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An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling

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  • Pan, Quan-Ke

Abstract

This paper addresses a new steelmaking-continuous casting (SCC) scheduling problem from iron and steel production processing. We model the problem as a combination of two coupled sub-problems. One sub-problem is a charge scheduling problem in a hybrid flowshop, and the other is a cast scheduling problem in parallel machines. To solve this SCC problem, we present a novel cooperative co-evolutionary artificial bee colony (CCABC) algorithm that has two sub-swarms, with each addressing a sub-problem. Problem-specific knowledge is used to construct an initial population, and an exploration strategy is introduced to guide the CCABC to promising regions during the search. To adapt the search operators in the classical artificial bee colony (ABC) to the cooperative co-evolution paradigm, an enhanced strategy for onlookers and a self-adaptive neighbourhood operator have been suggested. Extensive experiments based on both synthetic and real-world instances from an SCC process show the effectiveness of the proposed CCABC in solving the SCC scheduling problem.

Suggested Citation

  • Pan, Quan-Ke, 2016. "An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling," European Journal of Operational Research, Elsevier, vol. 250(3), pages 702-714.
    • Handle: RePEc:eee:ejores:v:250:y:2016:i:3:p:702-714
    DOI: 10.1016/j.ejor.2015.10.007
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    References listed on IDEAS

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    1. Tang, Lixin & Liu, Jiyin & Rong, Aiying & Yang, Zihou, 2000. "A mathematical programming model for scheduling steelmaking-continuous casting production," European Journal of Operational Research, Elsevier, vol. 120(2), pages 423-435, January.
    2. Bellabdaoui, A. & Teghem, J., 2006. "A mixed-integer linear programming model for the continuous casting planning," International Journal of Production Economics, Elsevier, vol. 104(2), pages 260-270, December.
    3. Ruiz, Ruben & Maroto, Concepcion, 2006. "A genetic algorithm for hybrid flowshops with sequence dependent setup times and machine eligibility," European Journal of Operational Research, Elsevier, vol. 169(3), pages 781-800, March.
    4. Lixin Tang & Gongshu Wang & Zhi-Long Chen, 2014. "Integrated Charge Batching and Casting Width Selection at Baosteel," Operations Research, INFORMS, vol. 62(4), pages 772-787, August.
    5. Tang, Lixin & Liu, Jiyin & Rong, Aiying & Yang, Zihou, 2001. "A review of planning and scheduling systems and methods for integrated steel production," European Journal of Operational Research, Elsevier, vol. 133(1), pages 1-20, August.
    6. Mao, Kun & Pan, Quan-ke & Pang, Xinfu & Chai, Tianyou, 2014. "A novel Lagrangian relaxation approach for a hybrid flowshop scheduling problem in the steelmaking-continuous casting process," European Journal of Operational Research, Elsevier, vol. 236(1), pages 51-60.
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    Cited by:

    1. Shuaipeng Yuan & Tieke Li & Bailin Wang, 2021. "A discrete differential evolution algorithm for flow shop group scheduling problem with sequence-dependent setup and transportation times," Journal of Intelligent Manufacturing, Springer, vol. 32(2), pages 427-439, February.
    2. Dayong Han & Qiuhua Tang & Zikai Zhang & Zixiang Li, 2020. "An Improved Migrating Birds Optimization Algorithm for a Hybrid Flow Shop Scheduling within Steel Plants," Mathematics, MDPI, vol. 8(10), pages 1-28, September.
    3. Fan Yang & Roel Leus, 2021. "Scheduling hybrid flow shops with time windows," Journal of Heuristics, Springer, vol. 27(1), pages 133-158, April.
    4. He, Xuan & Pan, Quan-Ke & Gao, Liang & Neufeld, Janis S., 2023. "An asymmetric traveling salesman problem based matheuristic algorithm for flowshop group scheduling problem," European Journal of Operational Research, Elsevier, vol. 310(2), pages 597-610.
    5. Tarun Kumar Sharma & Millie Pant, 2017. "Distribution in the placement of food in artificial bee colony based on changing factor," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(1), pages 159-172, March.
    6. Pan, Quan-Ke & Gao, Liang & Li, Xin-Yu & Gao, Kai-Zhou, 2017. "Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times," Applied Mathematics and Computation, Elsevier, vol. 303(C), pages 89-112.
    7. Torres, Nelson & Greivel, Gus & Betz, Joshua & Moreno, Eduardo & Newman, Alexandra & Thomas, Brian, 2024. "Optimizing steel coil production schedules under continuous casting and hot rolling," European Journal of Operational Research, Elsevier, vol. 314(2), pages 496-508.
    8. Jia Liu & Shuwei Wang, 2017. "Balancing Disassembly Line in Product Recovery to Promote the Coordinated Development of Economy and Environment," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    9. Pieter Moerloose & Broos Maenhout, 2023. "A two-stage local search heuristic for solving the steelmaking continuous casting scheduling problem with dual shared-resource and blocking constraints," Operational Research, Springer, vol. 23(1), pages 1-43, March.
    10. Cui, Yibing & Hu, Wei & Rahmani, Ahmed, 2023. "Fractional-order artificial bee colony algorithm with application in robot path planning," European Journal of Operational Research, Elsevier, vol. 306(1), pages 47-64.
    11. Hosseini, Amir & Otto, Alena & Pesch, Erwin, 2024. "Scheduling in manufacturing with transportation: Classification and solution techniques," European Journal of Operational Research, Elsevier, vol. 315(3), pages 821-843.

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