27 October 2013 Optimization of min-max vehicle routing problem based on genetic algorithm
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Proceedings Volume 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing; 89200B (2013) https://doi.org/10.1117/12.2035681
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
In some cases, there are some special requirements for the vehicle routing problem. Personnel or goods geographically scattered, should be delivered simultaneously to an assigned place by a fleet of vehicles as soon as possible. In this case the objective is to minimize the distance of the longest route among all sub-routes. An improved genetic algorithm was adopted to solve these problems. Each customer has a unique integer identifier and the chromosome is defined as a string of integers. Initial routes are constructed randomly, and then standard proportional selection incorporating elitist is chosen to guarantee the best member survives. New crossover and 2-exchange mutation is adopted to increase the diversity of group. The algorithm was implemented and tested on some instances. The results demonstrate the effectiveness of the method.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xia Liu, "Optimization of min-max vehicle routing problem based on genetic algorithm", Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200B (27 October 2013); doi: 10.1117/12.2035681; https://doi.org/10.1117/12.2035681
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