Logistics distribution center site selection method based on random chaos immune optimization algorithm

Abstract

The invention discloses a logistics distribution center site selection method based on a random chaos immune optimization algorithm, and belongs to the technical field of logistics distribution center site selection, and the method comprises the steps: 1, constructing a target function of logistics distribution center site selection; and 2, solving a target function of logistics distribution center site selection by adopting a random chaos immune algorithm. According to the logistics distribution center site selection method based on the random chaos immune optimization algorithm, the problem that a traditional immune algorithm is prone to falling into a local optimal solution is solved, the nonlinear characteristic of chaos mapping and a random adjustment mechanism are combined, the balance of the algorithm between global exploration and local development is effectively coordinated, and the selection accuracy of the logistics distribution center site selection method is improved. According to the method, the accuracy and the stability of site selection of the logistics distribution center can be quickly realized, and the delivery path and the delivery sequence of customers can be reasonably arranged, so that the site selection scheme of the logistics distribution center is fully utilized, and the distribution efficiency is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics distribution center location, and particularly relates to a method for locating a logistics distribution center based on a stochastic chaos immune optimization algorithm. Background Art

[0002] The tobacco industry always adheres to the advanced development concept of "large logistics and large distribution". Among them, the location of the logistics distribution center is a key link in the enterprise's logistics supply chain. Mastering the rapid and timely logistics distribution ability can bring stronger market competitiveness to the enterprise.

[0003] The location decision of the logistics distribution center is a typical NP-hard problem. Many scholars have discussed this problem. For example, the literature "Qin Fangfang, Zhang Jiarui, Zhang Ting, etc. Location of Logistics Distribution Center Based on Adaptive Genetic Algorithm [J]. Logistics Engineering and Management, 2023, 45(08): 1-6" proposed an adaptive genetic algorithm and obtained efficient location results; the literature "Zhao Shian, Qu Chiwen. Solving the Location Problem of Logistics Distribution Center by Improved Cuckoo Algorithm [J]. Practice and认识 of Mathematics, 2017, 47(03): 206-213" proposed an adaptive improvement strategy for parasitic nest fitness ranking to effectively coordinate the algorithm. Therefore, heuristic algorithms are widely used to solve the location problem of logistics distribution centers. However, the traditional immune optimization algorithm is prone to falling into local optimal solutions during the solution process, thus limiting the accuracy of the location results and greatly reducing the logistics distribution efficiency. Summary of the Invention

[0004] The technical problem to be solved by the present invention is: a method for locating a logistics distribution center based on a stochastic chaos immune optimization algorithm, which can improve the accuracy of location and enhance the distribution efficiency.

[0005] The technical solution adopted by the present invention is: a method for locating a logistics distribution center based on a stochastic chaos immune optimization algorithm, which includes the following steps:

[0006] Step 1: Construct an objective function for the location of the logistics distribution center;

[0007] Step 2: Use the stochastic chaos immune algorithm to solve the objective function for the location of the logistics distribution center.

[0008] The construction method of the objective function for the location of the logistics distribution center in Step 1 is as follows:

[0009] In the logistics operation system, let N = {1, 2,..., n} be the set of demand points, where each element i ∈ N corresponds to a demand node. For Define M i == {j|a ij <l} as the set of neighboring candidate centers of i, where a ijrepresents the node spacing, and the objective function is as follows:

[0010]

[0011] The constraints are:

[0012]

[0013] G ij ≤p i ,i∈N,j∈M i (5)

[0014]

[0015] G ij ,p i ∈{0,1},i∈N,j∈M i (7)

[0016] a ij ≤l(8)

[0017] Among them, F(min) represents the optimal path, ω i Characterizes the demand intensity of node i; G ij ∈{0,1} is a binary decision variable if and only if G ij =1, node G ij The service is provided by center j, and p is defined at the same time j ∈{0,1} is the location decision variable, where p j =1 means establishing a distribution center at location j; parameter l is a preset threshold used to constrain the maximum service radius of the distribution center.

[0018] The method of optimizing the objective function of logistics distribution center location selection using random chaotic immune algorithm in step 2 is as follows:

[0019] By combining the nonlinear characteristics of chaotic mapping and the random generation mechanism, the mutation probability is dynamically adjusted during the optimization process. The formula of random chaotic strategy is as follows:

[0020]

[0021] Among them, C t is the chaotic map value of the current iteration, and its initial value is a random number between 0 and 1; z is the control parameter.

[0022] Beneficial effects of the present invention: Compared with the existing technology, the logistics distribution center site selection method based on the random chaos immune optimization algorithm of the present invention, in view of the fact that traditional immune algorithms are prone to fall into local optimal solutions, combines the nonlinear characteristics of chaotic mapping and random adjustment mechanism, effectively coordinates the balance between global exploration and local development of the algorithm, can quickly achieve the accuracy and stability of logistics distribution center site selection, can reasonably arrange the customer's delivery route and sequence, thereby making full use of the logistics distribution center site selection plan and significantly improving distribution efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the logistics distribution center location selection method based on random chaos immune optimization algorithm;

[0024] Figure 2 This is the convergence comparison diagram of the immune optimization algorithm;

[0025] Figure 3 This is a convergence comparison chart of random chaos immune algorithm;

[0026] Figure 4 This is the location selection plan diagram of the logistics distribution center based on the immune optimization algorithm;

[0027] Figure 5 This is the location selection plan diagram of the logistics distribution center based on the random chaotic immune algorithm;

[0028] Figure 6 This is the flow chart of random chaos immune algorithm. DETAILED DESCRIPTION

[0029] In view of the fact that traditional immune optimization algorithms are prone to falling into local optimal solutions during the solution process, thereby limiting the accuracy of the site selection results, the present invention proposes a logistics distribution center site selection method based on a random chaotic immune optimization algorithm. In view of the fact that traditional immune algorithms are prone to falling into local optimal solutions, the nonlinear characteristics of chaotic mapping and the random adjustment mechanism are combined to effectively coordinate the balance between global exploration and local development of the algorithm, as shown in the specific embodiments.

[0030] Immune algorithms are based on swarm intelligence, solving problems through collaboration and information sharing among individuals within a group. Using affinity as the core metric for evaluating individuals, they inherit traditional genetic operations such as crossover and mutation, and also incorporate unique mechanisms such as clonal selection, immune memory, and vaccination to generate new antibodies. By regulating the antibody production process, they achieve self-regulation, thus better adapting to complex optimization problems.

[0031] Example 1: Figures 1-6 As shown in FIG, a logistics distribution center location selection method based on random chaos immune optimization algorithm includes the following steps:

[0032] Step 1: Construct the objective function for the location selection of the logistics distribution center. The objective function is constructed with the coordinate information of cities and the material demand as independent variables and the optimal path as the dependent variable.

[0033] The construction method of the objective function for the location selection of the logistics distribution center is as follows:

[0034] Apply the stochastic chaos immune algorithm to the location decision of the logistics distribution center to evaluate the optimization ability of the algorithm. In the logistics operation system, let N = {1, 2, …, n} be the set of demand points, where each element i ∈ N corresponds to a demand node. For Define M i == {j|a ij <l} as the set of neighboring candidate centers of i, where a ij represents the node distance. The objective function is as follows:

[0035]

[0036] The constraint conditions are:

[0037]

[0038] G ij ≤ p i , i ∈ N, j ∈ M i (5)

[0039]

[0040] G ij , p i ∈ {0, 1}, i ∈ N, j ∈ M i (7)

[0041] a ij ≤ l (8)

[0042] Among them, ω i represents the demand intensity of node i; G ij ∈ {0, 1} is a binary decision variable. When and only when G ij = 1, node G ij is served by center j. At the same time, define p j ∈ {0, 1} as the location decision variable, where p j = 1 means to establish a distribution center at j; the parameter l is a preset threshold used to constrain the maximum service radius of the distribution center;

[0043] Step 2: Use the stochastic chaos immune algorithm to solve the objective function for the location selection of the logistics distribution center.

[0044] Seven elements of the chaos immune algorithm:

[0045] Both immune algorithms and genetic algorithms employ swarm search strategies and emphasize information exchange among individuals within a swarm. However, immune algorithms differ from genetic algorithms in their individual evaluation criteria, basing their evaluation on affinity rather than fitness. This makes their individual evaluation more rational. Regarding individual generation, immune algorithms employ not only genetic operations such as crossover and mutation but also unique operations such as clonal selection, immune memory, and vaccination to generate new antibodies. Furthermore, immune algorithms can promote or inhibit antibody production, fully demonstrating the self-regulatory function of the immune response and effectively ensuring individual diversity.

[0046] The following are the seven key elements of the immune algorithm and their detailed descriptions:

[0047] (1) Antibody recognition

[0048] The objective function and its constraints are considered as antigens, while antibodies are candidate solutions to the optimization problem. The solutions are represented as antibodies in some way (such as encoding) to facilitate subsequent affinity calculation.

[0049] (2) Initial antibody generation

[0050] Randomly generate N antibodies with unique type string dimension M. These antibodies constitute the initial population, providing a diverse starting point for the subsequent optimization process.

[0051] (3) Affinity calculation

[0052] This is the core step of the immune algorithm and also the most challenging part. Affinity is usually measured by the value of the objective function, reflecting the quality of the antibody (solution). The higher the affinity, the better the match between the antibody and the antigen.

[0053] (4) Memory cell differentiation

[0054] Antibodies with higher affinity are selected from the current population as memory cells and stored in the memory bank. Memory cells are used to preserve excellent solutions for faster response in subsequent iterations.

[0055] (5) Antibody promotion and inhibition

[0056] Through cloning and mutation operations, antibodies with higher affinity are enhanced (promoted) while antibodies with lower affinity are suppressed. This step simulates the clonal selection mechanism of antibodies in the biological immune system to improve the overall quality of the population.

[0057] (6) Producing new antibodies

[0058] New antibodies are generated from the current population through operations such as crossover and mutation. These new antibodies inject new diversity into the population and help explore the solution space.

[0059] The method of optimizing the objective function of logistics distribution center location selection using random chaotic immune algorithm in step 2 is as follows:

[0060] Chaotic mapping is a common nonlinear motion phenomenon with characteristics such as ergodicity and randomness. Among them, the sinusoidal chaotic mapping is widely used in the field of optimization algorithms due to its unique chaotic characteristics, as shown in the following formula (1):

[0061] C t+1 =z*C t 2 *sin(π*C t ) (1)

[0062] Aiming at the problem that the random generation mechanism of mutation probability in immune algorithm easily leads to the algorithm falling into local optimal solution, a random-chaotic strategy is proposed. By combining the nonlinear characteristics of chaotic mapping and random generation mechanism, the mutation probability is dynamically adjusted during the optimization process, effectively avoiding the algorithm from falling into local optimal solution. The random-chaotic strategy is shown in the following formula (2):

[0063]

[0064] Among them, C t is the chaotic map value of the current iteration, and its initial value is a random number between 0 and 1; z is the control parameter, and its value is 2.3.

[0065] In order to illustrate the effect of the present invention, the following simulation verification is performed:

[0066] Based on the coordinate information and material demand of 31 cities across China (see Table 1 for specific data), a randomized chaotic immune algorithm was used to solve the logistics distribution center location problem and compared with the original immune algorithm. Algorithm parameter configuration: population size: 50, number of iterations: 100, number of distribution centers: 8, memory capacity: 10, crossover probability: 0.5, mutation probability: 0.4, diversity evaluation parameter: 0.95, control parameter: 2.3. The output of the objective function is: the average distance from the demand point to the assigned distribution center and the location results of the logistics distribution center location plan.

[0067] Table 1 Location and material demand of 31 cities

[0068] i <![CDATA[(Y i ,V i )]]> <![CDATA[h i ]]> i <![CDATA[(Y i ,V i )]]> <![CDATA[h i ]]> i <![CDATA[(Y i ,V i )]]> <![CDATA[h i ]]> 1 (1304，2312) 20 12 (2562，1756) 40 23 (3429，1908) 80 2 (3639，1315) 90 13 (2788，1491) 40 24 (3507，2376) 70 3 (4177，2244) 90 14 (2381，1676) 40 25 (3394，2643) 80 4 (3712，1399) 60 15 (1332，695) 20 26 (3439，3210) 40 5 (3488，1535) 70 16 (3715，1678) 80 27 (2935，3240) 40 6 (3326，1556) 70 17 (3918，2179) 90 28 (3140，3550) 60 7 (3238，1229) 40 18 (4061，2370) 70 29 (2545，2357) 70 8 (4196，1044) 90 19 (3780，2212) 100 30 (2778，2826) 50 9 (4312，790) 90 20 (3676，2578) 50 31 (2370，2975) 30 10 (4386，570) 70 21 (4029，2838) 50 11 (3007，1970) 60 22 (4263，2931) 50

[0069] In order to fully consider the randomness of the algorithm, the present invention performed 10 independent calculations for each algorithm. The average value of the sum of distances weighted by the demand amount of each demand point calculated by the original immune algorithm was 4.93×10 5The average value of the random chaotic immune algorithm proposed in this paper is 4.56×10 5 ,It can be seen that the random chaos immune algorithm has a significant improvement in optimization effect compared with the original immune algorithm. Figure 1 and Figure 2 The convergence curves of the immune algorithm and random chaos immune algorithm are shown respectively. Figure 4 and Figure 5 The corresponding logistics distribution center location solutions for the two algorithms are shown below. A comparative analysis of the weighted distances and average values ​​of the two algorithms shows that the randomized chaos immune algorithm outperforms the traditional immune algorithm in terms of global search capability and convergence speed. This demonstrates that incorporating randomized chaos strategies into immune algorithms can effectively prevent the algorithm from falling into local optimal solutions, thereby improving optimization results.

[0070] By integrating with information systems, this invention enables effective integration and scientific management of customer groups. Specifically, it rationally arranges customer delivery routes and sequences, thereby fully utilizing the logistics distribution center location plan and significantly improving delivery efficiency. This invention achieves efficient logistics distribution optimization through the following two core steps:

[0071] 1) Establish data analysis and standard models

[0072] This paper focuses on the key aspects of logistics distribution center site selection and establishes a logistics distribution center site selection model by improving the algorithm. This model can provide scientific guidance and standards for logistics distribution.

[0073] 2) Real-time route optimization

[0074] Based on key information such as the geographic location and material demand of the 31 cities involved, the present invention can automatically generate scientific and feasible delivery route plans every day. These plans can optimize the location and path, thereby achieving the goals of economy, speed, efficiency and improved service quality.

[0075] In summary, this paper proposes a logistics distribution center site selection method based on a randomized chaotic immune optimization algorithm. This method addresses the problem of traditional immune algorithms being prone to falling into local optimal solutions. By combining the nonlinear characteristics of chaotic mapping with a randomized adjustment mechanism, the algorithm effectively balances global exploration with local exploitation. Simulation experiments using the MATLAB platform validate the proposed algorithm's superior performance in site selection accuracy and computational efficiency.

Claims

1. A logistics distribution center location selection method based on random chaos immune optimization algorithm is characterized by: The method comprises the following steps: Step 1: Construct the objective function for the location selection of the logistics distribution center. The objective function takes the city's coordinate information and material demand as independent variables and the optimal path as the dependent variable. Step 2: Use random chaotic immune algorithm to solve the objective function of logistics distribution center location selection.

2. The method for selecting a logistics distribution center location based on random chaotic immune optimization algorithm according to claim 1 is characterized in that: The objective function for the location selection of the logistics distribution center in step 1 is constructed as follows: In the logistics operation system, let N = {1, 2, …, n} be the set of demand points, where each element i ∈ N corresponds to a demand node. For Define M i == {j|a ij <l} as the set of neighboring candidate centers of i, where, a ij represents the node spacing. The objective function is as follows: The constraints are: G ij ≤p i ,i∈N,j∈M i (5) G ij ,p i ∈{0,1},i∈N,j∈M i (7)a ij ≤l(8)where F(min) represents the optimal path, ω i Characterizes the demand intensity of node i; G ij ∈{0,1} is a binary decision variable if and only if G ij =1, node G ij The service is provided by center j, and p is defined at the same time j ∈{0,1} is the location decision variable, where p j =1 means establishing a distribution center at location j; parameter l is a preset threshold used to constrain the maximum service radius of the distribution center.

3. The method for selecting a logistics distribution center location based on random chaotic immune optimization algorithm according to claim 1 is characterized in that: The method of optimizing the objective function of logistics distribution center location selection using random chaotic immune algorithm in step 2 is as follows: By combining the nonlinear characteristics of chaotic mapping and the random generation mechanism, the mutation probability is dynamically adjusted during the optimization process. The formula of random chaotic strategy is as follows: Among them, C t is the chaotic map value of the current iteration, and its initial value is a random number between 0 and 1; z is the control parameter.

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