Remanufacturing production planning with compensation function approximation method

Abstract

Remanufacturing is becoming a strategic emerging industry in China. However, there are many uncertain factors such as remanufacturing rate of recycling products, reprocessing costs, quantity of recycling products during a remanufacturing process. Hence, it is difficult to make an accurate production planning. This paper aims at studying a new remanufacturing production planning model in view of some possible uncertain factors in a remanufacturing enterprise according to the features and characteristics of remanufacturing. Considering the production capacity constraint of recycling, reprocessing and reassembly under the condition of uncertain reprocessing amount, unpredictable reprocessing cost, unknown purchase volume of new parts, and uncertain customer demand, this paper develops a two-stage, multi-period hybrid programming model with compensation function based on uncertainty theory to minimize the total remanufacturing cost. A hybrid intelligent algorithm is designed combined with compensation function approximation, neural network training, and virus particle algorithm to optimize this two-stage uncertain remanufacturing production planning. By use of compensation function approximation method, it is to convert an infinite optimization problem in this algorithm into that of a finite one. Finally, one remanufacturing simulation case is studied to validate the efficiency and rationality of the proposed approach.