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Volume 8 Issue 6
Jun.  2021

IEEE/CAA Journal of Automatica Sinica

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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
Citation: 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

Dual-Objective Mixed Integer Linear Program and Memetic Algorithm for an Industrial Group Scheduling Problem

doi: 10.1109/JAS.2020.1003539
Funds:  This work was supported by the China Scholarship Council Scholarship, the National Key Research and Development Program of China (2017YFB0306400), the National Natural Science Foundation of China (62073069) and the Deanship of Scientific Research (DSR) at King Abdulaziz University (RG-48-135-40)
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  • 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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    Highlights

    • This work tackles a new bi-objective group scheduling problem arising from a real-world steel production system.
    • It optimizes two objectives, i.e., minimizing the number of late jobs and minimizing total setup time.
    • It considers the constraints of release time, due time, and sequence-dependent setup time.
    • This work establishes a mixed-integer linear program to describe the concerned problem in a rigorous way.
    • By integrating a nondominated sorting genetic algorithm II (NSGA-II) and iterated greedy algorithm (IGA)-based local search operators, this work designs a multi-objective memetic algorithm to solve the scheduling problem well.

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