Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7760
Title: Application of genetic algorithms to solve the multidepot vehicle routing problem
Authors: Lau, HCW
Chan, TM
Tsui, WT
Pang, WK
Keywords: Fuzzy logic
Genetic algorithms (GAs)
Logistics
Optimization
Supply chain management
Vehicle routing problem (VRP)
Issue Date: 2010
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on automation science and engineering, 2010, v. 7, no. 2, 4840417, p. 383-392 How to cite?
Journal: IEEE transactions on automation science and engineering 
Abstract: This paper deals with the optimization of vehicle routing problem in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time window constraint, the objective regarded in this paper comprises not only the cost due to the total traveling distance, but also the cost due to the total traveling time. We propose to use a stochastic search technique called fuzzy logic guided genetic algorithms (FLGA) to solve the problem. The role of fuzzy logic is to dynamically adjust the crossover rate and mutation rate after ten consecutive generations. In order to demonstrate the effectiveness of FLGA, a number of benchmark problems are used to examine its search performance. Also, several search methods, branch and bound, standard GA (i.e., without the guide of fuzzy logic), simulated annealing, and tabu search, are adopted to compare with FLGA in randomly generated data sets. Simulation results show that FLGA outperforms other search methods in all of three various scenarios.
URI: http://hdl.handle.net/10397/7760
ISSN: 1545-5955
EISSN: 1558-3783
DOI: 10.1109/TASE.2009.2019265
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