Designing a multistage reverse logistics network problem by hybrid genetic algorithm

ABSTRACT

We formulate a mathematical model of remanufacturing system as multistage reverse Logistics Network Problem (mrLNP) with minimizing the total costs for reverse logistics. The total costs for reverse logistics include shipping cost, fixed cost of opening the disassembly centers and processing centers and inventory holding cost at these centers over finite planning horizons. In this paper, we formulate the mrLNP model as a three stage logistics network model. For solving this problem, in the 1st and the 2nd stages, we propose a Genetic Algorithm (GA) with priority-based encoding method combined with a new crossover operator called as Weight Mapping Crossover (WMX). Additionally also a heuristic approach is applied in the 3rd stage where parts are transported from some processing centers to one manufacturer.

REFERENCES

• 1: Stock, J. K., 1992. Reverse logistics, White Paper, Council of Logistics Management, Oak Brook, IL.
• 2: Gen, M. and Cheng, R. W., 1997. Genetic Algorithm and Engineering Design, Wiley, New York.
• 3: Gen, M., Altiparmak, F. and Lin, L., 2006. A genetic algorithm for two-stage transportation problem using priority-based encoding, OR Spectrum, 28(3), 337--354.
• 4: Syarilf, A. and Gen, M. 2003. Double Spanning Tree-based Genetic algorithm For Two Stage Transportation Problem, International Journal of Knowledge-Based Intelligent Engineering System, 7(4), 388--389.

INDEX TERMS

Primary Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.3 Applications

Additional Classification:
  G. Mathematics of Computing
  G.2 DISCRETE MATHEMATICS
      G.2.2 Graph Theory

General Terms:
Algorithms, Design

Keywords:
genetic algorithm, multistage reverse logistics network problem, priority-based encoding method, weight mapping crossover