A Stochastic Product Priority Optimization Method for Remanufacturing System Based on Genetic Algorithm

Abstract

Increasing number of manufacturers are developing remanufacturing facilities to recover end-of-life products for product/component reuse and material recycling while the high uncertainty pattern of returned products complicates the production planning. In this thesis a stochastic production priority optimization method, considering various priority concerns for remanufacturing systems is developed. Priority ranking and matching algorithm is developed to determine the priority rule, using thirteen weighting factors. Queueing models are developed to formulate the objective function, a genetic algorithm is then developed to search optimal solution under different business configurations. Result of this research will provide insights to priority assignment mechanism, which in turn provides support to manufacturers in decision-making in production planning thus improving the performance of remanufacturing systems