designing a multistage reverse logistics network problem by hybrid genetic algorithm
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.
genetic algorithm priority-based encoding method weight mapping crossover