A modified beluga whale optimization for optimizing energy-efficient no-idle permutation flow shop scheduling problem
Abstract
Attention to energy-efficient permutation flow shop scheduling problems is increasing in the context of globalization and environmental awareness. However, the energy-efficient no-idle permutation flow shop scheduling problem (NIPFSP) has rarely been studied. This study proposes a procedure using Beluga Whale Optimization (BWO) to solve the energy-efficient NIPFSP issue to reduce total energy consumption (TEC). The offered procedure is tested using four job and machine variations and compared with the PSO algorithm. An independent sample t-test was performed to test the optimization results of the BWO and PSO in 4 cases. The results indicate that the offered procedure produces lower total energy consumption than the PSO algorithm. In addition, the algorithm also provides more competitive results when solving the energy-efficient NIPFSP problem with a larger number of jobs. The implications of this academic research show that the proposed procedure can be applied to solve the problem of energy-efficient NIPFSP, which is indicated by low energy consumption.
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- A modified beluga whale optimization for optimizing energy-efficient no-idle permutation flow shop scheduling problem
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Published: 27 February 2024
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