Multi-vehicle supermarket cold-chain logistics docking path optimization method and system

Abstract

The invention discloses a path optimization method and system for docking cold-chain logistics in supermarkets of multiple vehicle types, and applies a locust optimization algorithm to cold-chain logistics path optimization, thereby solving the problem of path optimization in docking cold-chain logistics in supermarkets. According to the technical scheme, the method comprises the following steps: establishing a rural-supermarket docking model, presetting constraint conditions in a multi-vehicle-type rural-supermarket docking cold-chain logistics path optimization method, and presetting two types of cold-chain vehicles, namely a light-duty refrigerator car and a heavy-duty refrigerator car; initializing the position and the yield of the rice field, the position, the processing cost and the efficiency of each processing plant, the position of a supermarket, the rice demand quantity and the position of an urban refrigeration house; solving a multi-vehicle-type rural-super butt joint cold-chain logistics path optimization model through an improved locust optimization algorithm; and completing transportation from the rice field to the processing plant, from the processing plant to the urban refrigeration house and from the processing plant to the supermarket, and obtaining an optimal path solution of the cold chain vehicle.

Description

Technical Field

[0001] The present invention relates to the technical field of route optimization, and in particular to a route optimization method and system for connecting supermarkets with cold chain logistics of multiple vehicle types. Background Art

[0002] In the current agricultural-supermarket docking process, cold chain transportation of agricultural products such as rice is usually carried out by cold chain vehicles of a single model. For example, large cold chain vehicles are generally used, but in the section from the processing plant to the supermarket, if the demand for docking supermarkets is small, there will be a waste of vehicle cargo space, resulting in excessively high transportation costs. If a large cold chain vehicle serves multiple supermarkets at the same time, there will be some loss of cold air during the unloading period of each supermarket, which will also increase the cold chain cost. On the contrary, if all small cold chain vehicles are used, due to their capacity limitations, vehicles are sometimes required to travel back and forth between the two places. The operating cost of this model is also high, and the transportation efficiency is low.

[0003] In addition, most of the existing cold chain logistics route optimization methods do not fully consider the situation of coordinated transportation of multiple models, as well as the route optimization problems at different transportation stages (such as rice fields to processing plants, processing plants to supermarkets / cold storage, cold storage to supermarkets). There is currently a lack of effective solutions for how to flexibly select appropriate models and transportation routes according to the demand of different supermarkets to optimize transportation costs and time. Summary of the invention

[0004] A brief summary of one or more aspects is given below to provide a basic understanding of these aspects. This summary is not an exhaustive overview of all conceived aspects, and is neither intended to identify the key or critical elements of all aspects nor to define the scope of any or all aspects. Its only purpose is to give some concepts of one or more aspects in a simplified form as a prelude to a more detailed description that will be given later.

[0005] The purpose of the present invention is to solve the above-mentioned problems and provide a path optimization method and system for supermarkets connecting with cold chain logistics of multiple types of vehicles. The locust optimization algorithm is applied to the cold chain logistics path optimization, thereby solving the path optimization problem of supermarkets connecting with cold chain logistics.

[0006] The technical solution of the present invention is: the present invention discloses a route optimization method for connecting a supermarket with multiple vehicle types to cold chain logistics, the method comprising:

[0007] Step 1: Establish a supermarket docking model, preset constraints in the path optimization method problem of supermarket docking cold chain logistics with multiple models, and preset different types of cold chain vehicles;

[0008] Step 2: Initialize the location and output of agricultural product planting areas, the location, processing fees, and efficiency of each processing plant, the location of supermarkets, the demand for agricultural products, and the location of urban cold storage;

[0009] Step 3: Solve the path optimization model of supermarkets connecting cold chain logistics with multiple models by improving the locust optimization algorithm;

[0010] Step 4: Complete the transportation from the planting site to the processing plant, from the processing plant to the urban cold storage, and from the processing plant to the supermarket, and obtain the optimal route solution for the cold chain vehicles.

[0011] According to an embodiment of the route optimization method for connecting supermarkets with cold chain logistics of multiple types of vehicles of the present invention, in step one, two types of cold chain vehicles are preset: light refrigerated trucks and heavy refrigerated trucks, wherein the light refrigerated trucks serve from urban cold storage to supermarkets with less demand for agricultural products; the heavy refrigerated trucks serve from agricultural product planting areas to processing plants, from processing plants to urban cold storage, and from processing plants to supermarkets with greater demand for agricultural products.

[0012] According to an embodiment of the method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types of vehicles of the present invention, step three further comprises:

[0013] Step 1: Initialize locust population, Cmax, Cmin and maximum number of iterations;

[0014] Step 2: Use the locust optimization algorithm to establish a mathematical model for updating the location of cold chain logistics vehicles;

[0015] Step 3: A curve adaptive strategy is introduced to improve parameter c to update parameter c in the locust optimization algorithm;

[0016] Step 4: Map the distance from the planting area to the processing plant, the distance from the processing plant to the city cold storage, and the distance from the processing plant to the large supermarket in the interval [1,4];

[0017] Step 5: Update the current position of the locust;

[0018] Step 6: Adopt an improved Levy flight strategy to increase degrees of freedom and random behavior;

[0019] Step 7: Update the parameter archive by comparison, where archive is a data structure used to store non-dominated solutions, and is used to save or retrieve the non-dominated Pareto optimal solutions obtained so far, so as to continuously update and optimize these solutions in the iterative process of the algorithm;

[0020] Step 8: Determine whether the number of iterations is met. If so, the algorithm ends and outputs the optimal path. Otherwise, continue the loop from step 2.

[0021] The present invention also discloses a route optimization system for connecting supermarkets with cold chain logistics of multiple vehicle types, the system comprising:

[0022] The model building module is used to establish a supermarket docking model, preset constraints in the path optimization method problem of supermarket docking cold chain logistics with multiple vehicle models, and preset different types of cold chain vehicles;

[0023] Model parameter initialization module, used to initialize the location and output of agricultural product planting areas, the location, processing fees, and efficiency of each processing plant, the location of supermarkets, the demand for agricultural products, and the location of urban cold storage;

[0024] Locust optimization algorithm module, used to solve the path optimization model of supermarket docking cold chain logistics for multiple vehicle types by improving the locust optimization algorithm;

[0025] The model solving module is used to complete the transportation from the planting area to the processing plant, from the processing plant to the urban cold storage, and from the processing plant to the supermarket, and obtain the optimal path solution for the cold chain vehicles.

[0026] According to an embodiment of the route optimization system for connecting supermarkets with cold chain logistics of multiple types of vehicles of the present invention, two types of cold chain vehicles are preset in the model building module: light refrigerated trucks and heavy refrigerated trucks, wherein the light refrigerated trucks serve from urban cold storage to supermarkets with less demand for agricultural products; the heavy refrigerated trucks serve from agricultural product planting areas to processing plants, from processing plants to urban cold storage, and from processing plants to supermarkets with greater demand for agricultural products.

[0027] According to an embodiment of the route optimization system for supermarkets with multiple vehicle types docking cold chain logistics of the present invention, the locust optimization algorithm module is further configured to perform the following processing:

[0028] Step 1: Initialize locust population, Cmax, Cmin and maximum number of iterations;

[0029] Step 2: Use the locust optimization algorithm to establish a mathematical model for updating the location of cold chain logistics vehicles;

[0030] Step 3: A curve adaptive strategy is introduced to improve parameter c to update parameter c in the locust optimization algorithm;

[0031] Step 4: Map the distance from the planting area to the processing plant, the distance from the processing plant to the city cold storage, and the distance from the processing plant to the large supermarket in the interval [1,4];

[0032] Step 5: Update the current position of the locust;

[0033] Step 6: Adopt an improved Levy flight strategy to increase degrees of freedom and random behavior;

[0034] Step 7: Update the parameter archive by comparison, where archive is a data structure used to store non-dominated solutions, and is used to save or retrieve the non-dominated Pareto optimal solutions obtained so far, so as to continuously update and optimize these solutions in the iterative process of the algorithm;

[0035] Step 8: Determine whether the number of iterations is met. If so, the algorithm ends and outputs the optimal path. Otherwise, continue the loop from step 2.

[0036] The present invention also discloses a computer system for optimizing the path of cold-chain logistics for supermarkets with multiple types of vehicles, comprising a memory, a processor, and program instructions stored in the memory for execution by the processor, wherein the processor executes the program instructions to implement the steps of the method for optimizing the path of cold-chain logistics for supermarkets with multiple types of vehicles as described above.

[0037] The present invention also discloses a computer-readable storage medium for optimizing the path of cold-chain logistics for supermarkets with multiple vehicle types, which stores program instructions executable by a processor to implement the steps of the path optimization method for cold-chain logistics for supermarkets with multiple vehicle types as described above.

[0038] The present invention also discloses a computer program product, including a computer program, which, when executed by a processor, implements the steps of the path optimization method for connecting supermarkets with cold chain logistics of multiple vehicle types as described above.

[0039] Compared with the prior art, the present invention has the following beneficial effects: the Grasshopper Optimization Algorithm (GOA) is an emerging meta-inspiration algorithm, which is proposed based on the foraging behavior of locust colonies, and a mathematical model is proposed by studying the behavior of locust colonies. The life cycle of locusts is mainly divided into two stages: larvae and adults. Larvae move slowly and move in a small range, while adults move long distances and quickly. According to this characteristic of locusts, the naturally inspired locust optimization algorithm is divided into two stages: local development in the larval period and global exploration in the adult period. The behavior of locusts is affected by locust colonies, gravity, wind and other effects. The application of the locust optimization algorithm in the path optimization problem meets the requirements of the actual cold chain logistics of agricultural supermarket docking. Therefore, the present invention uses the locust optimization algorithm to design a path optimization method for supermarket docking cold chain logistics of multiple models.

[0040] In the present invention, a multi-section and multi-vehicle transportation mode is proposed for the process of connecting agricultural products such as rice to supermarkets, and the constraints of the problem are determined. The position value coordinates of rice production areas, processing plants, and supermarkets, as well as the production of production areas, processing fees and efficiency indicators of processing plants, and supermarket rice demand data are obtained. A multi-vehicle cold chain logistics path optimization model for connecting agricultural products to supermarkets is constructed with the goal of minimizing the sum of transportation costs, fuel consumption costs of cold chain vehicles, toll costs, and processing costs. The degree of freedom is increased by introducing Levy flight, and an adaptive curve is designed to optimize the algorithm model. Finally, the Pareto optimal solution is obtained. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The above features and advantages of the present invention can be better understood after reading the detailed description of the embodiments of the present disclosure in conjunction with the following drawings. In the drawings, the components are not necessarily drawn to scale, and components with similar related properties or features may have the same or similar reference numerals.

[0042] Figure 1 A flow chart of an embodiment of the method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types according to the present invention is shown.

[0043] Figure 2 A schematic diagram showing an example of a cold chain logistics path optimization path.

[0044] Figure 3 A schematic diagram of an embodiment of a route optimization system for connecting supermarkets with cold chain logistics of multiple vehicle types according to the present invention is shown. DETAILED DESCRIPTION

[0045] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. Note that the aspects described below in conjunction with the accompanying drawings and specific embodiments are only exemplary and should not be construed as limiting the scope of protection of the present invention in any way.

[0046] Figure 1 The flow chart of an embodiment of the method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types according to the present invention is shown. Figure 1 , the implementation steps of the method of this embodiment are described in detail as follows.

[0047] Step 1: Establish a path optimization model for supermarkets with multiple models to connect to cold chain logistics, preset constraints in the path optimization method problem for supermarkets with multiple models to connect to cold chain logistics, and preset two types of cold chain vehicles: light refrigerated trucks and heavy refrigerated trucks. Light refrigerated trucks serve urban cold storage to supermarkets with less demand for agricultural products (such as rice); heavy refrigerated trucks serve agricultural product planting areas (such as rice fields) to processing plants, processing plants to urban cold storage, and processing plants to supermarkets with greater demand for agricultural products.

[0048] The process of establishing the route optimization model of supermarkets with multiple vehicle types docking cold chain logistics in step one further includes the following processing steps.

[0049] According to the actual situation, a supermarket docking model is established. The geographical coordinates of the planting area center in the model are: A0; the geographical coordinates of the processing plant are: X1, X2, ..., Xm; the geographical coordinates of the cold storage are: Y1, Y2, ..., Yn; the geographical coordinates of the supermarket are Z1, Z2, ..., Zk;

[0050] First, the total distance is divided into three parts. The first is from the planting site to the processing plant, the second is from the processing plant to the supermarket / cold storage, and the third is from the cold storage to the supermarket. Each section of the path is independent and does not interfere with each other. The total cost from the planting site to the processing plant is F1(x), where x indicates the processing plant code; the total cost from the processing plant to the cold storage / supermarket is F2(x,y), where x indicates the processing plant code and y indicates the supermarket / cold storage code; the cost from the cold storage to the supermarket is F3(y,z); where x indicates the cold storage code and y indicates the supermarket code. The relationship between the planting site and the processing plant is one-to-many, the relationship between the processing plant and the supermarket is one-to-many, the relationship between the processing plant and the cold storage is many-to-many, and the relationship between the cold storage and the supermarket is one-to-many. Finally, there is a one-to-many relationship between the planting site and the processing plant at both ends of the path.

[0051] The distances between each point on the way from the planting site to each processing plant are stored in D1(x), where x indicates the processing plant code; the distances between each point on the way from the processing plant to the supermarket / cold storage are stored in D2(x, y), where x indicates the processing plant code and y indicates the supermarket / cold storage code; the distances between each point on the way from the cold storage to the supermarket are stored in D3(x, y), where x indicates the cold storage code and y indicates the supermarket code.

[0052] The charging standards of each processing plant are stored in E(x), where x indicates the processing plant code, and the charging standards of each cold storage are stored in E1(x), where x indicates the cold storage code.

[0053] When calculating the optimal path for each section, first input the path arc vertex space V(x, y) between the initial position and the final position, and then find the arc index sequence of a path from the initial position SP(x, y) to the final position TP(x, y). In the arc space, the position of the selected vertex shows the index of the next vertex Vn(x, y), n∈[1,N+1], where n is the number of segments of the path, indicating that the path from the starting position to the target position is divided into n segments, each representing a small segment of the path. N is the total number of vertices on the path, which may represent the number of intermediate points passed in the path. The final index represents the target position V in the environment. n+1 (x, y) = TP(), calculate the initial position SP 0 and target position TP n+1 The path length between , the function used to calculate the path length is as follows:

[0054]

[0055] Where: l(p) is the length of the path; d(SP i ,TP i+1 ) is SP i and TP i+1 The distance between them.i ,TP i+1 The distance between the initial position and the target position is divided into n segments. The distance between each point on the way from the rice field to each processing plant is stored in D1(x), where x indicates the processing plant code, and d(SP i ,TP i+1 )=D1 mn Among them, D1 mn is the distance from the middle point m to n on the path from the rice field to the processing plant. The distances between the processing plant and the supermarket / cold storage are stored in D2(x, y), where x indicates the code of the processing plant, y indicates the code of the supermarket / cold storage, and d(SP i ,TP i+1 )=D2 mn .D2 mn is the distance from the middle point m to n on the path from the processing plant to the supermarket / cold storage. The distances of each point on the way from the cold storage to the supermarket are stored in D3(x, y), where x indicates the code of the cold storage and y indicates the code of the supermarket. d(SP i ,TP i+1 )=D3 mn D3 mn It is the distance from the middle point m to n on the path from the cold storage to the supermarket.

[0056] Step 2: Initialize the location and output of agricultural product planting areas (such as rice fields), the location, processing fees, and efficiency of each processing plant, the location of supermarkets, the demand for agricultural products (such as rice), and the location of urban cold storage.

[0057] Step 3: Solve the path optimization model of supermarkets connecting multiple types of vehicles to cold chain logistics by improving the locust optimization algorithm.

[0058] Further, the steps to improve the locust optimization algorithm are:

[0059] Step 1: Initialize the locust population, Cmax, Cmin and maximum number of iterations.

[0060] The locust population represents all candidate solutions in the algorithm. Each locust position corresponds to a potential solution, and the size of the population (i.e. the number of locusts) is an important parameter that affects the diversity and search ability of the algorithm.

[0061] Cmax and Cmin are control parameters in the Grasshopper Optimization Algorithm (GOA), which are used to adjust the global search and local search capabilities of the algorithm. These parameters decrease linearly with the increase in the number of iterations, thereby enhancing the global search capability in the early stage of the algorithm and enhancing the local search capability in the later stage.

[0062] The maximum number of iterations (Max_iter) is the total number of iterations that the algorithm runs, which is used to control the termination condition of the algorithm. This parameter determines when the algorithm stops searching for the optimal solution. Usually, the maximum number of iterations is set according to the complexity of the specific problem and the required accuracy.

[0063] Step 2: Use the locust optimization algorithm to establish a path optimization model for supermarkets with multiple vehicle types connecting to cold chain logistics.

[0064] The process of establishing a path optimization model for supermarkets connecting multiple vehicle types to cold chain logistics using the locust optimization algorithm further includes the following steps.

[0065] The cold chain logistics path is described by the following mathematical model:

[0066] X i =S i +G i +A i (2)

[0067] Where X i is the position of the i-th vehicle in cold chain logistics; S i The i-th vehicle of cold chain logistics is affected by other cold chain logistics vehicles; G i is the impact of gravity on the i-th vehicle in cold chain logistics; A i is the impact of wind force on the i-th vehicle in cold chain logistics.

[0068] The following mathematical model is used to explain the update behavior in the cold chain logistics path optimization method:

[0069] 1) S i The calculation formula is shown in formula (3):

[0070]

[0071] in: It represents the unit vector from the i-th vehicle in the cold chain logistics to the j-th vehicle in the cold chain logistics;

[0072] d ij It represents the distance between the i-th cold chain logistics vehicle and the j-th cold chain logistics vehicle;

[0073] The s() function is defined as the influence function of the interaction force of cold chain logistics vehicles on other cold chain logistics vehicles. The formula is as follows:

[0074] s t =fe -r / l -e -r (4)

[0075] Where: f is the attraction intensity parameter; l is the attraction range parameter, r represents the distance between cold chain logistics vehicles, which is the relative distance between vehicle i and vehicle j. e is the base of the natural logarithm. r is the interaction force between vehicle i and vehicle j. The interaction forces between cold chain logistics vehicles include attraction and repulsion;

[0076] When r When r > 0, cold chain logistics vehicles attract each other, and the value range of r is called the attraction zone;

[0077] When r = 0, there is neither attraction nor repulsion between cold chain logistics vehicles, and the value range of r is the comfort zone. When the value of r is too large, s r ≈0, but the value range of r is not in the comfort zone at this time;

[0078] When r When r < 0, cold chain logistics vehicles repel each other, and the value range of r at this time is called the exclusion zone;

[0079] N represents the total number of cold chain logistics vehicles.

[0080] 2) G i The value is calculated as follows:

[0081]

[0082] Where: g is the gravitational constant; is a unit vector pointing toward the center of the Earth.

[0083] 3) A i The calculation formula is as follows:

[0084]

[0085] Where: u is a floating constant; is a unit vector with the same direction as the wind direction;

[0086] Since the cold chain logistics vehicles reach the comfort zone very quickly, they cannot converge to the target position. Therefore, the path optimization model of supermarkets with multiple models docking cold chain logistics cannot be directly used to solve the optimization problem. By introducing parameters to distinguish the optimization at different stages, the mathematical model for updating the position of cold chain logistics vehicles is as follows:

[0087]

[0088] in: is the new position of the i-th cold chain logistics vehicle in the d-th dimension, that is, the updated position of the vehicle after iteration, denotes the current position of the i-th and j-th cold chain logistics vehicles in the d-th dimension, and denotes the current position of the i-th cold chain logistics vehicle in the d-th dimension. ij represents the distance between the i-th and j-th cold chain logistics vehicles. i ,x j Represents the target position of the i-th and j-th cold chain logistics vehicles or the center point of the target area.

[0089] ub d lb d are the upper and lower boundaries of the d-dimensional variable of the i-th cold chain logistics vehicle, is the optimal solution in the current d-dimensional space. The parameter c in the curly brackets is the decreasing coefficient that reduces the size of the comfort zone, the exclusion zone, and the attraction zone. The parameter c outside the curly brackets reduces the movement of the cold chain logistics vehicle near the target value as the number of iterations increases. Under the joint action of the two parameters c inside and outside the brackets, as the number of iterations increases, the global search range is reduced, but the local search around the target is increased. Represents the influence function of the interaction force on cold chain logistics vehicles from other cold chain logistics vehicles.

[0090] Step 3: A curve adaptive strategy is introduced to improve parameter c to update parameter c in the locust optimization algorithm.

[0091] The enhanced adaptive strategy is introduced to improve the parameter c. The formula for updating the parameter c in the locust optimization algorithm is as follows:

[0092]

[0093] Among them, cmin and cmax are the minimum and maximum values ​​of parameter c, t is the current iteration number, and T is the maximum iteration number. The change of parameter c from large to small corresponds to the transition of the algorithm from global search to local search. Strengthening self-adaptation helps to continuously search globally at the beginning and search locally in the later stage, and increase the search range in the early stage and reduce the search range in the later stage, so that the algorithm does not fall into the local optimum, which greatly improves the convergence speed of the algorithm.

[0094] Step 4: Map the distance from the planting area to the processing plant, the distance from the processing plant to the urban cold storage, and the distance from the processing plant to the large supermarket in the interval [1,4].

[0095] Step 5: Update the locust's current position.

[0096] Step 6: Adopt an improved Levy flight strategy with increased degrees of freedom and random behavior.

[0097] Step 6 further includes the following processing steps.

[0098] Sure Later, Levi flight was introduced to increase the degree of freedom:

[0099]

[0100] in,

[0101]

[0102] Γ(x)=(x-1)! (12)

[0103] Among them, levy(d) is the step size of the Levy flight process, which is used to adjust the position of the cold chain logistics vehicles, increase the randomness and freedom of the search space, and avoid the algorithm from falling into the local optimum. 1 , r 2 Generated from a standard normal distribution or uniform distribution. Used to increase the diversity of solutions. σ is the scale factor, controlling the distribution and amplitude of the step size. β controls the tail behavior of the Lévy flight, i.e. the scale and frequency of the jumps. Γ(x) is the Gamma function, which can be simplified to Γ(x) = (x-1)! for positive integers n.

[0104] Step 7: Update the archive by comparing.

[0105] Step 7 further includes the following process of creating an archive and updating it through the roulette wheel method.

[0106] Add an external storage archive to temporarily store non-dominated solutions. Archieve is a data structure used to store non-dominated solutions (Pareto optimal solutions). It is used to save or retrieve the non-dominated Pareto optimal solutions obtained so far, so that these solutions can be continuously updated and optimized during the iteration of the algorithm. The key module of the archive is the archive controller, which is used to control the archive. The archive has a maximum number of members. When a solution wants to enter the archive or the archive is full, during the iteration process, the non-dominated solution obtained is compared with the archive. There will be three different possible situations as follows:

[0107] New members are not allowed to enter the archive if they are dominated by one or more masters in the archive.

[0108] If the new member dominates one or more members in the archive, it replaces the original members.

[0109] New members should also be added to the archive if they do not dominate each other. If the archive is full, the grid mechanism should be run first to rearrange the partitioning of the target space and find the most crowded segment to omit one of its solutions. Then, the new solution should be inserted into the least crowded segment to improve the diversity of the final approximate Pareto optimal front. The selection is done by a roulette wheel method with the probability of each hypercube being:

[0110]

[0111] Where C is a constant greater than 1, N i is the number of Pareto optimal solutions obtained in the i-th segment.

[0112] Step 8: Determine whether the number of iterations is met. If so, the algorithm ends and outputs the optimal path. Otherwise, continue the loop from step 2.

[0113] Step 4: Complete the transportation from the planting site to the processing plant, from the processing plant to the urban cold storage, and from the processing plant to the supermarket, and obtain the optimal route solution for the cold chain vehicles.

[0114] Step 4 further includes the following processing steps.

[0115] Determine the fitness function, compare the fitness values, find the Pareto optimal solution, and find the current global optimal solution as follows:

[0116] The size of the supermarket is M, and the total cost from the rice field to the supermarket z is G(z); it is the sum of the total costs of the three sections, expressed as:

[0117] G(z)=F1i(x)+F2i(x,y)+F3i(y,z) (14)

[0118] Among them, F1i(x) represents the first path cost of the optimal path to the zth supermarket, that is, the corresponding cost from the rice field to the processing plant, and x indicates the selected processing plant code; F2i(x,y) represents the second path cost of the optimal path to the zth supermarket, that is, the corresponding cost from the processing plant to the supermarket / cold storage, and y represents the supermarket / cold storage code; F3i(y,z) represents the third path cost of the optimal path to the zth supermarket, that is, the cost from the cold storage to the supermarket, where y is the cold storage code and z is the supermarket code.

[0119] F 1i (x) = l(p 0x )M+E(x) (15)

[0120] F 2i (x,y)=l(p xy )M (16)

[0121] F 3i (y,z)=l(p yz )M1+E2(y) (17)

[0122] Among them, l(p 0x ) is the distance from the planting site to the xth processing site, l(p xy ) is the distance from the xth processing plant location to the yth supermarket / cold storage location, l(p yz ) is the distance from the yth cold storage location to the zth supermarket, M refers to the per-unit distance cost of large cold chain logistics vehicles, and M1 is the per-unit logistics cost of small cold chain logistics vehicles. E(x) is the processing cost of transporting to the x processing plant, and E2(y) is the cost consumed in the y cold storage during the cold chain process. If y is the indicated supermarket, then l(p yz ) is 0, and F 3i (y,z) is 0.

[0123] The total time from the planting site to the supermarket is T(z); it is the sum of the time taken for the three sections, expressed as:

[0124] T(z)=T1i(x)+T2i(x,y)+T3i(y,z) (18)

[0125] Among them, T1i(x) represents the first path time of the optimal path from the planting site to the i-th supermarket, that is, the corresponding time from the planting site to the processing plant, and x indicates the selected processing plant code; T2i(x,y) represents the second path time of the optimal path to the i-th supermarket, that is, the corresponding time from the processing plant to the supermarket / cold storage, and y represents the supermarket / cold storage code; T3i(y,z) represents the third path time of the optimal path to the i-th supermarket, that is, the time from the cold storage to the supermarket, where y is the cold storage code and z is the supermarket code.

[0126]

[0127] Where t is the loading and unloading time, if y is the indicated supermarket, then l(p yz ) is 0, and T3i(y,z) is 0.

[0128] The ultimate goal is to plan the optimal path solution for multiple supermarkets, that is, the one with the lowest cost and the least time. In order to obtain the optimal solution for multi-objective optimization, the Pareto optimal solution is used. The definition of the Pareto advantage operator is as follows:

[0129]

[0130] in: Respectively The value of the ith objective function. i is the index of the objective function, and n is the total number of objective functions. This equation states that if all components of x are smaller than the corresponding components of y, or at least one component is smaller, then x is said to dominate y, denoted as x>y;

[0131] Pareto mathematics is defined as follows:

[0132]

[0133] The feasible solution set consists of all feasible solutions in X, denoted as Any one of the solutions It is called a non-dominated solution, that is, a Pareto optimal solution. X is a Pareto optimal solution, also known as a non-dominated solution. The Pareto optimal solution is solved by using a d-dimensional decision vector and multi-objective planning. The Pareto multi-objective optimization formula for solving the shortest path is as follows:

[0134]

[0135] Where: x is a set of decision vectors; F(p) is the target vector (f1,f2,…fn)∈Y∈R h The objective function of is, Y is the objective space, which is the set of all possible objective values ​​in the multi-objective optimization problem. h is the dimension of the objective function. h (x) represents the value of the decision vector x on the hth objective function. h It is an h-dimensional real number space, which represents the space of all possible objective function values ​​in multi-objective optimization. ;x L and x U are the upper and lower bound constraints of the feasible solution set. The feasible set of decision spaces of all search particles that satisfy the constraints is λ = {x∈R h |x∈[x L , x U ]}, As mentioned earlier, the purpose of optimization is to find the Pareto optimal solution. F(p) is the objective function in the path planning problem, which has two inequalities, Gz and Tz. Therefore, the optimal mathematical formula of the multi-objective cold chain logistics optimization algorithm is expressed as:

[0136]

[0137] in:

[0138]

[0139] is a decision vector, which is composed of the estimated coordinates of the corresponding solution of the cold chain logistics path optimization algorithm, Gz is the total cost from the rice field to the supermarket, and Tz is the total transportation time from the rice field to the supermarket. are the upper and lower bounds of the function respectively.

[0140] based on Figure 1 The method shown, Figure 2 An example of a cold chain logistics path optimization path is shown.

[0141] Figure 3 The principle of an embodiment of the route optimization system for cold chain logistics of supermarkets with multiple vehicle types of vehicles of the present invention is shown. Figure 3 The system of this embodiment includes: a model building module, a model parameter initialization module, a locust optimization algorithm module, and a model solving module.

[0142] The model building module is used to establish the agricultural-supermarket docking model, preset constraints in the path optimization method problem of multi-model supermarket docking cold chain logistics, and preset two types of cold chain vehicles: light refrigerated trucks and heavy refrigerated trucks. Light refrigerated trucks serve urban cold storage to supermarkets with less demand for agricultural products; heavy refrigerated trucks serve agricultural product planting areas to processing plants, processing plants to urban cold storage, and processing plants to supermarkets with greater demand for agricultural products.

[0143] The specific processing and Figure 1 Step 1 of the method embodiment shown is the same and will not be repeated here.

[0144] The model parameter initialization module is used to initialize the location and output of the planting site, the location, processing fee, and efficiency of each processing plant, the location of the supermarket, the demand for agricultural products, and the location of the city cold storage.

[0145] The specific processing of the model parameter initialization module is Figure 1 Step 2 of the method embodiment shown is the same and will not be repeated here.

[0146] The locust optimization algorithm module is used to solve the path optimization model of supermarkets connecting cold chain logistics with multiple vehicle models by improving the locust optimization algorithm.

[0147] Specific processing and Figure 1 Step three of the method embodiment shown is the same and will not be repeated here.

[0148] The model solving module is used to complete the transportation from the planting area to the processing plant, from the processing plant to the urban cold storage, and from the processing plant to the supermarket, and obtain the optimal path solution for the cold chain vehicles.

[0149] The specific processing of the model solving module Figure 1 Step 4 of the method embodiment shown is the same and will not be repeated here.

[0150] In addition, the present invention also discloses a computer system for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types, including a memory, a processor, and program instructions stored in the memory for the processor to run, wherein the processor executes the program instructions to achieve the following Figure 1 The steps of an embodiment of a method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types are shown.

[0151] In addition, the present invention also discloses a computer-readable storage medium for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types, which stores program instructions executable by a processor to achieve the following: Figure 1 The steps of an embodiment of a method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types are shown.

[0152] In addition, the present invention also discloses a computer program product, including a computer program, which, when executed by a processor, implements the following Figure 1 The steps of an embodiment of a method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types are shown.

[0153] Although the above methods are illustrated and described as a series of actions for simplicity of explanation, it should be understood and appreciated that these methods are not limited by the order of the actions, because according to one or more embodiments, some actions may occur in a different order and / or concurrently with other actions from those illustrated and described herein or not illustrated and described herein but understandable to those skilled in the art.

[0154] Those skilled in the art will further appreciate that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of the two. To clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps are generally described above in terms of their functionality. Whether such functionality is implemented as hardware or software depends on the specific application and the design constraints imposed on the overall system. The technician may implement the described functionality in different ways for each specific application, but such implementation decisions should not be interpreted as resulting in a departure from the scope of the present invention.

[0155] The various illustrative logic blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or performed with a general purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in cooperation with a DSP core, or any other such configuration.

[0156] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software module may reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, a storage medium may be integrated into a processor. The processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In an alternative, the processor and the storage medium may reside in a user terminal as discrete components.

[0157] In one or more exemplary embodiments, the functions described may be implemented in hardware, software, firmware, or any combination thereof. If implemented as a computer program product in software, each function may be stored on or transmitted by a computer-readable medium as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of a computer program from one place to another. Storage media may be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer. Any connection is also properly referred to as a computer-readable medium. For example, if the software is transmitted from a website, a server, or other remote source using a coaxial cable, a fiber optic cable, a twisted pair, a digital subscriber line (DSL), or wireless technologies such as infrared, radio, and microwaves, the coaxial cable, fiber optic cable, twisted pair, DSL, or wireless technologies such as infrared, radio, and microwaves are included in the definition of the medium. Disk and disc as used herein include compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk and Blu-ray disc, wherein disk often reproduces data magnetically, while disc reproduces data optically with lasers. Combinations of the above should also be included within the scope of computer-readable media.

[0158] The previous description of the disclosure is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to the disclosure will be apparent to those skilled in the art, and the general principles defined herein may be applied to other variations without departing from the spirit or scope of the disclosure. Thus, the disclosure is not intended to be limited to the examples and designs described herein, but should be granted the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A route optimization method for cold chain logistics of supermarkets with multiple models, characterized in that: Methods include: Step 1: Establish a supermarket docking model, preset constraints in the path optimization method problem of supermarket docking cold chain logistics with multiple models, and preset different types of cold chain vehicles; Step 2: Initialize the location and output of agricultural product planting areas, the location, processing fees, and efficiency of each processing plant, the location of supermarkets, the demand for agricultural products, and the location of urban cold storage; Step 3: Solve the path optimization model of supermarkets connecting cold chain logistics with multiple models by improving the locust optimization algorithm; Step 4: Complete the transportation from the planting site to the processing plant, from the processing plant to the urban cold storage, and from the processing plant to the supermarket, and obtain the optimal route solution for the cold chain vehicles.

2. The route optimization method for connecting supermarkets with cold chain logistics of multiple vehicle types according to claim 1 is characterized in that: In step one, two types of cold chain vehicles are preset: light refrigerated trucks and heavy refrigerated trucks. Light refrigerated trucks serve urban cold storages to supermarkets with lower demand for agricultural products; heavy refrigerated trucks serve agricultural product planting areas to processing plants, processing plants to urban cold storages, and processing plants to supermarkets with higher demand for agricultural products.

3. The route optimization method for cold chain logistics docking of supermarkets with multiple models according to claim 1 is characterized in that: Step three further includes: Step 1: Initialize locust population, Cmax, Cmin and maximum number of iterations; Step 2: Use the locust optimization algorithm to establish a mathematical model for updating the location of cold chain logistics vehicles; Step 3: A curve adaptive strategy is introduced to improve parameter c to update parameter c in the locust optimization algorithm; Step 4: Map the distance from the planting area to the processing plant, the distance from the processing plant to the city cold storage, and the distance from the processing plant to the large supermarket in the interval [1,4]; Step 5: Update the current position of the locust; Step 6: Adopt an improved Levy flight strategy to increase degrees of freedom and random behavior; Step 7: Update the parameter archive by comparison, where archive is a data structure used to store non-dominated solutions, and is used to save or retrieve the non-dominated Pareto optimal solutions obtained so far, so as to continuously update and optimize these solutions in the iterative process of the algorithm; Step 8: Determine whether the number of iterations is met. If so, the algorithm ends and outputs the optimal path. Otherwise, continue the loop from step 2.

4. A route optimization system for supermarkets with multiple models to connect to cold chain logistics, characterized in that: The system includes: The model building module is used to establish a supermarket docking model, preset constraints in the path optimization method problem of supermarket docking cold chain logistics with multiple vehicle models, and preset different types of cold chain vehicles; Model parameter initialization module, used to initialize the location and output of agricultural product planting areas, the location, processing fees, and efficiency of each processing plant, the location of supermarkets, the demand for agricultural products, and the location of urban cold storage; Locust optimization algorithm module, used to solve the path optimization model of supermarket docking cold chain logistics for multiple vehicle types by improving the locust optimization algorithm; The model solving module is used to complete the transportation from the planting area to the processing plant, from the processing plant to the urban cold storage, and from the processing plant to the supermarket, and obtain the optimal path solution for the cold chain vehicles.

5. The route optimization system for cold chain logistics docking of supermarkets with multiple models according to claim 4 is characterized in that: In the model building module, two types of cold chain vehicles are preset: light refrigerated trucks and heavy refrigerated trucks. Light refrigerated trucks serve urban cold storages to supermarkets with lower demand for agricultural products; heavy refrigerated trucks serve agricultural product planting areas to processing plants, processing plants to urban cold storages, and processing plants to supermarkets with higher demand for agricultural products.

6. The route optimization system for cold chain logistics docking of supermarkets with multiple vehicle types according to claim 4 is characterized in that: The locust optimization algorithm module is further configured to perform the following processing: Step 1: Initialize locust population, Cmax, Cmin and maximum number of iterations; Step 2: Use the locust optimization algorithm to establish a mathematical model for updating the location of cold chain logistics vehicles; Step 3: A curve adaptive strategy is introduced to improve parameter c to update parameter c in the locust optimization algorithm; Step 4: Map the distance from the planting area to the processing plant, the distance from the processing plant to the city cold storage, and the distance from the processing plant to the large supermarket in the interval [1,4]; Step 5: Update the current position of the locust; Step 6: Adopt an improved Levy flight strategy to increase degrees of freedom and random behavior; Step 7: Update the parameter archive by comparison, where archive is a data structure used to store non-dominated solutions, and is used to save or retrieve the non-dominated Pareto optimal solutions obtained so far, so as to continuously update and optimize these solutions in the iterative process of the algorithm; Step 8: Determine whether the number of iterations is met. If so, the algorithm ends and outputs the optimal path. Otherwise, continue the loop from step 2.

7. A computer system for optimizing the route of cold chain logistics for supermarkets with multiple models, characterized in that: It comprises a memory, a processor and program instructions stored in the memory and executable by the processor, wherein the processor executes the program instructions to implement the steps of the path optimization method for connecting supermarkets with cold chain logistics of multiple vehicle types as described in any one of claims 1 to 3.

8. A computer-readable storage medium for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types, characterized in that: It stores program instructions executable by a processor to implement the steps of the path optimization method for connecting supermarkets with cold chain logistics of multiple vehicle types as described in any one of claims 1 to 3.

9. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method for optimizing the route of cold chain logistics for supermarkets with multiple vehicle types connected as described in any one of claims 1 to 3 are implemented.

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