A Graded Optimization-Based Approach to Remanufacturing Production Decision

Abstract

As a new kind of manufacturing method by recycling valuable part of used products, remanufacturing reduces production cost and environment pollution, so has received more and more attentions. The key problem in remanufacturing is making production decision. At present, such a problem is usually solved by converting it into an optimization problem, most of which use static models or simple dynamical models. Since both demand rate of new products and recycle rate of used products are dynamic, static models or even most simple dynamic models may not adequately describe such dynamic issues in remanufacturing production decision. For this case, in this paper, a complex dynamic optimization model is presented to describe the dynamic remanufacturing production decision problem. To solve this model, a new constraint-preferred graded optimization strategy-based genetic algorithm (CGGA) is given. Experiment example analysis demonstrates the proposed approach’s feasibility.