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Article

Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm

1
School of Transportation, Ludong University, Yantai 264025, China
2
Department of Computer Science and Technology, Henan Institute of Technology, Xinxiang 453003, China
3
School of Information and Electrical Engineering, Ludong University, Yantai 264025, China
*
Author to whom correspondence should be addressed.
Mathematics 2018, 6(11), 220; https://doi.org/10.3390/math6110220
Received: 28 September 2018 / Revised: 20 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018
(This article belongs to the Special Issue Evolutionary Computation)
Under the current environmental pressure, many manufacturing enterprises are urged or forced to adopt effective energy-saving measures. However, environmental metrics, such as energy consumption and CO2 emission, are seldom considered in the traditional production scheduling problems. Recently, the energy-related scheduling problem has been paid increasingly more attention by researchers. In this paper, an energy-efficient job shop scheduling problem (EJSP) is investigated with the objective of minimizing the sum of the energy consumption cost and the completion-time cost. As the classical JSP is well known as a non-deterministic polynomial-time hard (NP-hard) problem, an improved whale optimization algorithm (IWOA) is presented to solve the energy-efficient scheduling problem. The improvement is performed using dispatching rules (DR), a nonlinear convergence factor (NCF), and a mutation operation (MO). The DR is used to enhance the initial solution quality and overcome the drawbacks of the random population. The NCF is adopted to balance the abilities of exploration and exploitation of the algorithm. The MO is employed to reduce the possibility of falling into local optimum to avoid the premature convergence. To validate the effectiveness of the proposed algorithm, extensive simulations have been performed in the experiment section. The computational data demonstrate the promising advantages of the proposed IWOA for the energy-efficient job shop scheduling problem. View Full-Text
Keywords: energy-efficient job shop scheduling; dispatching rule; nonlinear convergence factor; mutation operation; whale optimization algorithm energy-efficient job shop scheduling; dispatching rule; nonlinear convergence factor; mutation operation; whale optimization algorithm
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MDPI and ACS Style

Jiang, T.; Zhang, C.; Zhu, H.; Gu, J.; Deng, G. Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm. Mathematics 2018, 6, 220. https://doi.org/10.3390/math6110220

AMA Style

Jiang T, Zhang C, Zhu H, Gu J, Deng G. Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm. Mathematics. 2018; 6(11):220. https://doi.org/10.3390/math6110220

Chicago/Turabian Style

Jiang, Tianhua; Zhang, Chao; Zhu, Huiqi; Gu, Jiuchun; Deng, Guanlong. 2018. "Energy-Efficient Scheduling for a Job Shop Using an Improved Whale Optimization Algorithm" Mathematics 6, no. 11: 220. https://doi.org/10.3390/math6110220

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