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《机械工程前沿(英文)》 >> 2019年 第14卷 第4期 doi: 10.1007/s11465-019-0560-z

Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level

. School of Mechanical & Electronical Engineering, Lanzhou University of Technology, Lanzhou 730050, China.. Institute of Sustainable Design and Manufacturing, Dalian University of Technology, Dalian 116024, China.. College of Engineering, South China Agricultural University, Guangzhou 510642, China.. Department of Industrial, Manufacturing & Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA

录用日期: 2019-11-15 发布日期: 2019-11-15

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摘要

The rise of the engine remanufacturing industry has resulted in increased possibilities of energy conservation during the remanufacturing process, and scheduling could exert significant effects on the energy performance of manufacturing systems. However, only a few studies have specifically addressed energy-efficient scheduling for remanufacturing. Considering the uncertain processing time and routes and the operation characteristics of remanufacturing, we used the crankshaft as an illustrative case and built a fuzzy job-shop scheduling model to minimize the energy consumption during remanufacturing. An improved adaptive genetic algorithm was developed by using the hormone modulation mechanism to deal with the scheduling problem that simultaneously involves parallel machines, batch machines, and uncertain processing routes and time. The algorithm demonstrated superior performance in terms of optimal value, run time, and convergent generation in comparison with other algorithms. Computational results indicated that the optimal scheduling scheme is expected to generate 1.7 kW∙h of energy saving for the investigated problem size. In addition, the scheme could improve the energy efficiency of the crankshaft remanufacturing process by approximately 5%. This study provides a basis for production managers to improve the sustainability of remanufacturing through energy-aware scheduling.

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