Logistics Distribution Vehicle Routing Problem And Double Population Hybrid Genetic Algorithm

Compared with developed Western countries,my country's logistics industry started relatively late.Although great progress has been made in recent years,the overall operating efficiency of the logistics industry is relatively low.According to relevant statistics,it is found that in the logistics operations such as transportation,distribution,warehousing,handling,circulation processing and information processing,the cost of distribution operations accounts for more than 50% of the total cost of logistics operations,and has always been high.Therefore,how to rationally optimize the distribution lines and vehicles and realize the high-efficiency and low-cost operation of distribution operations in the logistics system has always been the focus of the logistics industry.The distribution operation problem can be abstracted into a capacity-constrained vehicle routing problem,which is a typical NP-hard problem and a combination optimization problem.The complexity of vehicle routing problem solving will increase exponentially as the problem size increases.For the solution method of vehicle routing problem,there are mainly precise algorithm,traditional heuristic algorithm and intelligent optimization algorithm.In this paper,genetic algorithm is used to solve the vehicle routing problem.The genetic algorithm was proposed by Professor J.H.Holland of the United States in 1975.The algorithm is simple,easy to implement,and has the advantages of parallelism and strong robustness.It is a classic algorithm for solving problems such as vehicle routing.After fully studying the vehicle path characteristics and the advantages and disadvantages of the genetic algorithm,this paper designs three types of improved genetic algorithms to solve the problem,and the effectiveness of the algorithm is proved by the test of related data sets.main tasks as follows:(1)When the traditional genetic algorithm solves the capacity-constrained vehicle path,the chromosome coding adopts a random division strategy.This coding strategy will generate a large number of infeasible solutions and reduce the search space of the algorithm.Therefore,a genetic algorithm based on integer mapping coding is designed so that each chromosome produces a feasible solution after decoding,to ensure that the space of the algorithm solution always maintains the diversity characteristics during the algorithm iteration process,and improve the probability of the algorithm to find the optimal solution.(2)Verification shows that the genetic algorithm based on integer mapping coding is better than the traditional genetic algorithm,but the algorithm still has problems such as premature convergence and easy to fall into local optimality.The solution depends heavily on the initial population,and the specific coding strategy weakens the choice.Search capabilities of operators and crossover operators.Therefore,consider combining the local search capability of the tabu search algorithm with the global search capability of the genetic algorithm,design a hybrid genetic tabu search algorithm to solve the capacity-constrained vehicle routing problem,and improve the search performance of the algorithm through a combination of global search and local search.Improve the probability of the algorithm to find the optimal solution.(3)Verification shows that the result of the hybrid algorithm of genetic tabu search is superior to the traditional genetic algorithm and genetic algorithm based on integer mapping encoding,but it requires multiple trial calculations to obtain a satisfactory solution.The performance of the algorithm is unstable and the solution comparison depends on The initial population.In order to further improve the stability and accuracy of the algorithm,a two-population hybrid genetic algorithm is designed.As the elite group,population I uses the scanning algorithm to produce the initial population,so that population I is in a better state at the beginning of the iteration.Local search using variable neighborhood search strategy improves the probability of the algorithm finding the optimal solution.Population II,as a disadvantaged group,conducts overall development,and when it is judged that the population reaches the premature convergence state,new external individuals are implanted to ensure the diversity of population II.Population I and Population II carry out collaborative optimization between populations through immigration operators to improve the stability and feasibility of the algorithm as a whole.The study found that the dual-population hybrid genetic algorithm is superior to the traditional genetic algorithm,genetic algorithm based on mapping coding and genetic tabu search hybrid algorithm in terms of stability and solution accuracy,which provides a new solution for the capacity constrained vehicle routing problem.