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

Tianhua Jiang, Chao Zhang, Huiqi Zhu, Jiuchun Gu and Guanlong Deng
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Tianhua Jiang: School of Transportation, Ludong University, Yantai 264025, China
Chao Zhang: Department of Computer Science and Technology, Henan Institute of Technology, Xinxiang 453003, China
Huiqi Zhu: School of Transportation, Ludong University, Yantai 264025, China
Jiuchun Gu: School of Transportation, Ludong University, Yantai 264025, China
Guanlong Deng: School of Information and Electrical Engineering, Ludong University, Yantai 264025, China

Mathematics, 2018, vol. 6, issue 11, 1-16

Abstract: 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 CO 2 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.

Keywords: energy-efficient job shop scheduling; dispatching rule; nonlinear convergence factor; mutation operation; whale optimization algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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