Open AccessMULTI-OBJECTIVE DISTRIBUTION ROUTING OPTIMIZATION WITH TIME WINDOW BASED ON IMPROVED GENETIC ALGORITHM
Author(s) -
Shaofei Wu,
C. Chen
Publication year - 2018
Publication title -
latin american applied research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.123
H-Index - 23
eISSN - 1851-8796
pISSN - 0327-0793
DOI - 10.52292/j.laar.2018.218
Subject(s) - crossover , vehicle routing problem , genetic algorithm , mathematical optimization , computer science , convergence (economics) , matlab , mutation , population , cultural algorithm , algorithm , population based incremental learning , path (computing) , estimation of distribution algorithm , routing (electronic design automation) , mathematics , artificial intelligence , computer network , biochemistry , chemistry , demography , sociology , economics , gene , programming language , economic growth , operating system
In order to solve the shortcomings of the traditional genetic algorithm in solving the problem of logistics distribution path, a modified genetic algorithm is proposed to solve the Vehicle Routing Problem with Time Windows (VRPTW) under the condition of vehicle load and time window. In the crossover process, the best genes can be preserved to reduce the inferior individuals resulting from the crossover, thus improving the convergence speed of the algorithm. A mutation operation is designed to ensure the population diversity of the algorithm, reduce the generation of infeasible solutions, and improve the global search ability of the algorithm. The algorithm is implemented on Matlab 2016a. The example shows that the improved genetic algorithm reduces the transportation cost by about 10% compared with the traditional genetic algorithm and can jump out of the local convergence and obtain the optimal solution, thus providing a more reasonable vehicle route.