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.

Date Published: 27 October 2013
PDF: 5 pages
Proc. SPIE 8920, MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing, 89200B (27 October 2013); doi: 10.1117/12.2035681

Published in SPIE Proceedings Vol. 8920:
MIPPR 2013: Parallel Processing of Images and Optimization and Medical Imaging Processing
Jianguo Liu, Editor(s)