IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v29y2018i3d10.1007_s10845-014-1023-3.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-014-1023-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-014-1023-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xu Zhang & Zhixue Liao & Lichao Ma & Jin Yao, 2022. "Hierarchical multistrategy genetic algorithm for integrated process planning and scheduling," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 223-246, January.
    2. Barzanji, Ramin & Naderi, Bahman & Begen, Mehmet A., 2020. "Decomposition algorithms for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 93(C).
    3. Servranckx, Tom & Vanhoucke, Mario, 2019. "Strategies for project scheduling with alternative subgraphs under uncertainty: similar and dissimilar sets of schedules," European Journal of Operational Research, Elsevier, vol. 279(1), pages 38-53.
    4. 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.
    5. Bürgy, Reinhard & Bülbül, Kerem, 2018. "The job shop scheduling problem with convex costs," European Journal of Operational Research, Elsevier, vol. 268(1), pages 82-100.
    6. Linda Zhang & Carman K.M. Lee & Pervaiz Akhtar, 2020. "Towards customization: Evaluation of integrated sales, product, and production configuration," Post-Print hal-03276827, HAL.
    7. Burdett, R.L. & Kozan, E., 2010. "A disjunctive graph model and framework for constructing new train schedules," European Journal of Operational Research, Elsevier, vol. 200(1), pages 85-98, January.
    8. Xue, Guisen & Felix Offodile, O. & Zhou, Hong & Troutt, Marvin D., 2011. "Integrated production planning with sequence-dependent family setup times," International Journal of Production Economics, Elsevier, vol. 131(2), pages 674-681, June.
    9. Tamssaouet, Karim & Dauzère-Pérès, Stéphane, 2023. "A general efficient neighborhood structure framework for the job-shop and flexible job-shop scheduling problems," European Journal of Operational Research, Elsevier, vol. 311(2), pages 455-471.
    10. Toorajipour, Reza & Sohrabpour, Vahid & Nazarpour, Ali & Oghazi, Pejvak & Fischl, Maria, 2021. "Artificial intelligence in supply chain management: A systematic literature review," Journal of Business Research, Elsevier, vol. 122(C), pages 502-517.
    11. Zhang, Linda L. & Lee, Carman K.M. & Akhtar, Pervaiz, 2020. "Towards customization: Evaluation of integrated sales, product, and production configuration," International Journal of Production Economics, Elsevier, vol. 229(C).
    12. Ławrynowicz Anna, 2011. "Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems," Foundations of Management, Sciendo, vol. 3(2), pages 7-26, January.
    13. Servranckx, Tom & Vanhoucke, Mario, 2019. "A tabu search procedure for the resource-constrained project scheduling problem with alternative subgraphs," European Journal of Operational Research, Elsevier, vol. 273(3), pages 841-860.
    14. Ma, Yujie & Du, Gang & Jiao, Roger J., 2020. "Optimal crowdsourcing contracting for reconfigurable process planning in open manufacturing: A bilevel coordinated optimization approach," International Journal of Production Economics, Elsevier, vol. 228(C).
    15. Drótos, Márton & Erdos, Gábor & Kis, Tamás, 2009. "Computing lower and upper bounds for a large-scale industrial job shop scheduling problem," European Journal of Operational Research, Elsevier, vol. 197(1), pages 296-306, August.
    16. 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.
    17. Rossit, Daniel Alejandro & Tohmé, Fernando & Frutos, Mariano, 2018. "The Non-Permutation Flow-Shop scheduling problem: A literature review," Omega, Elsevier, vol. 77(C), pages 143-153.
    18. 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.
    19. Zhang, Luping & Wong, T.N., 2015. "An object-coding genetic algorithm for integrated process planning and scheduling," European Journal of Operational Research, Elsevier, vol. 244(2), pages 434-444.
    20. Čapek, R. & Šůcha, P. & Hanzálek, Z., 2012. "Production scheduling with alternative process plans," European Journal of Operational Research, Elsevier, vol. 217(2), pages 300-311.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:29:y:2018:i:3:d:10.1007_s10845-014-1023-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.