Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/24104
Title: A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem
Authors: Lau, HCW
Chan, TM
Tsui, WT
Chan, FTS 
Ho, GTS
Choy, KL 
Keywords: Fuzzy logic
Genetic algorithms
Logistics
Multi-objective evolutionary algorithms
Multi-objective optimization
Supply chain management
Vehicle routing problem
Issue Date: 2009
Publisher: Pergamon Press
Source: Expert systems with applications, 2009, v. 36, no. 4, p. 8255-8268 How to cite?
Journal: Expert systems with applications 
Abstract: In the field of supply chain management and logistics, using vehicles to deliver products from suppliers to customers is one of the major operations. Before transporting products, optimizing the routing of vehicles is required so as to provide a low-cost and efficient service for customers. This paper deals with the problem of optimization of vehicle routing in which multiple depots, multiple customers, and multiple products are considered. Since the total traveling time is not always restrictive as a time constraint, the objective considered in this paper comprises not only the total traveling distance, but also the total traveling time. We propose using a multi-objective evolutionary algorithm called the fuzzy logic guided non-dominated sorting genetic algorithm 2 (FL-NSGA2) to solve this multi-objective optimization 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 FL-NSGA2, we compared it with the following: non-dominated sorting genetic algorithms 2 (NSGA2) (without the guide of fuzzy logic), strength Pareto evolutionary algorithm 2 (SPEA2) (with and without the guide of fuzzy logic), and micro-genetic algorithm (MICROGA) (with and without the guide of fuzzy logic). Simulation results showed that FL-NSGA2 outperformed other search methods in all of three various scenarios.
URI: http://hdl.handle.net/10397/24104
ISSN: 0957-4174
EISSN: 1873-6793
DOI: 10.1016/j.eswa.2008.10.031
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