Method, device and storage medium for solving cold chain transportation optimization with processing time

Abstract

The present invention relates to a method, device and storage medium for solving cold chain transportation optimization with processing time, which is applied to the field of cold chain logistics technology. The method comprises: establishing a route optimization model and limiting the constraints of the model, solving the problem with the minimum sum of production and transportation time and the maximum sum of product profits as constraints, obtaining the initial solution of the population through a random search algorithm, and setting irrelevant solutions in the initial solution as non-dominated solutions; optimizing the solution through the optimization coefficient. The solution adopts some ideas of the differential evolution algorithm, combines the characteristics of discrete optimization and multi-objective optimization, and proposes an optimization coefficient with the characteristics of good convergence and simple implementation. When various constraints are met, the transportation and production plans are automatically calculated and generated, thereby achieving a balance between production time and profit, and providing managers with comprehensive decision support, which is no longer limited to transportation efficiency.

Description

Technical Field

[0001] The present invention relates to the field of cold chain transportation technology, and in particular to a method, device and storage medium for solving cold chain transportation optimization with processing time. Background Art

[0002] Currently, the market primarily uses single-temperature refrigerated trucks to transport cold chain goods, with a few organizations employing multi-temperature refrigerated trucks. Multi-temperature refrigerated trucks divide the vehicle compartment into multiple temperature zones, enabling the simultaneous transport of refrigerated, frozen, or insulated goods. System optimization is more complex than for single-temperature cold chain transport, typically requiring the design of the shortest transport route while maintaining the loading capacity of each temperature zone. In some scenarios, post-transport product production strategies and production schedules may also need to be considered.

[0003] Cold chain transportation system optimization can be viewed as a variant of the vehicle routing problem, a common combinatorial optimization problem with fruitful research results in both academia and industry. In recent years, research on cold chain transportation system optimization has gradually increased, but much of it has focused on optimizing cold storage multi-temperature distribution routes, centered around equipment such as cold storage cabinets (boxes). Research on vehicle routing optimization with processing times is very limited, currently focusing primarily on the Traveling Salesman Problem (TSPJ) with job-times. There is very little research on cold chain transportation, and this research often focuses solely on transportation efficiency while ignoring considerations of other dimensions. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to solve the problem in the prior art that only the route of the cold chain transportation process can be planned, while ignoring the important factor of profit.

[0005] According to a first aspect of an embodiment of the present invention, a method for optimizing cold chain transportation with processing time is provided, comprising:

[0006] Obtain data on the transportation of raw materials to processing sites by multi-temperature zone vehicles;

[0007] Establish a route optimization model for transporting raw materials to processing points using vehicles in multiple temperature zones, and determine the objective function, decision variables, and related constraints of the route optimization model;

[0008] Input relevant data into the route optimization model, output multiple solutions, and represent each solution with a vector;

[0009] The output multiple solutions are grouped into a population, and the initial solution of the population is obtained through a random search algorithm. The irrelevant solutions in the initial solution are set as non-dominated solutions.

[0010] Set the iteration variable, perform iterative calculation on the non-dominated solution, obtain the feasible solution of the iterated non-dominated solution, and group all feasible solutions into a feasible solution set;

[0011] Randomly select two feasible solutions from the set of feasible solutions and determine the optimization coefficient through the two feasible solutions;

[0012] Optimize the transportation route plan and product plan of two feasible solutions through optimization coefficients;

[0013] Compare the elements in the feasible solution set with the optimized feasible solution, and update the feasible solution set based on the comparison results;

[0014] Repeat the above steps until all elements in the feasible solution set have been compared, and one or more independent solutions with the smallest sum of production and transportation time and the largest sum of product profits are obtained.

[0015] Preferably,

[0016] The objective function includes: a first objective function and a second objective function;

[0017] The first objective function takes minimizing the sum of production and transportation time as the optimization goal;

[0018] The second objective function takes maximizing the sum of product profits as the optimization goal.

[0019] Preferably,

[0020] The relevant constraints include: the number of vehicles visiting all processing points, the starting point constraint of the vehicle, the vehicle constraint of the processing point, the product constraint of the processing point, the decision variable constraint, the time continuity constraint and the vehicle loading capacity constraint.

[0021] Preferably,

[0022] Randomly selecting two feasible solutions from the set of feasible solutions and determining the optimization coefficient through the two feasible solutions includes:

[0023] Randomly select two feasible solutions from the feasible solution set, and calculate the difference between the sum of production and transportation time and the sum of product profits respectively through the two feasible solutions;

[0024] The optimization coefficient is obtained based on the difference between the sum of production and transportation time and the change in the number of feasible solutions in the feasible solution set during the first u iterations. ;

[0025] The optimization coefficient is obtained based on the difference in the sum of product profits and the degree of change in the number of feasible solutions in the feasible solution set during the first u iterations. .

[0026] Preferably,

[0027] The optimization of the transport route scheme and product scheme of the two feasible solutions by optimizing the coefficients includes:

[0028] Select a feasible solution for the transportation route, randomly select a node to be removed, and then randomly select a node whose distance to the node to be removed is less than the preset distance as the replacement node. Delete the node to be removed from the original transportation route and use the node to be removed as the neighboring node of the replacement node in the new route. Repeat this process for the number of repetitions. ;

[0029] Select any feasible solution node and change the type of processed product according to the product constraints of the processing point, and repeat the process for the number of repetitions. .

[0030] Preferably,

[0031] The comparing the elements in the feasible solution set using the optimized feasible solution and updating the feasible solution set using the comparison result includes:

[0032] The optimized feasible solution is compared with all elements in the feasible solution set one by one. If the optimized feasible solution is better than an element in the feasible solution set, the element is deleted from the feasible solution set; if the optimized feasible solution is not correlated with any element in the feasible solution set, the optimized feasible solution is added to the feasible solution set.

[0033] Preferably, it also includes:

[0034] If the number of elements in the feasible solution set is greater than a preset threshold, the crowding distance of each feasible solution is calculated, the crowding distances of the feasible solutions are ranked in ascending order, and the top X feasible solutions are deleted.

[0035] Preferably,

[0036] The unrelated solutions include:

[0037] If there is no phenomenon that plan S1 dominates plan S2 or plan S2 dominates plan S1, then plan S1 and plan S2 are said to be unrelated.

[0038] According to a second aspect of an embodiment of the present invention, a device for optimizing cold chain transportation with processing time is provided, comprising:

[0039] Data acquisition module: used to obtain relevant data on the transportation of raw materials to processing points by multi-temperature zone vehicles;

[0040] Model building module: used to establish a route optimization model for multi-temperature zone vehicles to transport raw materials to processing points, and determine the objective function, decision variables and related constraints of the route optimization model;

[0041] Output module: used to input relevant data into the route optimization model and output multiple solutions, each of which is represented by a vector;

[0042] Non-dominated solution acquisition module: used to group the output multiple solutions into a population, obtain the initial solution of the population through a random search algorithm, and set the irrelevant solutions in the initial solution as non-dominated solutions;

[0043] Feasible solution set acquisition module: used to set iteration variables, perform iterative calculations on non-dominated solutions, obtain feasible solutions of the iterated non-dominated solutions, and group all feasible solutions into a feasible solution set;

[0044] Optimization coefficient acquisition module: used to randomly select two feasible solutions from the set of feasible solutions and determine the optimization coefficient through the two feasible solutions;

[0045] Optimization module: used to optimize the transportation route plan and product plan of two feasible solutions through optimization coefficients;

[0046] Update module: used to compare the elements in the feasible solution set with the optimized feasible solution, and update the feasible solution set based on the comparison results;

[0047] Result acquisition module: used to repeat the above steps until all elements in the feasible solution set have been compared, and one or more independent solutions with the minimum sum of production and transportation time and the maximum sum of product profits are obtained.

[0048] According to a third aspect of an embodiment of the present invention, a storage medium is provided, wherein the storage medium stores a computer program, and when the computer program is executed by a main controller, each step in the above method is implemented.

[0049] The technical solutions provided by the embodiments of the present invention may have the following beneficial effects:

[0050] This application establishes a route optimization model and limits the constraints on the model. It solves the problem with the constraints of minimizing the sum of production and transportation time and maximizing the sum of product profits. The initial solution of the population is obtained through a random search algorithm, and irrelevant solutions in the initial solution are set as non-dominated solutions. The solution is optimized through the optimization coefficient. This solution adopts some ideas of the differential evolution algorithm, combines the characteristics of discrete optimization and multi-objective optimization, and proposes an optimization coefficient with the characteristics of good convergence and simple implementation. When various constraints are met, the transportation and production plans are automatically calculated and generated, thereby achieving a balance between production time and profit, providing managers with comprehensive decision-making support, no longer limited to transportation efficiency.

[0051] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0053] Figure 1 is a flow chart illustrating a method for optimizing cold chain transportation with processing time according to an exemplary embodiment;

[0054] Figure 2 is a schematic diagram showing an optimization process according to an exemplary embodiment;

[0055] Figure 3 is a system schematic diagram of a device for solving cold chain transportation optimization with processing time according to another exemplary embodiment;

[0056] In the accompanying figure: 1-data acquisition module, 2-model construction module, 3-output module, 4-non-dominated solution acquisition module, 5-feasible solution set acquisition module, 6-optimization coefficient acquisition module, 7-optimization module, 8-update module, 9-result acquisition module. DETAILED DESCRIPTION

[0057] Exemplary embodiments will be described in detail herein, examples of which are illustrated in the accompanying drawings. In the following description, when referring to the drawings, like numbers in different figures represent like or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present invention, as detailed in the appended claims.

[0058] Example 1

[0059] Figure 1FIG. 1 is a flow chart of a method for optimizing cold chain transportation with processing time according to an exemplary embodiment. Figure 1 As shown, the method includes:

[0060] S1, obtain relevant data on multi-temperature zone vehicles transporting raw materials to processing sites;

[0061] S2, establish a route optimization model for transporting raw materials to processing points using vehicles in multiple temperature zones, and determine the objective function, decision variables, and related constraints of the route optimization model;

[0062] S3, inputs relevant data into the route optimization model, outputs multiple solutions, and represents each solution with a vector;

[0063] S4, grouping the output multiple solutions into a population, obtaining the initial solution of the population through a random search algorithm, and setting the irrelevant solutions in the initial solution as non-dominated solutions;

[0064] S5, set the iteration variable, perform iterative calculation on the non-dominated solution, obtain the feasible solution of the iterated non-dominated solution, and group all feasible solutions into a feasible solution set;

[0065] S6, randomly selecting two feasible solutions from the set of feasible solutions, and determining the optimization coefficient through the two feasible solutions;

[0066] S7, optimizing the transportation route scheme and product scheme of the two feasible solutions by optimizing the coefficients;

[0067] S8, comparing the elements in the feasible solution set with the optimized feasible solution, and updating the feasible solution set based on the comparison result;

[0068] S9, repeat the above steps until all elements in the feasible solution set have been compared, and one or more independent solutions with the minimum sum of production and transportation time and the maximum sum of product profits are obtained;

[0069] It is understandable that this application obtains the relevant data of the processing point location, multi-temperature zone refrigerated truck, and production products, including: the number of processing points N, the distance l between processing point i and processing point j ij , the number of product categories Q, the average vehicle speed Cv, the number of raw material types divided by temperature zones T, and the profit of product p is r p , the quantity of raw materials s required for product p is , the quantity of product p required by processing point i The parameters involved in multi-temperature zone refrigerated trucks include: the number of refrigerated trucks F, the maximum amount of raw materials that can be loaded in the s temperature zone of the refrigerated truck k, ; Time matrix for production of products at processing points and profit matrix , where e ip and r ip They represent the time and profit of processing point i producing product p respectively; then establish a route optimization model for multi-temperature zone refrigerated trucks to transport raw materials to processing points, including multi-objective functions and related constraints; determine the decision variables of the optimization model , =1 means that the refrigerated truck k passes through processing point i and processing point j in sequence, otherwise =0; =1 means that processing point i is served by refrigerated truck k, otherwise =0; =1 means that processing point i produces product p, otherwise = 0. Let Cmax = max{ }, represents the time spent on the node with the longest transportation and processing time, Indicates the time when the vehicle arrives at processing point i; the above data is input into the route optimization model, and multiple solutions are output. Each solution is represented by a vector, for example, the g-th solution S g for{( )}, indicating that the time it takes for vehicle k to arrive at processing point i is , the products produced by processing point i are p1 and p2; the output solutions are organized into populations and the population size is determined , using random search algorithm to obtain Initial feasible solutions, set the irrelevant solutions as non-dominated solutions; set the iteration variable ir, let |A h | represents the number of feasible solutions of non-dominated solutions in the hth iteration process, and all feasible solutions are grouped into a feasible solution set; two solutions s are selected from the feasible solution set a and s b , calculate the difference of objective function i = | f i (s a ) f i (s b ) |;Determine the optimization coefficient based on the difference ; By optimizing the coefficients of s a and s bThe transportation route plan and product plan are optimized to obtain the optimized feasible solution. The feasible solution set is updated through the optimized feasible solution. The above steps are repeated until all elements in the feasible solution set have been compared, and then the iterative variable is increased by 1. The above steps are repeated. When ir reaches Hmax, the algorithm stops; one or more independent plans with the minimum sum of production and transportation time and the maximum sum of product profits are obtained; the above plan adopts some ideas of the differential evolution algorithm, combines the characteristics of discrete optimization and multi-objective optimization, and proposes an optimization coefficient. It has the characteristics of good convergence and simple implementation. Under the condition of meeting various constraints, it automatically calculates and generates transportation and production plans, thereby achieving a balance between production time and profit, providing managers with comprehensive decision support, no longer limited to transportation efficiency.

[0070] Preferably,

[0071] The objective function includes: a first objective function and a second objective function;

[0072] The first objective function takes minimizing the sum of production and transportation time as the optimization goal;

[0073] The second objective function takes maximizing the sum of product profits as the optimization goal;

[0074] It can be understood that the first objective function is to minimize the sum of production and transportation time, and the formula is as follows:

[0075]

[0076] The second objective function takes maximizing the sum of product profits as the optimization goal, and the formula is as follows:

[0077] For the convenience of later calculation, it is transformed into:

[0078]

[0079] Preferably,

[0080] The relevant constraints include: the number of vehicles visiting all processing points, the starting point of the vehicle, the vehicle at the processing point, the product produced at the processing point, the decision variable, the time continuity, and the vehicle loading capacity.

[0081] It is understandable that for the completeness and effectiveness of the model, the following constraints need to be made:

[0082] The vehicle visits all processing points only once:

[0083]

[0084] All vehicles depart from the central warehouse and eventually return to:

[0085]

[0086] Each processing point can only be served by the same vehicle:

[0087]

[0088] Each processing point produces G products:

[0089]

[0090] Constraints between decision variables: ;

[0091] Time continuity requirements: ;

[0092] Loading capacity constraints for refrigerated trucks:

[0093]

[0094] Preferably,

[0095] Randomly selecting two feasible solutions from the set of feasible solutions and determining the optimization coefficient through the two feasible solutions includes:

[0096] Randomly select two feasible solutions from the feasible solution set, and calculate the difference between the sum of production and transportation time and the sum of product profits respectively through the two feasible solutions;

[0097] The optimization coefficient is obtained based on the difference between the sum of production and transportation time and the change in the number of feasible solutions in the feasible solution set during the first u iterations. ;

[0098] The optimization coefficient is obtained based on the difference in the sum of product profits and the degree of change in the number of feasible solutions in the feasible solution set during the first u iterations. ;

[0099] It can be understood that since there are two objective functions, each feasible solution includes the solution with the minimum sum of production and transportation time and the solution with the maximum sum of product profits. Therefore, when calculating the difference of the objective function, two differences are included, one is the time difference and the other is the profit difference. Through the formula Get solutions separately a and s b After the two differences, set the optimization coefficients respectively , ,in It represents the degree of change in the number of feasible solutions during the first u iterations. The formula is: , as well as is a constant, and the specific value is set by the decision maker. By bringing the obtained time difference and profit difference into the formula, we can get as well as .

[0100] Preferably,

[0101] The optimization of the transport route scheme and product scheme of the two feasible solutions by optimizing the coefficients includes:

[0102] Select a feasible solution for the transportation route, randomly select a node to be removed, and then randomly select a node whose distance to the node to be removed is less than the preset distance as the replacement node. Delete the node to be removed from the original transportation route and use the node to be removed as the neighboring node of the replacement node in the new route. Repeat this process for the number of repetitions. ;

[0103] Select any feasible solution node and change the type of processed product according to the product constraints of the processing point, and repeat the process for the number of repetitions. ;

[0104] It is understandable that Figure 2 As shown in the figure, first a node to be removed (node ​​5) is randomly selected, then a node (node ​​8) is randomly selected from the neighboring nodes of node 5 (all nodes with a distance less than R), and then node 5 is deleted from the original route and becomes the neighboring node of node 8 in the new route. The number of times this process is repeated is [ ]( rounding);

[0105] Randomly select a node and change the type of processed product under the constraint that each processing point produces G types of products. The number of times this process is repeated is [ ],( (rounded).

[0106] Preferably,

[0107] The comparing the elements in the feasible solution set using the optimized feasible solution and updating the feasible solution set using the comparison result includes:

[0108] Compare the optimized feasible solution with all elements in the feasible solution set one by one. If the optimized feasible solution is better than an element in the feasible solution set, delete the element from the feasible solution set. If the optimized feasible solution is not correlated with any element in the feasible solution set, add the optimized feasible solution to the feasible solution set.

[0109] It can be understood that the optimized feasible solution is compared with the elements in the feasible solution set one by one. If the optimized feasible solution is better than an element in the feasible solution set, the element will be deleted from the feasible solution set. This step prevents the feasible solution set from having an element that is not completely better than another element in terms of profit and time, and also makes the elements in the feasible solution set show an overall decreasing trend; if the optimized feasible solution is not related to any element in the feasible solution set, the optimized feasible solution will be added to the feasible solution set.

[0110] Preferably, it also includes:

[0111] If the number of elements in the feasible solution set is greater than a preset threshold, the crowding distance of each feasible solution is calculated, the crowding distances of the feasible solutions are ranked in ascending order, and the top X feasible solutions are deleted;

[0112] It is understandable that when |A h |Exceeds threshold value|A| max When , calculate the crowding distance of each feasible solution, rank the crowding distances, which can be ranked in ascending order, and then delete the top ranked [|A| max / 4] number of solutions, if they are ordered from the smallest to the smallest in terms of crowding distance, then delete the last [|A| max / 4] number of solutions. When the number of feasible solutions in the feasible solution set reaches a preset threshold, the feasible solutions with low crowding distance are deleted. Otherwise, the time consumed in each round of iteration will gradually increase, making the algorithm infeasible.

[0113] Preferably,

[0114] The unrelated solutions include:

[0115] If there is no phenomenon that plan S1 dominates plan S2 or plan S2 dominates plan S1, then plan S1 and plan S2 are said to be irrelevant;

[0116] It is understandable that Pareto Dominance:

[0117] Definition 1: Scheme s 1 dominates s 2 (s 1 s 2), if and only if:

[0118]

[0119] Definition 2: If there is no phenomenon that plan s 1 dominates s 2 or plan s 2 dominates s 1, then plans s 1 and s 2 are said to be irrelevant;

[0120] Definition 3: Pareto Optimum: Solution s1 is optimal if and only if s 2, make s2 s 1;

[0121] Definition 4: Pareto Front: The set of Pareto optimal solutions Ps={ s 1|

[0122] s 2 s 1};

[0123] In layman's terms, unrelated solutions mean that no solution is better or worse than another solution in terms of time and profit. Two solutions that have advantages and disadvantages in terms of time and profit are unrelated.

[0124] The following simulation example illustrates the above calculation process:

[0125] There are 30 outlets, each of which processes 2 products from 5 types. Each product requires 6kg of ambient temperature raw materials and 4kg of refrigerated raw materials. The number of products required by each outlet ranges from 1 to 8. There are 2 multi-temperature zone refrigerated trucks available for transportation. Each refrigerated truck compartment is divided into a refrigerated area and an ambient temperature area. The maximum load capacity of each temperature area is 500kg, and the average speed is 30km / h.

[0126]

[0127] Determine the parameters of the algorithm, let =100、u=5、H max =104, =1.2, =0.03, |A| max =30. The initial solution is obtained by random search algorithm as shown in Table 1. max After iterations, the feasible solutions are shown in Table 2. It can be seen that the quality of the target value has been significantly improved: Table 1:

[0128]

[0129] Table 2:

[0130]

[0131] Example 2

[0132] Figure 3 2 is a system diagram illustrating a device for optimizing cold chain transportation with processing time according to another exemplary embodiment, including:

[0133] Data acquisition module 1: used to obtain relevant data of multi-temperature zone vehicles transporting raw materials to processing points;

[0134] Model Building Module 2: Used to establish a route optimization model for transporting raw materials to processing points using vehicles in multiple temperature zones, and to determine the objective function, decision variables, and related constraints of the route optimization model;

[0135] Output module 3: used to input relevant data into the route optimization model and output multiple solutions, each of which is represented by a vector;

[0136] Non-dominated solution acquisition module 4: used to group the output multiple solutions into a population, obtain the initial solution of the population through a random search algorithm, and set the irrelevant solutions in the initial solution as non-dominated solutions;

[0137] Feasible solution set acquisition module 5: used to set iteration variables, perform iterative calculations on non-dominated solutions, obtain feasible solutions of the iterated non-dominated solutions, and group all feasible solutions into a feasible solution set;

[0138] Optimization coefficient acquisition module 6: used to randomly select two feasible solutions from the set of feasible solutions and determine the optimization coefficient based on the two feasible solutions;

[0139] Optimization module 7: used to optimize the transportation route plan and product plan of two feasible solutions through optimization coefficients;

[0140] Update module 8: used to compare the elements in the feasible solution set with the optimized feasible solution, and update the feasible solution set according to the comparison result;

[0141] Result acquisition module 9: used to repeat the above steps until all elements in the feasible solution set have been compared, and one or more independent solutions with the minimum sum of production and transportation time and the maximum sum of product profits are obtained;

[0142] It can be understood that the present application obtains relevant data of multi-temperature zone vehicles transporting raw materials to processing points through the data acquisition module 1; establishes a route optimization model for multi-temperature zone vehicles transporting raw materials to processing points through the model construction module 2, and determines the objective function, decision variables and related constraints of the route optimization model; inputs relevant data into the route optimization model through the output module 3, outputs multiple solutions, and represents each solution with a vector; organizes the output multiple solutions into a population through the non-dominated solution acquisition module 4, obtains the initial solution of the population through the random search algorithm, and sets the irrelevant solutions in the initial solution as non-dominated solutions; sets the iteration variable through the feasible solution set acquisition module 5, iterates the non-dominated solution, obtains the feasible solution of the non-dominated solution after the iteration, and organizes all feasible solutions into a feasible solution set; randomly selects two feasible solutions from the feasible solution set through the optimization coefficient acquisition module 6, and confirms the feasible solution through the two feasible solutions. The optimization coefficient is determined; the optimization module 7 optimizes the transportation route scheme and product scheme of the two feasible solutions through the optimization coefficient; the updating module 8 compares the elements in the feasible solution set through the optimized feasible solution, and updates the feasible solution set through the comparison result; the above steps are repeated through the result acquisition module 9 until all elements in the feasible solution set have been compared, and one or more independent solutions with the smallest sum of production and transportation time and the largest sum of product profits are obtained; in the above scheme, part of the idea of ​​the differential evolution algorithm is adopted, and the characteristics of discrete optimization and multi-objective optimization are combined to propose an optimization coefficient, which has the characteristics of good convergence and simple implementation. Under the condition of meeting various constraints, the transportation and production schemes are automatically calculated and generated, thereby achieving a balance between production time and profit, providing managers with comprehensive decision support, no longer limited to transportation efficiency.

[0143] Example 3:

[0144] This embodiment provides a storage medium storing a computer program. When the computer program is executed by a host controller, each step of the above method is implemented.

[0145] It is understandable that the storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0146] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0147] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are used for descriptive purposes only and should not be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" is at least two.

[0148] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention pertain.

[0149] It should be understood that various components of the present invention may be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods may be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof may be used: a discrete logic circuit having logic gate circuits for implementing logic functions on data signals, an application-specific integrated circuit having suitable combinational logic gate circuits, a programmable gate array (PGA), a field-programmable gate array (FPGA), etc.

[0150] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.

[0151] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing module, or each unit may exist physically separately, or two or more units may be integrated into a single module. The aforementioned integrated modules may be implemented in the form of hardware or in the form of software functional modules. If the integrated modules are implemented in the form of software functional modules and sold or used as independent products, they may also be stored in a computer-readable storage medium.

[0152] The storage medium mentioned above can be a read-only memory, a magnetic disk or an optical disk, etc.

[0153] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0154] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and transform the above embodiments within the scope of the present invention.

Claims

1. Solve the cold chain transportation optimization method with processing time, characterized by: The method comprises: Obtain data on the transportation of raw materials to processing sites by multi-temperature zone vehicles; Establish a route optimization model for transporting raw materials to processing points using vehicles in multiple temperature zones, and determine the objective function, decision variables, and related constraints of the route optimization model; The objective function includes: a first objective function and a second objective function; The first objective function takes minimizing the sum of production and transportation time as the optimization goal; The second objective function takes maximizing the sum of product profits as the optimization goal; The relevant constraints include: the number of vehicles visiting all processing points, the starting point of the vehicle, the vehicle at the processing point, the product produced at the processing point, the decision variable, the time continuity, and the vehicle loading capacity. The decision variables include: determining the decision variables of the optimization model , =1 means that the refrigerated truck k passes through processing point i and processing point j in sequence, otherwise =0; =1 means that processing point i is served by refrigerated truck k, otherwise =0; =1 means that processing point i produces product p, otherwise =0; Input relevant data into the route optimization model, output multiple solutions, and represent each solution with a vector; The output multiple solutions are grouped into a population, and the initial solution of the population is obtained through a random search algorithm. The irrelevant solutions in the initial solution are set as non-dominated solutions; Set the iteration variable, perform iterative calculation on the non-dominated solution, obtain the feasible solution of the iterated non-dominated solution, and group all feasible solutions into a feasible solution set; Randomly select two feasible solutions from the set of feasible solutions and determine the optimization coefficient through the two feasible solutions; Randomly selecting two feasible solutions from the set of feasible solutions and determining the optimization coefficient through the two feasible solutions includes: Randomly select two feasible solutions from the feasible solution set, and calculate the difference between the sum of production and transportation time and the sum of product profits respectively through the two feasible solutions; The optimization coefficient is obtained based on the difference between the sum of production and transportation time and the change in the number of feasible solutions in the feasible solution set during the first u iterations. ; The optimization coefficient is obtained based on the difference in the sum of product profits and the degree of change in the number of feasible solutions in the feasible solution set during the first u iterations. ; Optimize the transportation route plan and product plan of two feasible solutions through optimization coefficients; Compare the elements in the feasible solution set with the optimized feasible solution, and update the feasible solution set based on the comparison results; Repeat the above steps until all elements in the feasible solution set have been compared, and one or more independent solutions with the smallest sum of production and transportation time and the largest sum of product profits are obtained.

2. The method according to claim 1, characterized in that The optimization of the transport route scheme and product scheme of the two feasible solutions by optimizing the coefficients includes: Select a feasible solution for the transportation route, randomly select a node to be removed, and then randomly select a node whose distance to the node to be removed is less than the preset distance as the replacement node. Delete the node to be removed from the original transportation route and use the node to be removed as the neighboring node of the replacement node in the new route. Repeat this process for the number of repetitions. ; Select any feasible solution node and change the type of processed product according to the product constraints of the processing point, and repeat the process for the number of repetitions. .

3. The method according to claim 2, characterized in that The comparing the elements in the feasible solution set using the optimized feasible solution and updating the feasible solution set using the comparison result includes: The optimized feasible solution is compared with all elements in the feasible solution set one by one. If the optimized feasible solution is better than an element in the feasible solution set, the element is deleted from the feasible solution set; if the optimized feasible solution is not correlated with any element in the feasible solution set, the optimized feasible solution is added to the feasible solution set.

4. The method according to claim 3, characterized in that Also includes: If the number of elements in the feasible solution set is greater than a preset threshold, the crowding distance of each feasible solution is calculated, the crowding distances of the feasible solutions are ranked in ascending order, and the top X feasible solutions are deleted.

5. The method according to claim 4, characterized in that The unrelated solutions include: If there is no phenomenon that plan S1 dominates plan S2 or plan S2 dominates plan S1, then plan S1 and plan S2 are said to be irrelevant.

6. Solve the cold chain transportation optimization device with processing time, characterized by: The device comprises: Data acquisition module: used to obtain relevant data on the transportation of raw materials to processing points by multi-temperature zone vehicles; Model building module: used to establish a route optimization model for multi-temperature zone vehicles to transport raw materials to processing points, and determine the objective function, decision variables and related constraints of the route optimization model; The objective function includes: a first objective function and a second objective function; The first objective function takes minimizing the sum of production and transportation time as the optimization goal; The second objective function takes maximizing the sum of product profits as the optimization goal; The relevant constraints include: the number of vehicles visiting all processing points, the starting point of the vehicle, the vehicle at the processing point, the product produced at the processing point, the decision variable, the time continuity, and the vehicle loading capacity. The decision variables include: determining the decision variables of the optimization model , =1 means that the refrigerated truck k passes through processing point i and processing point j in sequence, otherwise =0; =1 means that processing point i is served by refrigerated truck k, otherwise =0; =1 means that processing point i produces product p, otherwise =0; Output module: used to input relevant data into the route optimization model and output multiple solutions, each of which is represented by a vector; Non-dominated solution acquisition module: used to group the output multiple solutions into a population, obtain the initial solution of the population through a random search algorithm, and set the irrelevant solutions in the initial solution as non-dominated solutions; Feasible solution set acquisition module: used to set iteration variables, perform iterative calculations on non-dominated solutions, obtain feasible solutions of the iterated non-dominated solutions, and group all feasible solutions into a feasible solution set; Optimization coefficient acquisition module: used to randomly select two feasible solutions from the set of feasible solutions and determine the optimization coefficient through the two feasible solutions; Randomly selecting two feasible solutions from the set of feasible solutions and determining the optimization coefficient through the two feasible solutions includes: Randomly select two feasible solutions from the feasible solution set, and calculate the difference between the sum of production and transportation time and the sum of product profits respectively through the two feasible solutions; The optimization coefficient is obtained based on the difference between the sum of production and transportation time and the change in the number of feasible solutions in the feasible solution set during the first u iterations. ; The optimization coefficient is obtained based on the difference in the sum of product profits and the degree of change in the number of feasible solutions in the feasible solution set during the first u iterations. ; Optimization module: used to optimize the transportation route plan and product plan of two feasible solutions through optimization coefficients; Update module: used to compare the elements in the feasible solution set with the optimized feasible solution, and update the feasible solution set based on the comparison results; Result acquisition module: used to repeat the above steps until all elements in the feasible solution set have been compared, and one or more independent solutions with the minimum sum of production and transportation time and the maximum sum of product profits are obtained.

7. A storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the main controller, it implements the various steps in the method for optimizing cold chain transportation with processing time as described in any one of claims 1 to 5.

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