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More MILP models for integrated process planning and scheduling

Author

Listed:
  • Liangliang Jin
  • Qiuhua Tang
  • Chaoyong Zhang
  • Xinyu Shao
  • Guangdong Tian

Abstract

The integration of process planning and scheduling is important for an efficient utilisation of manufacturing resources. In general, there are two types of models for this problem. Although some MILP models have been reported, most existing models belong to the first type and they cannot realise a true integration of process planning and scheduling. Especially, they are completely powerless to deal with the cases where jobs are expressed by network graphs because generating all the process plans from a network graph is difficult and inefficient. The network graph-specific models belong to the other type, and they have seldom been deliberated on. In this research, some novel MILP models for integrated process planning and scheduling in a job shop flexible manufacturing system are developed. By introducing some network graph-oriented constraints to accommodate different operation permutations, the proposed models are able to express and utilise flexibilities contained in network graphs, and hence have the power to solve network graph-based instances. The established models have been tested on typical test bed instances to verify their correctness. Computational results show that this research achieves the anticipant purpose: the proposed models are capable of solving network graph-based instances.

Suggested Citation

  • Liangliang Jin & Qiuhua Tang & Chaoyong Zhang & Xinyu Shao & Guangdong Tian, 2016. "More MILP models for integrated process planning and scheduling," International Journal of Production Research, Taylor & Francis Journals, vol. 54(14), pages 4387-4402, July.
  • Handle: RePEc:taf:tprsxx:v:54:y:2016:i:14:p:4387-4402
    DOI: 10.1080/00207543.2016.1140917
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    References listed on IDEAS

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    1. 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.
    2. Yumin He & Ram Rachamadugu & Milton L. Smith & Kathryn E. Stecke, 2015. "Segment set-based part input sequencing in flexible manufacturing systems," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5106-5117, September.
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    4. Alan S. Manne, 1960. "On the Job-Shop Scheduling Problem," Operations Research, INFORMS, vol. 8(2), pages 219-223, April.
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    Cited by:

    1. 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).
    2. Jin Huang & Liangliang Jin & Chaoyong Zhang, 2017. "Mathematical Modeling and a Hybrid NSGA-II Algorithm for Process Planning Problem Considering Machining Cost and Carbon Emission," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    3. Ke Yang & Dazhi Pan, 2023. "An Improved Mayfly Optimization Algorithm for Type-2 Multi-Objective Integrated Process Planning and Scheduling," Mathematics, MDPI, vol. 11(20), pages 1-19, October.
    4. Barzanji, Ramin & Naderi, Bahman & Begen, Mehmet A., 2020. "Decomposition algorithms for the integrated process planning and scheduling problem," Omega, Elsevier, vol. 93(C).

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