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Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines

Author

Listed:
  • Boxuan Zhao

    (Xi’an Jiaotong University)

  • Jianmin Gao

    (Xi’an Jiaotong University)

  • Kun Chen

    (Xi’an Jiaotong University)

  • Ke Guo

    (Xi’an Jiaotong University)

Abstract

The flexibilities of alternative process plans and unrelated parallel machines are benefit for the optimization of the job shop scheduling problem, but meanwhile increase the complexity of the problem. This paper constructs the mathematical model for the multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines, splits the problem into two sub-problems, namely flexible processing route decision and task sorting, and proposes a two-generation (father and children) Pareto ant colony algorithm to generate a feasible scheduling solution. The father ant colony system solves the flexible processing route decision problem, which selects the most appropriate process node set from the alternative process node set. The children ant colony system solves the sorting problem of the process task set generated by the father ant colony system. The Pareto ant colony system constructs the applicable pheromone matrixes and heuristic information with respect to the sub-problems and objectives. And NSGAII is used as comparison whose genetic operators are re-defined. The experiment confirms the validation of the proposed algorithm. By comparing the result of the algorithm to NSGAII, we can see the proposed algorithm has a better performance.

Suggested Citation

  • Boxuan Zhao & Jianmin Gao & Kun Chen & Ke Guo, 2018. "Two-generation Pareto ant colony algorithm for multi-objective job shop scheduling problem with alternative process plans and unrelated parallel machines," Journal of Intelligent Manufacturing, Springer, vol. 29(1), pages 93-108, January.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:1:d:10.1007_s10845-015-1091-z
    DOI: 10.1007/s10845-015-1091-z
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    References listed on IDEAS

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    1. Moncayo-Martínez, Luis A. & Zhang, David Z., 2011. "Multi-objective ant colony optimisation: A meta-heuristic approach to supply chain design," International Journal of Production Economics, Elsevier, vol. 131(1), pages 407-420, May.
    2. P. Mohapatra & A. Nayak & S.K. Kumar & M.K. Tiwari, 2015. "Multi-objective process planning and scheduling using controlled elitist non-dominated sorting genetic algorithm," International Journal of Production Research, Taylor & Francis Journals, vol. 53(6), pages 1712-1735, March.
    3. Garcia-Martinez, C. & Cordon, O. & Herrera, F., 2007. "A taxonomy and an empirical analysis of multiple objective ant colony optimization algorithms for the bi-criteria TSP," European Journal of Operational Research, Elsevier, vol. 180(1), pages 116-148, July.
    4. Karl Doerner & Walter Gutjahr & Richard Hartl & Christine Strauss & Christian Stummer, 2004. "Pareto Ant Colony Optimization: A Metaheuristic Approach to Multiobjective Portfolio Selection," Annals of Operations Research, Springer, vol. 131(1), pages 79-99, October.
    5. Li, Xinyu & Shao, Xinyu & Gao, Liang & Qian, Weirong, 2010. "An effective hybrid algorithm for integrated process planning and scheduling," International Journal of Production Economics, Elsevier, vol. 126(2), pages 289-298, August.
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    Cited by:

    1. Ying Sun & Jeng-Shyang Pan & Pei Hu & Shu-Chuan Chu, 2023. "Enhanced Equilibrium Optimizer algorithm applied in job shop scheduling problem," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 1639-1665, April.
    2. Xiaoyu Wen & Qingbo Song & Yunjie Qian & Dongping Qiao & Haoqi Wang & Yuyan Zhang & Hao Li, 2023. "Effective Improved NSGA-II Algorithm for Multi-Objective Integrated Process Planning and Scheduling," Mathematics, MDPI, vol. 11(16), pages 1-17, August.

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