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Multitask and Multistage Production Planning and Scheduling for Process Industries

Author

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  • Francesco Gaglioppa

    (Graduate Program in Industrial and Systems Engineering, Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota 55455)

  • Lisa A. Miller

    (Graduate Program in Industrial and Systems Engineering, Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota 55455)

  • Saif Benjaafar

    (Graduate Program in Industrial and Systems Engineering, Department of Mechanical Engineering, University of Minnesota, Minneapolis, Minnesota 55455)

Abstract

We consider the planning and scheduling of production in a multitask/multistage batch manufacturing process typical of industries such as chemical manufacturing, food processing, and oil refining. We allow instances in which multiple sequences of tasks may be used to produce end products. We formulate the problem as a mixed-integer linear program and show that the linear programming relaxation has a large integrality gap and requires significant computational effort to solve to optimality for large instances. Using echelon inventory, we construct a new family of valid inequalities for this problem. The formulation with the additional constraints leads to a significantly tighter linear programming relaxation and to greatly reduced solution times for the mixed-integer linear program.

Suggested Citation

  • Francesco Gaglioppa & Lisa A. Miller & Saif Benjaafar, 2008. "Multitask and Multistage Production Planning and Scheduling for Process Industries," Operations Research, INFORMS, vol. 56(4), pages 1010-1025, August.
    • Handle: RePEc:inm:oropre:v:56:y:2008:i:4:p:1010-1025
    DOI: 10.1287/opre.1080.0525
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    References listed on IDEAS

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    Cited by:

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    3. Ming Zhao & Minjiao Zhang, 2020. "Multiechelon Lot Sizing: New Complexities and Inequalities," Operations Research, INFORMS, vol. 68(2), pages 534-551, March.
    4. Jing, Fuying & Chao, Xiangrui, 2022. "Forecast horizons for a two-echelon dynamic lot-sizing problem," Omega, Elsevier, vol. 110(C).
    5. Minjiao Zhang & Simge Küçükyavuz & Hande Yaman, 2012. "A Polyhedral Study of Multiechelon Lot Sizing with Intermediate Demands," Operations Research, INFORMS, vol. 60(4), pages 918-935, August.
    6. Ayşe N. Arslan & Jean‐Philippe P. Richard & Yongpei Guan, 2016. "On the polyhedral structure of two‐level lot‐sizing problems with supplier selection," Naval Research Logistics (NRL), John Wiley & Sons, vol. 63(8), pages 647-666, December.
    7. Leachman, Robert C. & Johnston, Lenrick & Li, Shan & Shen, Zuo-Jun, 2014. "An automated planning engine for biopharmaceutical production," European Journal of Operational Research, Elsevier, vol. 238(1), pages 327-338.
    8. Lingxiu Dong & Panos Kouvelis & Xiaole Wu, 2014. "The Value of Operational Flexibility in the Presence of Input and Output Price Uncertainties with Oil Refining Applications," Management Science, INFORMS, vol. 60(12), pages 2908-2926, December.

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