TY - JOUR
T1 - Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level
AU - Zhao, Jiali
AU - Peng, Shitong
AU - Li, Tao
AU - Lv, Shengping
AU - Li, Mengyun
AU - Zhang, Hongchao
N1 - Publisher Copyright:
© 2019, Higher Education Press and Springer-Verlag GmbH Germany, part of Springer Nature.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/12/1
Y1 - 2019/12/1
N2 - 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.
AB - 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.
KW - adaptive genetic algorithm
KW - energy efficiency
KW - hormone modulation mechanism
KW - remanufacturing scheduling
KW - sustainable remanufacturing
UR - http://www.scopus.com/inward/record.url?scp=85075242466&partnerID=8YFLogxK
U2 - 10.1007/s11465-019-0560-z
DO - 10.1007/s11465-019-0560-z
M3 - Article
AN - SCOPUS:85075242466
VL - 14
SP - 474
EP - 488
JO - Frontiers of Mechanical Engineering
JF - Frontiers of Mechanical Engineering
SN - 2095-0233
IS - 4
ER -