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Published in: Neural Computing and Applications 5/2021

15-06-2020 | Original Article

Hybrid multi-objective opposite-learning evolutionary algorithm for integrated production and maintenance scheduling with energy consideration

Authors: Binghai Zhou, Xiujuan Li, Wenlong Liu

Published in: Neural Computing and Applications | Issue 5/2021

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Abstract

While conventional scheduling researches take production efficiency, cost and quality as objectives, increasingly serious ecological problems and energy shortage have turned scholars’ attention to energy-efficient scheduling. Meanwhile, maintenance activities are of great importance to equipment availability and production continuity. This paper addresses an energy-efficient multi-objective scheduling problem of a serial production line (SPSP) integrating production and maintenance, with the criteria of minimizing the makespan and the total energy consumption simultaneously. The impact of integrating preventive maintenance (PM) is analyzed, and the better interval mode is picked out. An energy-saving strategy combining shutdown windows of PM and energy-saving windows on idle machines is designed to cut down the total energy consumption. In order to tackle this NP-hard problem, a hybrid multi-objective opposite-based learning evolutionary algorithm (HMOLEA) is developed, in which the opposite-based learning and IGD indicator-based evolutionary mechanism are combined together and a special rank assignment is designed to improve the computational efficiency. Furthermore, a self-adaptive weighted mutation operator is fused into the framework of HMOLEA to enhance the exploration of the algorithm. Extensive computational experiments are carried out to verify the effectiveness and superiority of HMOLEA in solving the SPSP.

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Literature
10.
go back to reference Wang L, Zhou G, Xu Y et al (2011) Advances in the study on hybrid flow shop scheduling. Control Instr Chem Industry 38(1):1–8 Wang L, Zhou G, Xu Y et al (2011) Advances in the study on hybrid flow shop scheduling. Control Instr Chem Industry 38(1):1–8
11.
go back to reference Wang L, Zhou G, Xu Y et al (2012) An artificial bee colony algorithm for solving hybrid flow-shop scheduling problem with unrelated parallel machines. Control Theory Appl 29(12):1551–1557MATH Wang L, Zhou G, Xu Y et al (2012) An artificial bee colony algorithm for solving hybrid flow-shop scheduling problem with unrelated parallel machines. Control Theory Appl 29(12):1551–1557MATH
25.
go back to reference Fei YY, Ma HM (2018) Multi-objective joint optimization of batch-discrete hybrid flow shop scheduling integrated with machine maintenance[C]. In: 2018 5th International conference on industrial engineering and applications (ICIEA). IEEE, pp 247–253. https://doi.org/10.1109/IEA.2018.8387105 Fei YY, Ma HM (2018) Multi-objective joint optimization of batch-discrete hybrid flow shop scheduling integrated with machine maintenance[C]. In: 2018 5th International conference on industrial engineering and applications (ICIEA). IEEE, pp 247–253. https://​doi.​org/​10.​1109/​IEA.​2018.​8387105
34.
go back to reference Azadeh A, Goodarzi AH, Kolaee MH et al (2019) An efficient simulation–neural network–genetic algorithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects. Neural Comput Appl 31:5327–5341CrossRef Azadeh A, Goodarzi AH, Kolaee MH et al (2019) An efficient simulation–neural network–genetic algorithm for flexible flow shops with sequence-dependent setup times, job deterioration and learning effects. Neural Comput Appl 31:5327–5341CrossRef
41.
go back to reference Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). IEEE vol 1 pp 695–701 Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: International conference on computational intelligence for modelling, control and automation and international conference on intelligent agents, web technologies and internet commerce (CIMCA-IAWTIC’06). IEEE vol 1 pp 695–701
44.
Metadata
Title
Hybrid multi-objective opposite-learning evolutionary algorithm for integrated production and maintenance scheduling with energy consideration
Authors
Binghai Zhou
Xiujuan Li
Wenlong Liu
Publication date
15-06-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05075-3

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