Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem |
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| Z. Y. Zhao, S. X. Liu, M. C. Zhou, and A. Abusorrah, "Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1199-1209, Jun. 2021. doi: 10.1109/JAS.2020.1003539 |
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| Authors: | Ziyan Zhao Shixin Liu MengChu Zhou Abdullah Abusorrah |
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| Affiliation: | 1. State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;2. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark NJ 07102 USA;3. State Key Laboratory of Synthetical Automation for Process Industries and the College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;4. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark NJ 07102 USA;5. Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21481, Saudi Arabia;6. Department of Electrical and Computer Engineering, Faculty of Engineering, and Center of Research Excellence in Renewable Energy and Power Systems, King Abdulaziz University, Jeddah 21481, Saudi Arabia |
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| Abstract: | Group scheduling problems have attracted much attention owing to their many practical applications. This work proposes a new bi-objective serial-batch group scheduling problem considering the constraints of sequence-dependent setup time, release time, and due time. It is originated from an important industrial process, i.e., wire rod and bar rolling process in steel production systems. Two objective functions, i.e., the number of late jobs and total setup time, are minimized. A mixed integer linear program is established to describe the problem. To obtain its Pareto solutions, we present a memetic algorithm that integrates a population-based nondominated sorting genetic algorithm II and two single-solution-based improvement methods, i.e., an insertion-based local search and an iterated greedy algorithm. The computational results on extensive industrial data with the scale of a one-week schedule show that the proposed algorithm has great performance in solving the concerned problem and outperforms its peers. Its high accuracy and efficiency imply its great potential to be applied to solve industrial-size group scheduling problems. |
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| Keywords: | Insertion-based local search iterated greedy algorithm machine learning memetic algorithm nondominated sorting genetic algorithm II (NSGA-II) production scheduling |
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