A fuzzy guided multi-objective evolutionary algorithm model for solving transportation problem

  • Authors:

  • H. C. W. Lau;T. M. Chan;W. T. Tsui;F. T. S. Chan;G. T. S. Ho;K. L. Choy

  • Affiliations:

  • Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam, Hong Kong;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong;Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2009

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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.