A hybrid genetic algorithm with multiple decoding methods for energy-aware remanufacturing system scheduling problem

Wenjie Wang

doi:10.1016/j.rcim.2022.102509

A hybrid genetic algorithm with multiple decoding methods for energy-aware remanufacturing system scheduling problem

2023-06-01

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DOI：10.1016/j.rcim.2022.102509

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Remanufacturing system scheduling is an essential and effective approach to realize the digitization and greening of the remanufacturing industry. However, previous researches on the remanufacturing system scheduling problem mainly consider a single or two production stages and economic objectives. In this paper, by integrating the three core production stages, i.e., disassembly, reprocessing and reassembly together, we study the energyaware remanufacturing system scheduling problem in which the well-accepted Turn Off and On strategy is also considered. First, a mathematical model aiming at minimizing the total energy consumption (TEC) of the remanufacturing system is established. Then, a hybrid genetic algorithm based on variable neighborhood search (GAVNS) solution method is proposed, given the NP-hard nature of the problem. In GAVNS, each chromosome is encoded by a job sequence and three different decoding methods are specially designed according to the formation of optimization objective TEC. To enhance the algorithm's local search capability, the variable neighborhood search technique is introduced. The feasibility and effectiveness of GAVNS in addressing the energyaware remanufacturing system scheduling problem is verified through simulation experiments on a set of designed test instances. Experimental results also demonstrate that: (1) the Turn Off and On strategy can effectively reduce TEC of the remanufacturing system, which can reach an energy saving rate of 6.68%; (2) the performance of those decoding methods varies with respect to the problem size; (3) the decoding method based on minimizing the energy consumption of the remanufacturing system (namely DM3) has the best performance among the three decoding methods in most cases; (4) GAVNS is more effective than its four peers, i.e., a variant GAVNS_R, iterated greedy algorithm (IG), extended artificial bee colony algorithm (EABC), discrete invasive weed optimization algorithm (DIWO) in seeking the optimal schedule.

Keywords:

Energy -aware scheduling

Remanufacturing system scheduling

Metaheuristics

Turn Off and On strategy

Multiple decoding methods

A hybrid genetic algorithm with multiple decoding methods for energy-aware remanufacturing system scheduling problem

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