these two phases. Research integrating the harvesting and distribution processes of multiple farms into a unified scheduling framework is even rarer. Furthermore, existing research on agricultural product harvesting and distribution problems mostly employs traditional metaheuristic algorithms. Summary of the Invention

[0005] To address these issues, this invention provides a multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm. This method is designed to solve the problems of insufficient integration of agricultural product harvesting and delivery processes, high costs, and difficulty in ensuring freshness in the existing "community-supported agriculture" model.

[0006] To address the aforementioned problems, embodiments of the present invention provide a multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm, the method comprising:

[0007] To address the integrated scheduling problem of multi-farm harvesting and delivery, constraints are set, including multiple existing farms cooperating to provide agricultural products to customers. Managers receive customer orders and distribute them to each farm, assigning harvesting and delivery tasks to each farm. Specifically, the harvesting team is arranged to harvest agricultural products in an optimized order, and vehicles are arranged to deliver the agricultural products to customers from the farms along optimized routes and finally return to the farms.

[0008] Define an agricultural product freshness loss function, which describes how the freshness of agricultural products changes over time from harvesting to delivery to customers.

[0009] Define two objective functions: minimizing operating costs and maximizing customer satisfaction. Operating costs include harvesting costs, transportation costs, and vehicle usage costs, while customer satisfaction is related to the freshness of the delivered agricultural products.

[0010] Based on the constraints and the objective function, a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery is established.

[0011] Solve the bi-objective optimization model to obtain the optimal solution set, and then schedule agricultural products based on the harvesting and distribution information in the optimal solution set.

[0012] Preferably, the constraint conditions further include:

[0013] Each farm grows a variety of agricultural products, and each farm grows the same types of agricultural products;

[0014] Each type of agricultural product can only be harvested by one harvesting team;

[0015] Each picking team can only pick one type of agricultural product at a time;

[0016] Each type of agricultural product has a specific freshness loss rate; the freshness of agricultural products decreases over time after harvesting.

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

[0018] Each customer is assigned to only one farm to supply their produce;

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

[0020] The time it takes for the vehicle to leave the farm is equal to the time it takes for the last type of agricultural product to be loaded to be harvested.

[0021] The vehicle carrying capacity is limited, meaning that the total load of vehicles at any point on the route does not exceed the vehicle carrying capacity.

[0022] The starting and ending points of the vehicles must be the same farm.

[0023] Preferably, the agricultural product freshness loss function is expressed as:

[0024] ;

[0025] In the formula, This represents the initial freshness of agricultural products, i.e., their maximum freshness. Agricultural products Freshness loss rate; Representative agricultural products In time After that, the novelty wore off.

[0026] Preferably, the function for minimizing operating costs is:

[0027]

[0028] In the formula, Represents operating costs, The cost of harvesting per unit of time; This represents the cost of travel per unit of time. This represents the fixed operating cost of each vehicle; If the customer Allocated to farms ,but ;otherwise, ; On behalf of clients agricultural products Demand; The picking team agricultural products The harvesting time; If the farm The picking team Picking agricultural products ,but ;otherwise, ; Representative node To the node Travel time; If the farm vehicles Access Node Then visit the node ,but ;otherwise, ; , represents the node index, where , representing a set of nodes , representing a collection of customers Represents the total number of customers. , representing a collection of farms Represents the number of farms; , representing the vehicle index, where , representing a collection of vehicles Representative Farm The number of vehicles in the middle; , representing the farm index; , representing the index of the picking team, in which This represents the gathering of the picking team. Representative Farm The number of picking teams in China; , representing an agricultural product index, in which , representing a collection of agricultural products, where 0 represents virtual agricultural products. Represents the variety and quantity of agricultural products; This represents a set of agricultural products that do not contain 0.

[0029] Preferably, the function for maximizing customer satisfaction is represented by minimizing the loss of freshness:

[0030] ;

[0031] In the formula, This indicates a loss of freshness; On behalf of clients To capture the maximum loss of freshness in agricultural products; , , representing a collection of customers Represents the total number of customers.

[0032] Preferably, the dual-objective optimization model for the integrated scheduling of multi-farm harvesting and delivery is expressed as follows:

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[0063] In the formula, , representing an agricultural product index, in which , representing a collection of agricultural products, where 0 represents virtual agricultural products. Represents the variety and quantity of agricultural products; This represents a set of agricultural products that do not contain 0. , represents the node index, where , representing a set of nodes , representing a collection of customers Represents the total number of customers. , representing a collection of farms Represents the number of farms; , representing the vehicle index, where , representing a collection of vehicles Representative Farm The number of vehicles in the middle; , representing the index of the picking team, in which This represents the gathering of the picking team. Representative Farm The number of picking teams in China; On behalf of clients agricultural products Demand; If the customer Agricultural products are needed but ;otherwise, ; Agricultural products Freshness loss rate; Representative node To the node Travel time; This represents the maximum load capacity of each vehicle; Represents infinity; If the customer Allocated to farms ,but ;otherwise, ; If the farm The picking team Picking agricultural products ,but ;otherwise, ; If the farm The picking team After harvesting agricultural products Then came the harvesting of agricultural products. ,but ;otherwise, ; Representative Farm The picking team Picking virtual products Then came the harvesting of agricultural products. ; If the farm vehicles Serving customers ,but ;otherwise, ; If the farm vehicles Access Node Then visit the node ,but ;otherwise, ; middle and Representing two different farms, Representative Farm vehicles From the farm Depart for the customer ; middle and Representing two different farms, Representative Farm vehicles From the customer Return to the farm ; middle , and Representing three different farms, Representative Farm vehicles From the farm Return to the farm ; Representative vehicle From the farm Departure time; The vehicle arrived at the customer's location. Time; Representative Farm agricultural products The start time of harvesting; Representative Farm agricultural products The time for completion of harvesting; Representative agricultural products Delivered to customer The loss of freshness that occurs over time; On behalf of clients To capture the greatest loss of freshness in agricultural products.

[0064] Preferably, the method for solving the bi-objective optimization model to obtain the optimal solution set is as follows:

[0065] An improved artificial bee colony algorithm is used to solve a multi-objective optimization model to obtain the optimal solution set. Specifically, the improved artificial bee colony algorithm includes:

[0066] Step S61: Initialize algorithm parameters, including population size. Maximum number of tests Q-table, initial state, maximum running time; randomly generate the initial population, containing... The solutions are represented by integer codes, each solution is assigned the number of trials to be 0; evaluate the population. The objective value of each solution is stored in an external file;

[0067] Step S62: Perform the hired bee phase: The population performs a crossover operation. If a solution is promoted, its trial count is incremented by 1.

[0068] Step S63: Execute the bystander bee phase: Select the optimal neighborhood structure using the Q-learning method based on the current state. The population executes the selected neighborhood structure. If a solution is improved, its trial count is incremented by 1.

[0069] Step S64: Perform the scout bee phase: Check whether the number of trials for each solution in the population has reached the maximum limit. If the maximum limit is reached, a solution is randomly selected from an external file to replace the original solution.

[0070] Step S65: Population updates external archives; local search method is performed on the external archives.

[0071] Step S66: Calculate the reward value based on the state change and update the Q table;

[0072] Step S67: Determine whether the maximum running time has been reached. If it has, terminate; otherwise, return to step S62.

[0073] Step S68: Output the optimal solution set obtained from the external archive.

[0074] This invention also provides a multi-farm harvesting and delivery integrated scheduling system based on an improved artificial bee colony algorithm. This system is used to implement the aforementioned multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm, specifically including:

[0075] The constraint setting module is used to set constraints for the integrated scheduling problem of multi-farm harvesting and delivery. These constraints include multiple existing farms cooperating with each other to provide agricultural products to customers, managers receiving customer orders and allocating them to various farms, and assigning harvesting and delivery tasks to each farm. Specifically, the harvesting team is arranged to harvest agricultural products in an optimized harvesting order, and vehicles are arranged to deliver agricultural products to customers from the farms along optimized routes and finally return to the farms.

[0076] The function definition module is used to define the agricultural product freshness loss function, which describes the change in freshness of agricultural products over time from harvesting to delivery to customers.

[0077] The objective function definition module is used to define two objective functions: minimizing operating costs and maximizing customer satisfaction. Operating costs include harvesting costs, transportation costs, and vehicle usage costs, while customer satisfaction is related to the freshness of the delivered agricultural products.

[0078] The dual-objective optimization model establishment module is used to establish a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery based on the constraints and the objective function.

[0079] The solution and scheduling module is used to solve the bi-objective optimization model, obtain the optimal solution set, and schedule agricultural products based on the harvesting and distribution information in the optimal solution set.

[0080] This invention also provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to realize the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm described above.

[0081] This invention also provides a computer storage medium storing a computer software product, the computer software product including several instructions to cause a computer device to execute the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm described above.

[0082] As can be seen from the above technical solutions, this invention application has the following beneficial effects:

[0083] (1) Compared with previous studies that focused only on a single link in the fresh agricultural product supply chain, this invention achieves an innovative breakthrough by integrating the two key links of harvesting and distribution, and introducing a multi-farm collaborative operation model. By implementing a two-stage integrated scheduling strategy involving multiple farms, consumers can directly connect with farmers to obtain both economical and high-quality fresh agricultural products such as fruits and vegetables. This strategy actively responds to the national initiatives of community-supported agriculture and direct sourcing, demonstrating strong practical application value.

[0084] (2) To address the challenges of integrated scheduling of multi-farm harvesting and distribution, this invention constructs a model framework with dual objectives. This framework not only considers the operating costs in the agricultural product supply chain but also places great emphasis on consumer satisfaction with product freshness. This model aims to help farmers reduce costs and expand profit margins while ensuring that consumers can enjoy high-quality and fresh agricultural products. Although these two objectives are somewhat conflicting, a Pareto optimal solution can be found through problem-solving.

[0085] (3) To effectively solve the above bi-objective optimization model, this invention proposes an improved artificial bee colony algorithm. This algorithm is meticulously designed in terms of encoding rules, crossover strategies, neighborhood search structures, and local search methods, and incorporates reinforcement learning techniques to improve its solution efficiency. Comparative experiments with three classic multi-objective algorithms verify the effectiveness and superiority of this model and algorithm. The final Pareto optimal solution helps reduce operating costs and decrease the loss of freshness in agricultural products, thereby improving service satisfaction and providing valuable reference for farmers and the fresh food e-commerce industry. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Referring to the drawings will make the features and advantages of the present invention clearer. The drawings are illustrative and should not be construed as limiting the present invention in any way. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Wherein:

[0087] Figure 1 This is a flowchart illustrating a multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm, as provided in the embodiments.

[0088] Figure 2 This is a schematic diagram illustrating the integrated scheduling problem of multi-farm harvesting and delivery in the example;

[0089] Figure 3 This is a flowchart of the improved artificial bee colony algorithm in the embodiment;

[0090] Figure 4 This is a schematic diagram of the encoding and decoding of the solution in the embodiment;

[0091] Figure 5 This is a schematic diagram of the crossing method in the embodiment;

[0092] Figure 6 Box plots of IGD and HV indices obtained by the four algorithms in different computational examples are shown below.

[0093] Figure 7This is a block diagram of a multi-farm harvesting and delivery integrated scheduling system based on an improved artificial bee colony algorithm, as provided in the embodiment. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0095] Example 1

[0096] To address the issues of insufficient integration, high costs, and difficulty in guaranteeing freshness in the harvesting and distribution process of agricultural products under the existing "community-supported agriculture" model. For example... Figure 1 As shown in the figure, this invention proposes a multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm. The method includes:

[0097] To address the integrated scheduling problem of multi-farm harvesting and delivery, constraints are set, including multiple existing farms cooperating to provide agricultural products to customers. Managers receive customer orders and distribute them to each farm, assigning harvesting and delivery tasks to each farm. Specifically, the harvesting team is arranged to harvest agricultural products in an optimized order, and vehicles are arranged to deliver the agricultural products to customers from the farms along optimized routes and finally return to the farms.

[0098] Define an agricultural product freshness loss function, which describes how the freshness of agricultural products changes over time from harvesting to delivery to customers.

[0099] Define two objective functions: minimizing operating costs and maximizing customer satisfaction. Operating costs include harvesting costs, transportation costs, and vehicle usage costs, while customer satisfaction is related to the freshness of the delivered agricultural products.

[0100] Based on constraints and objective functions, a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery is established.

[0101] Solve the bi-objective optimization model to obtain the optimal solution set, and then schedule agricultural products based on the harvesting and distribution information in the optimal solution set.

[0102] As can be seen from the above technical solution, this invention proposes a multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm, aiming to solve the problems of insufficient integration, high costs, and difficulty in guaranteeing freshness in the existing "community-supported agriculture" model. This method integrates the two major links of harvesting and delivery, and introduces multi-farm collaboration, achieving full-chain optimization of agricultural products from harvesting to delivery. Simultaneously, this invention defines an agricultural product freshness loss function and two objective functions, constructing a dual-objective optimization model that includes minimizing operating costs and maximizing customer satisfaction. By solving this model, this invention obtains a Pareto optimal solution, achieving dual optimization of operating costs and customer satisfaction. Furthermore, the improved artificial bee colony algorithm proposed in this invention is carefully designed in terms of encoding, crossover strategy, neighborhood search, and local search, and incorporates reinforcement learning technology, significantly improving the solution efficiency. This method not only reduces operating costs and minimizes agricultural product freshness loss, but also improves service satisfaction, providing practical guidance and useful reference for farmers and the fresh food e-commerce industry, demonstrating strong practical application value and superiority.

[0103] A schematic diagram of the multi-farm harvesting and delivery integrated scheduling problem studied in this invention is shown below. Figure 2 This includes five decisions: (1) allocating customers (orders) to each farm; (2) assigning tasks to the picking teams in each farm, that is, assigning the harvested agricultural products to the picking teams; (3) the order of harvesting agricultural products by the picking teams; (4) the allocation between customer points and transport vehicles in each farm, that is, assigning customer points to transport vehicles; and (5) the delivery routes of the vehicles.

[0104] Specifically, for the integrated scheduling problem of multi-farm harvesting and delivery, constraints are set, including: multiple farms cooperating to provide agricultural products (various vegetables and fruits) to customers; managers receiving customer orders and allocating them to various farms, assigning harvesting and delivery tasks to each farm; that is, arranging harvesting teams to harvest agricultural products in an optimized order, and arranging vehicles to deliver agricultural products to customers from the farms along optimized routes, and finally returning to the farms; each farm grows multiple agricultural products, and each farm grows the same types of agricultural products; each agricultural product can only be harvested by one harvesting team; each harvesting team can only harvest one type of agricultural product at a time; each agricultural product has a specific freshness loss rate, and its freshness decreases over time after harvesting; each customer may need more than one type of agricultural product; each customer is only assigned to one farm to provide agricultural products; each customer is delivered by only one vehicle; the time it takes for a vehicle to depart from the farm is equal to the time it takes for the last type of agricultural product to be loaded to be harvested; the vehicle's carrying capacity is limited, that is, the total load of vehicles at any point on the route does not exceed the vehicle's carrying capacity; the starting point and ending point of the vehicle must be the same farm.

[0105] Furthermore, define the model symbols, including the sets, indices, parameters, variables, and other symbols that need to be used, as shown in Table 1 below.

[0106] Table 1

[0107] Specifically, this embodiment defines a freshness loss function for agricultural products. This function describes how the freshness of agricultural products changes over time, from harvesting to delivery to the customer. Freshness is a crucial factor affecting customer satisfaction; therefore, this function is essential for assessing customer satisfaction. Generally, the freshness loss function can be expressed as a function of time, where the freshness of agricultural products gradually decreases over time. Specifically, the freshness loss function is expressed as follows:

[0108] ;

[0109] In the formula, This represents the initial freshness of agricultural products, i.e., their maximum freshness. Representative agricultural products In time After that, the novelty wore off.

[0110] Specifically, this embodiment defines two objective functions: minimizing operating costs and maximizing customer satisfaction. Operating costs are a key objective of the model optimization, encompassing harvesting costs, transportation costs, and vehicle usage costs. Harvesting costs are influenced by the efficiency of the harvesting team, while transportation and vehicle usage costs are related to delivery routes and vehicle allocation. Therefore, by optimizing the harvesting team's efficiency and delivery routes, operating costs can be reduced. Specifically, the function for minimizing operating costs can be expressed as:

[0111]

[0112] In the formula, This represents operating costs.

[0113] Customer satisfaction is another important optimization objective, related to the freshness of delivered agricultural products. Higher freshness and more punctual delivery lead to higher customer satisfaction. Agricultural product freshness is influenced by a freshness loss function. Specifically, maximizing customer satisfaction is represented by minimizing the freshness loss:

[0114] ;

[0115] In the formula, This indicates a loss of freshness.

[0116] Specifically, based on the above constraints and objective function, this embodiment establishes a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery, wherein the dual-objective optimization model is expressed as follows:

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[0147] In the formula, Representative Farm The picking team Picking virtual products Then came the harvesting of agricultural products. ; middle and Representing two different farms, Representative Farm vehicles From the farm Depart for the customer ; middle and Representing two different farms, Representative Farm vehicles From the customer Return to the farm ; middle , and Representing three different farms, Representative Farm vehicles From the farm Return to the farm .

[0148] It should be noted that, ; ; All three constraints are set to 0 to ensure that the vehicle's starting and ending points are on the same farm.

[0149] Specifically, this embodiment employs an improved artificial bee colony algorithm to solve a multi-objective optimization model, obtaining an optimal solution set (Pareto equilibrium solution). Based on the harvesting and delivery information within the optimal solution set, agricultural product scheduling is performed, including determining customer order allocation, harvesting task allocation, harvesting order, vehicle allocation, and delivery routes. The improved artificial bee colony algorithm, such as... Figure 3 As shown, it specifically includes:

[0150] Step S61: Initialize algorithm parameters, including population size. Maximum number of tests Q-table, initial state, maximum running time; randomly generate the initial population, containing... The solutions are represented by integer codes, each solution is assigned the number of trials to be 0; evaluate the population. The objective value of each solution is stored in an external file;

[0151] Step S62: Perform the hired bee phase: The population performs a crossover operation. If a solution is promoted, its trial count is incremented by 1.

[0152] Step S63: Execute the bystander bee phase: Select the optimal neighborhood structure using the Q-learning method based on the current state. The population executes the selected neighborhood structure. If a solution is improved, its trial count is incremented by 1.

[0153] Step S64: Perform the scout bee phase: Check whether the number of trials for each solution in the population has reached the maximum limit. If the maximum limit is reached, a solution is randomly selected from an external file to replace the original solution.

[0154] Step S65: Population updates external archives; local search method is performed on the external archives.

[0155] Step S66: Calculate the reward value based on the state change and update the Q table;

[0156] Step S67: Determine whether the maximum running time has been reached. If it has, terminate; otherwise, return to step S62.

[0157] Step S68: Output the optimal solution set obtained from the external archive.

[0158] Furthermore, this embodiment also uses integer encoding to represent the solution to the problem, including three integer strings, integer string 1 ( ) represents the customer's allocation to the farm, and the integer string 2 ( ) represents the harvesting tasks of each farm (including the harvesting order of agricultural products and the types and quantities of agricultural products allocated to the harvesting teams), and the integer string 3 ( () indicates the customer's delivery order. For example... Figure 4From integer string 1, we know that customers 1, 3, 5, and 9 are assigned to farm 1; the remaining customers are assigned to farm 2. From integer string 2, we know that: Farm 1's harvesting team 1 is responsible for harvesting two types of agricultural products, harvesting agricultural product 1 and agricultural product 2 in sequence; Farm 1's harvesting team 2 is responsible for harvesting one type of agricultural product, namely agricultural product 3. Similarly, Farm 2's harvesting team 1 is responsible for harvesting two types of agricultural products, harvesting agricultural product 2 and agricultural product 1 in sequence; Farm 2's harvesting team 2 is responsible for harvesting one type of agricultural product, namely agricultural product 3. Finally, based on customer demand, vehicle loading limits, and integer string 3, we can obtain the decision for the delivery stage: according to integer string 3, customers from the same farm are sequentially assigned to the same vehicle until a vehicle reaches its loading limit, at which point a new vehicle is assigned, ultimately obtaining the delivery route.

[0159] In addition, such as Figure 5 As shown, in the process of solving the optimal solution set using the improved artificial bee colony algorithm, integer strings 1 and 2 use a uniform crossover method, while integer string 3 uses a sequential crossover method. The uniform crossover design is as follows: a random number 0 or 1 is generated. If the number 1 is generated, the gene is copied from parent generation 1; if the number 0 is generated, the gene is copied from parent generation 2. The sequential crossover design is as follows: two different cut points are randomly selected, and the gene segment between the cut points is selected as the crossover segment. The crossover segment from parent generation 1 is copied to the corresponding gene position in the offspring individuals. In parent generation 2, the gene from the crossover segment of parent generation 1 is deleted, and the remaining genes are assigned to the offspring individuals sequentially.

[0160] To verify the effectiveness and superiority of the improved artificial bee colony algorithm (Q-ABC-K) proposed in this embodiment, three algorithms were selected as comparison algorithms: Non-dominated sorting genetic algorithm II (NSGA-II), decomposition-based multi-objective evolutionary algorithm (MOEA / D), and multi-objective brainstorming optimization algorithm (MOBSO). These algorithms were run 20 times on examples of different sizes. The IGD and HV metrics were used to measure the algorithm performance. It is worth noting that a smaller IGD value indicates better algorithm performance, while a larger HV value indicates better algorithm performance. Figure 6 Box plots obtained on two metrics are presented. It can be seen that the improved artificial bee colony algorithm outperforms the other three algorithms.

[0161] like Figure 7 As shown, this invention provides a multi-farm harvesting and delivery integrated scheduling system based on an improved artificial bee colony algorithm. This system is used to implement the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm described in Embodiment 1 above, specifically including:

[0162] The constraint setting module 100 is used to set constraints for the integrated scheduling problem of multi-farm harvesting and delivery. These constraints include multiple existing farms cooperating with each other to provide agricultural products to customers, managers receiving customer orders and allocating them to each farm, and arranging harvesting and delivery tasks for each farm. Specifically, the harvesting team is arranged to harvest agricultural products in an optimized harvesting order, and vehicles are arranged to deliver agricultural products to customers from the farms along optimized routes and finally return to the farms.

[0163] Function definition module 200 is used to define the agricultural product freshness loss function, which describes the change in freshness of agricultural products over time from harvesting to delivery to customers.

[0164] The objective function definition module 300 is used to define two objective functions, including a function to minimize operating costs and a function to maximize customer satisfaction. The operating costs include harvesting costs, transportation costs, and vehicle usage costs, while customer satisfaction is related to the freshness of the delivered agricultural products.

[0165] The dual-objective optimization model building module 400 is used to build a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery based on constraints and objective functions.

[0166] The solution and scheduling module 500 is used to solve the bi-objective optimization model, obtain the optimal solution set, and schedule agricultural products based on the harvesting and distribution information in the optimal solution set.

[0167] This embodiment presents a multi-farm harvesting and delivery integrated scheduling system based on an improved artificial bee colony algorithm. This system implements the aforementioned multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm. Therefore, the specific implementation of the multi-farm harvesting and delivery integrated scheduling system based on the improved artificial bee colony algorithm can be found in the previous section on the implementation of the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm. For example, the constraint setting module 100, function definition module 200, objective function definition module 300, bi-objective optimization model establishment module 400, and solution and scheduling module 500 are respectively used to implement steps S1, S2, S3, S4, and S5 in the aforementioned multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm. Therefore, its specific implementation can be referred to the descriptions of the corresponding embodiments. To avoid redundancy, further details are omitted here.

[0168] Example 3

[0169] This invention provides an electronic device, which includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to realize the above-mentioned multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm.

[0170] Example 4

[0171] This invention provides a computer storage medium storing a computer software product, which includes several instructions to cause a computer device to execute the above-described multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm.

[0172] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied 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.

[0173] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0174] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0175] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A multi-farm harvesting and delivery integrated scheduling method based on an improved artificial bee colony algorithm, characterized in that, include: To address the integrated scheduling problem of multi-farm harvesting and delivery, constraints are set, including multiple existing farms cooperating to provide agricultural products to customers. Managers receive customer orders and distribute them to each farm, assigning harvesting and delivery tasks to each farm. Specifically, the harvesting team is arranged to harvest agricultural products in an optimized order, and vehicles are arranged to deliver the agricultural products to customers from the farms along optimized routes and finally return to the farms. Define an agricultural product freshness loss function, which describes how the freshness of agricultural products changes over time from harvesting to delivery to customers. Two objective functions are defined: a function to minimize operating costs and a function to maximize customer satisfaction. Operating costs include harvesting costs, transportation costs, and vehicle usage costs. Customer satisfaction is related to the freshness of the delivered agricultural products. The function to maximize customer satisfaction is expressed by minimizing the loss of freshness. ； In the formula, This indicates a loss of freshness; On behalf of clients To capture the maximum loss of freshness in agricultural products; , , representing a collection of customers Represents the total number of customers; Based on the constraints and the objective function, a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery is established. An improved artificial bee colony algorithm is used to solve the bi-objective optimization model to obtain the optimal solution set, and agricultural products are scheduled based on the harvesting and distribution information in the optimal solution set. The improved artificial bee colony algorithm specifically includes: Step S61: Initialize algorithm parameters, including population size. Maximum number of trials Q-table, initial state, maximum running time; randomly generate the initial population, containing... The solutions are represented by integer codes, each solution is assigned the number of trials to be 0; evaluate the population. The objective value of each solution is stored in an external file; Step S62: Perform the hired bee phase: The population performs a crossover operation. If a solution is promoted, its trial count is incremented by 1. Step S63: Execute the bystander bee phase: Select the optimal neighborhood structure using the Q-learning method based on the current state. The population executes the selected neighborhood structure. If a solution is improved, its trial count is incremented by 1. Step S64: Perform the scout bee phase: Check whether the number of trials for each solution in the population has reached the maximum limit. If the maximum limit is reached, a solution is randomly selected from an external file to replace the original solution. Step S65: Population updates external archives; local search method is performed on the external archives. Step S66: Calculate the reward value based on the state change and update the Q table; Step S67: Determine whether the maximum running time has been reached. If it has, terminate; otherwise, return to step S62. Step S68: Output the optimal solution set obtained from the external file.

2. The multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm according to claim 1, characterized in that, The constraints also include: Each farm grows a variety of agricultural products, and each farm grows the same types of agricultural products; Each type of agricultural product can only be harvested by one harvesting team; Each picking team can only pick one type of agricultural product at a time; Each type of agricultural product has a specific freshness loss rate; the freshness of agricultural products decreases over time after harvesting. Each customer may need more than one type of agricultural product; Each customer is assigned to only one farm to supply their produce; Each customer is delivered by only one vehicle; The time it takes for the vehicle to leave the farm is equal to the time it takes for the last type of agricultural product to be loaded to be harvested. The vehicle carrying capacity is limited, meaning that the total load of vehicles at any point on the route does not exceed the vehicle carrying capacity. The starting and ending points of the vehicles must be the same farm.

3. The multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm according to claim 1, characterized in that, The freshness loss function for agricultural products is expressed as follows: ； In the formula, This represents the initial freshness of agricultural products, i.e., their maximum freshness. Agricultural products Freshness loss rate; Representative agricultural products In time After that, the novelty wore off.

4. The multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm according to claim 1, characterized in that, The function for minimizing operating costs is: ； In the formula, Represents operating costs, The cost of harvesting per unit of time; This represents the cost of travel per unit of time. This represents the fixed operating cost of each vehicle; If the customer Allocated to farms ,but ; otherwise, ; On behalf of clients agricultural products Demand; The picking team agricultural products The harvesting time; If the farm The picking team Picking agricultural products ,but ; otherwise, ; Representative node To the node Travel time; If the farm vehicles Access Node Then visit the node ,but ; otherwise, ; , represents the node index, where , representing a set of nodes , representing a collection of customers Represents the total number of customers. , representing a collection of farms Represents the number of farms; , representing the vehicle index, where , representing a collection of vehicles Representative Farm The number of vehicles in the middle; , representing the farm index; , representing the index of the picking team, in which This represents the gathering of the picking team. Representative Farm The number of picking teams in China; , representing an agricultural product index, in which , representing a collection of agricultural products, where 0 represents virtual agricultural products. Represents the variety and quantity of agricultural products; This represents a set of agricultural products that do not contain 0.

5. The multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm according to claim 1, characterized in that, The dual-objective optimization model for the integrated scheduling of multi-farm harvesting and delivery is expressed as follows: ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； ； In the formula, , representing an agricultural product index, in which , representing a collection of agricultural products, where 0 represents virtual agricultural products. Represents the variety and quantity of agricultural products; This represents a set of agricultural products that do not contain 0. , represents the node index, where , representing a set of nodes , representing a collection of customers Represents the total number of customers. , representing a collection of farms Represents the number of farms; , representing the vehicle index, where , representing a collection of vehicles Representative Farm The number of vehicles in the middle; , representing the index of the picking team, in which This represents the gathering of the picking team. Representative Farm The number of picking teams in China; On behalf of clients agricultural products Demand; If the customer Agricultural products are needed but ; otherwise, ; Agricultural products Freshness loss rate; Representative node To the node Travel time; This represents the maximum load capacity of each vehicle; Represents infinity; If the customer Allocated to farms ,but ; otherwise, ; If the farm The picking team Picking agricultural products ,but ; otherwise, ; If the farm The picking team After harvesting agricultural products Then came the harvesting of agricultural products. ,but ; otherwise, ; Representative Farm The picking team Picking virtual products Then came the harvesting of agricultural products. ; If the farm vehicles Serving customers ,but ; otherwise, ; If the farm vehicles Access Node Then visit the node ,but ; otherwise, ; middle and Representing two different farms, Representative Farm vehicles From the farm Depart for the customer ; middle and Representing two different farms, Representative Farm vehicles From the customer Return to the farm ; middle , and Representing three different farms, Representative Farm vehicles From the farm Return to the farm ; Representative vehicle From the farm Departure time; The vehicle arrived at the customer's location. Time; Representative Farm agricultural products The start time of harvesting; Representative Farm agricultural products The time for completion of harvesting; Representative agricultural products Delivered to customer The loss of freshness that occurs over time; On behalf of clients To capture the greatest loss of freshness in agricultural products.

6. A multi-farm harvesting and delivery integrated scheduling system based on an improved artificial bee colony algorithm, characterized in that, The system is used to implement the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm as described in any one of claims 1 to 5, specifically including: The constraint setting module is used to set constraints for the integrated scheduling problem of multi-farm harvesting and delivery. These constraints include multiple existing farms cooperating with each other to provide agricultural products to customers, managers receiving customer orders and allocating them to various farms, and assigning harvesting and delivery tasks to each farm. Specifically, the harvesting team is arranged to harvest agricultural products in an optimized harvesting order, and vehicles are arranged to deliver agricultural products to customers from the farms along optimized routes and finally return to the farms. The function definition module is used to define the agricultural product freshness loss function, which describes the change in freshness of agricultural products over time from harvesting to delivery to customers. The objective function definition module is used to define two objective functions: minimizing operating costs and maximizing customer satisfaction. Operating costs include harvesting costs, transportation costs, and vehicle usage costs, while customer satisfaction is related to the freshness of the delivered agricultural products. The dual-objective optimization model establishment module is used to establish a dual-objective optimization model for integrated scheduling of multi-farm harvesting and delivery based on the constraints and the objective function. The solution and scheduling module is used to solve the bi-objective optimization model, obtain the optimal solution set, and schedule agricultural products based on the harvesting and distribution information in the optimal solution set.

7. An electronic device, characterized in that, The electronic device includes a processor, a memory, and a bus system. The processor and the memory are connected through the bus system. The memory is used to store instructions, and the processor is used to execute the instructions stored in the memory to implement the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm as described in any one of claims 1 to 5.

8. A computer storage medium, characterized in that, The computer storage medium stores a computer software product, which includes several instructions to cause a computer device to execute the multi-farm harvesting and delivery integrated scheduling method based on the improved artificial bee colony algorithm as described in any one of claims 1 to 5.

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