OPTIMIZATION OF REMANUFACTURING PRODUCTION SCHEDULING CONSIDERING UNCERTAIN FACTORS

This paper attempts to optimize the remanufacturing production scheduling under randomness and fuzziness. Firstly, the rough set theory and multi-objective approximation ranking algorithm were combined into a quality evaluation method of remanufacturing recycling resources, which eliminates the redundant information in quality evaluation. Then, a remanufacturing production scheduling model was constructed under uncertain conditions, and a hybrid algorithm coupling double fuzzy algorithm, backpropagation (BP) neural network and genetic algorithm was developed to solve the model. The simulation results show that the algorithm achieved good convergence and the obtained solution can minimize the total cost of production scheduling and the processing time. This means the model algorithm can effectively optimize the scheduling of remanufacturing production. The research findings shed new light on the fast quality evaluation of recycled resources and the optimization scheduling of remanufacturing production.