Journal
JOURNAL OF CLEANER PRODUCTION
Volume 188, Issue -, Pages 575-588Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.03.254
Keywords
Flexible job-shop scheduling problem; Energy-saving scheduling; Energy consumption; Modified genetic algorithm; Particle swarm optimization
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Funding
- National Natural Science Foundation of China [51675388, 51775392]
- Key Research Base of Humanities and Social Sciences in Hubei University of Higher Education [17CYY02]
- Wuhan University of Science and Technology Graduate Student Short-term Abroad (Habitat) Training Special Funds [024/24000203]
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Scheduling can have significant impacts on energy saving in manufacturing systems. The complex process constraints and dynamic manufacturing tasks in flexible manufacturing system make the scheduling a complicated nonlinear programming problem. To this end, this paper proposes a two-stage energy-saving optimization method for Flexible Job-Shop Scheduling Problems (FJSP). In this method, an operation-based integrated chart is firstly proposed to reveal the dynamic characteristics of the operations, enabling the energy-saving scheduling optimization. Then the optimization is conducted at two stages: the machine tool stage and the operation sequence stage. A Modified Genetic Algorithm (MGA) is applied at the first stage and a hybrid method that integrates Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) is adopted at the second stage. Finally, a case study is employed to illustrate the applicability and validity of the proposed method. The results revealed that the proposed method can effectively optimize FJSP. This may provide a basis for decision makers to utilize a manufacturing scheduling that is optimized regarding its energy saving. (C) 2018 Elsevier Ltd. All rights reserved.
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