A multi-objective discrete differential evolution algorithm for energy-efficient distributed blocking flow shop scheduling problem

ABSTRACTThe energy problem in green manufacturing has attracted enormous attention from researchers and practitioners in the manufacturing domain with the global energy crisis and the aggravation of environmental pollution. The distributed blocking flow shop scheduling problem (DBFSP) has considerable application scenarios in connection with its widespread application in the industry under the background of intelligent manufacturing. A multi-objective discrete differential evolution (MODE) algorithm is proposed to solve the energy-efficient distributed blocking flow shop scheduling problem (EEDBFSP) with the objectives of the makespan and total energy consumption (TEC) in this paper. The cooperative initialisation strategy is proposed to generate the initial population of the EEDBFSP. The mutation, crossover, and selection operators are redesigned to enable the MODE algorithm as applied to discrete space. A local search strategy based on the knowledge of five operators is introduced to enhance the exploitation capability of the MODE algorithm in the EEDBFSP. The non-critical path energy-efficient strategy is proposed to reduce energy consumption according to the specific constraints in the EEDBFSP. The effectiveness of each strategy in the MODE algorithm is verified and compared with the state-of-the-art algorithms. The numerical results demonstrate that the MODE algorithm is the efficient optimiser for solving the EEDBFSP.KEYWORDS: Energy-efficient distributed schedulingblocking flow shopmulti-objectivediscrete differential evolutiontotal energy consumption (TEC) Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data are openly available in ‘CSDN’ at https://download.csdn.net/download/weixin_45627438/85802283.Additional informationFundingThis work was financially supported by the National Natural Science Foundation of China under grant 62063021. It was also supported by the Key Program of National Natural Science Foundation of Gansu Province under Grant 23JRRA784, the High-level Foreign Experts Project of Gansu Province under Grant 22JR10KA007, the Key Research Programs of Science and Technology Commission Foundation of Gansu Province (21YF5WA086), Lanzhou Science Bureau project (2018-rc-98), and Project of Gansu Natural Science Foundation (21JR7RA204), respectively.Notes on contributorsFuqing ZhaoFuqing Zhao received the B.Sc. and Ph.D. degrees from the Lanzhou University of Technology, Lanzhou, China, in 1994 and 2006, respectively. Since 1998, he has been with the School of Computer Science Department, Lanzhou University of Technology, Lanzhou, China, where he became a Full Professor in 2012. He has been as the post Doctor with the State Key Laboratory of Manufacturing System Engineering, Xi’an Jiaotong University, Xi’an, China in 2009. He has been as a visiting scholar in Exeter Manufacturing Enterprise Center in Exeter University and Georgia Tech Manufacturing Institute in Georgia Institute of Technology from 2008–2019 to 2014–2015 respectively. He has authored two academic book and over 90 refereed papers. His current research interests include intelligent optimisation and scheduling.Hui ZhangHui Zhang received the B.S. degree in information management and information system from the School of Computer Science and Technology, Taizhou University, Taizhou, China, in 2020. She received the M.S. degree in computer technology from the School of Computer and Communication Technology, Lanzhou University of Technology, Lanzhou, China, in 2023. She is currently working towards the Ph.D. degree at the School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China. Her current research interests include intelligent optimisation and scheduling algorithms.Ling WangLing Wang received the B.Sc. in automation and Ph.D. in control theory and control engineering from Tsinghua University, Beijing, China, in 1995 and 1999, respectively. Since 1999, he has been with the Department of Automation, Tsinghua University, where he became a full professor in 2008. His current research interests include intelligent optimisation and production scheduling. He has authored five academic books and more than 300 refereed papers. Professor Wang is a recipient of the National Natural Science Fund for Distinguished Young Scholars of China, the National Natural Science Award (Second Place) in 2014, the Science and Technology Award of Beijing City in 2008, the Natural Science Award (First Place in 2003, and Second Place in 2007) nominated by the Ministry of Education of China. Professor Wang now is the Editor-in-Chief of International Journal of Automation and Control, and the Associate Editor of IEEE Transactions on Evolutionary Computation, Swarm and Evolutionary Computation, etc.Tianpeng XuTianpeng Xu graduated from the School of Information Science and Engineering of Lanzhou University in 2005. He received the B.S. from School of Electrical and Information, Lanzhou University of Technology, Lanzhou, China, in 2013. From then on, he has been working at Lanzhou University of Technology. At present, he is pursuing his PhD in manufacturing information system, and his current main research direction is intelligent optimisation and scheduling.Ningning ZhuNingning Zhu received the M.S. degree from School of Computer and Communication, Lanzhou University of Technology, Lanzhou, China, in 2012, where she is currently pursuing the Ph.D. degree in manufacturing information system. Her current research interests include intelligent optimisation and scheduling algorithms.Jonrinaldi JonrinaldiJonrinaldi Jonrinaldi received the PhD degree from University of Exeter, Exeter, UK in 2012. He is now the chair at the Department of Industrial Engineering, Universitas Andalas, Indonesia. He had published more than 40 refereed papers, and his research interests include logistics, inventory management, production/operations, and supply chain optimisation.