Multiobjective Scheduling of Energy-Efficient Stochastic Hybrid Open Shop With Brain Storm Optimization and Simulation Evaluation

Recently, energy conservation in manufacturing industry, particular in energy-intensive industries, receives much attention in order to meet the environmental protection and sustainable development needs. Optimal job scheduling is of great importance in reducing unnecessary energy consumption. To th...

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Vydané v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 54; číslo 7; s. 4260 - 4272
Hlavní autori: Fu, Yaping, Zhou, Mengchu, Guo, Xiwang, Qi, Liang, Gao, Kaizhou, Albeshri, Aiiad
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York IEEE 01.07.2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Brain storm optimization
Discrete event systems
discrete-event simulation
Energy consumption
Energy efficiency
energy-efficient scheduling
Environmental protection
Evolution
hybrid open shop scheduling
Job shop scheduling
Manufacturing
multiobjective optimization
Multiple objective analysis
Optimization
Particle swarm optimization
Production
Scheduling
Simulation
Stochastic processes
stochastic simulation
Sustainable development
Workstations
ISSN:2168-2216, 2168-2232
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Shrnutí:Recently, energy conservation in manufacturing industry, particular in energy-intensive industries, receives much attention in order to meet the environmental protection and sustainable development needs. Optimal job scheduling is of great importance in reducing unnecessary energy consumption. To this end, both energy and time-related criteria need to be taken into consideration to achieve an efficient and sustainable production process. Generally, it is difficult to obtain the accurate processing time of jobs in advance due to various uncertainties in open shop scheduling problems arising from manufacturing and service systems. This work formulates a stochastic multiobjective hybrid open shop scheduling problem that consists of open shop and parallel-machine models. First, a multiobjective chance-constrained program is established to minimize total tardiness and energy consumption while meeting makespan requirements. Second, we newly develop a multiobjective framework integrating a brain storm optimizer and a simulation system to solve this problem. We combine population evolution to enhance exploration and external archive evolution to strengthen exploitation into the brain storm optimizer to seek for promising solutions. A simulation system is accordingly designed by using stochastic simulation and discrete-event simulation to assess the searched solutions. Finally, by conducting experiments and comparing the proposed method with several existing algorithms and an exact solver, our results confirm that it significantly outperforms its peers in tackling the considered problem.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2024.3376292