Modeling and scheduling for remanufacturing systems with disassembly, reprocessing, and reassembly considering total energy consumption.

As one of the mainstream development directions of remanufacturing industry, remanufacturing system scheduling has become a hot research topic recently. This study regards a scheduling problem for remanufacturing systems where end-of-life (EOL) products are firstly disassembled into their constituent components, and next these components are reprocessed to like-new states. At last, the reprocessed components are reassembled into new remanufactured products. Among various system configurations, we investigate a scheduling problem for the one with parallel disassembly workstations, several parallel flow-shop-type reprocessing lines and parallel reassembly workstations for the objective of minimize total energy consumption. To address this problem, a mathematical model is established and an improved genetic algorithm (IMGA) is proposed to solve it due to the problem complexity. The proposed IMGA adopts a hybrid initialization method to improve the solution quality and diversity at the beginning. Crossover operation and mutation operation are specially designed subject to the characteristics of the optimization problem. Besides, an elite strategy is combined to gain a faster convergence speed. Numerical experiments are conducted and the results verify the effectiveness of the scheduling model and proposed algorithm. The work can assist production managers in better planning a scheduling scheme for remanufacturing systems.