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Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning

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  • S. Zhang

    (The University of Hong Kong)

  • T. N. Wong

    (The University of Hong Kong)

Abstract

This study develops an enhanced ant colony optimization (E-ACO) meta-heuristic to accomplish the integrated process planning and scheduling (IPPS) problem in the job-shop environment. The IPPS problem is represented by AND/OR graphs to implement the search-based algorithm, which aims at obtaining effective and near-optimal solutions in terms of makespan, job flow time and computation time taken. In accordance with the characteristics of the IPPS problem, the mechanism of ACO algorithm has been enhanced with several modifications, including quantification of convergence level, introduction of node-based pheromone, earliest finishing time-based strategy of determining the heuristic desirability, and oriented elitist pheromone deposit strategy. Using test cases with comprehensive consideration of manufacturing flexibilities, experiments are conducted to evaluate the approach, and to study the effects of algorithm parameters, with a general guideline for ACO parameter tuning for IPPS problems provided. The results show that with the specific modifications made on ACO algorithm, it is able to generate encouraging performance which outperforms many other meta-heuristics.

Suggested Citation

  • S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
    • Handle: RePEc:spr:joinma:v:29:y:2018:i:3:d:10.1007_s10845-014-1023-3
    DOI: 10.1007/s10845-014-1023-3
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    References listed on IDEAS

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    1. Kis, Tamas, 2003. "Job-shop scheduling with processing alternatives," European Journal of Operational Research, Elsevier, vol. 151(2), pages 307-332, December.
    2. 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.
    3. A Ławrynowicz, 2008. "Integration of production planning and scheduling using an expert system and a genetic algorithm," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 455-463, April.
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    Citations

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    Cited by:

    1. Sicheng Zhang & T.N. Wong, 2016. "Studying the impact of sequence-dependent set-up times in integrated process planning and scheduling with E-ACO heuristic," International Journal of Production Research, Taylor & Francis Journals, vol. 54(16), pages 4815-4838, August.
    2. Zhu, Xuedong & Son, Junbo & Zhang, Xi & Wu, Jianguo, 2023. "Constraint programming and logic-based Benders decomposition for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 117(C).
    3. Mengrui Zhu & Yun Yang & Xiaobing Feng & Zhengchun Du & Jianguo Yang, 2023. "Robust modeling method for thermal error of CNC machine tools based on random forest algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(4), pages 2013-2026, April.
    4. Qihao Liu & Xinyu Li & Liang Gao, 2021. "Mathematical modeling and a hybrid evolutionary algorithm for process planning," Journal of Intelligent Manufacturing, Springer, vol. 32(3), pages 781-797, March.
    5. Wenkang Zhang & Yufan Zheng & Rafiq Ahmad, 2023. "The integrated process planning and scheduling of flexible job-shop-type remanufacturing systems using improved artificial bee colony algorithm," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2963-2988, October.
    6. Chao Liu & Peifeng Niu & Guoqiang Li & Yunpeng Ma & Weiping Zhang & Ke Chen, 2018. "Enhanced shuffled frog-leaping algorithm for solving numerical function optimization problems," Journal of Intelligent Manufacturing, Springer, vol. 29(5), pages 1133-1153, June.
    7. Hyun Cheol Lee & Chunghun Ha, 2019. "Sustainable Integrated Process Planning and Scheduling Optimization Using a Genetic Algorithm with an Integrated Chromosome Representation," Sustainability, MDPI, vol. 11(2), pages 1-23, January.
    8. Chunghun Ha, 2020. "Evolving ant colony system for large-sized integrated process planning and scheduling problem considering sequence-dependent setup times," Flexible Services and Manufacturing Journal, Springer, vol. 32(3), pages 523-560, September.
    9. Zhang, Sicheng & Li, Xiang & Zhang, Bowen & Wang, Shouyang, 2020. "Multi-objective optimisation in flexible assembly job shop scheduling using a distributed ant colony system," European Journal of Operational Research, Elsevier, vol. 283(2), pages 441-460.
    10. G. Cherif & E. Leclercq & D. Lefebvre, 2023. "Scheduling of a class of partial routing FMS in uncertain environments with beam search," Journal of Intelligent Manufacturing, Springer, vol. 34(2), pages 493-514, February.
    11. Tomoko Sakiyama & Ikuo Arizono, 2018. "Coordination of Pheromone Deposition Might Solve Time-Constrained Travelling Salesman Problem," Complexity, Hindawi, vol. 2018, pages 1-5, December.
    12. Abdessamad Ait El Cadi & Omar Souissi & Rabie Ben Atitallah & Nicolas Belanger & Abdelhakim Artiba, 2018. "Mathematical programming models for scheduling in a CPU/FPGA architecture with heterogeneous communication delays," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 629-640, March.

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