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Hybrid stochastic and robust optimization model for lot-sizing and scheduling problems under uncertainties

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  • Hu, Zhengyang
  • Hu, Guiping

Abstract

Uncertainty is among the significant concerns in production scheduling. It has become increasingly important to take uncertainties into consideration for lot-sizing and scheduling. In this paper, we adopt the Hybrid Stochastic and Robust Optimization (HSRO) approach in lot-sizing and scheduling problems in which suppliers have the flexibility of satisfying a fraction of demand based on the market and their policies. Two types of uncertainties have been considered simultaneously: demand and overtime processing cost. Robust optimization is adopted for uncertain demand and Sample Average Approximation (SAA) technique is applied to solve the stochastic program for uncertain overtime processing cost. Numerical results based on a manufacturing company has been conducted to not only validate the proposed hybrid model but also quantitatively demonstrate the merit of our approach. Sample size stability test and sensitivity analyses on various parameters have also been conducted.

Suggested Citation

  • Hu, Zhengyang & Hu, Guiping, 2020. "Hybrid stochastic and robust optimization model for lot-sizing and scheduling problems under uncertainties," European Journal of Operational Research, Elsevier, vol. 284(2), pages 485-497.
    • Handle: RePEc:eee:ejores:v:284:y:2020:i:2:p:485-497
    DOI: 10.1016/j.ejor.2019.12.030
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    References listed on IDEAS

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    Cited by:

    1. Wu, Wei & Hayashi, Takito & Haruyasu, Kato & Tang, Liang, 2023. "Exact algorithms based on a constrained shortest path model for robust serial-batch and parallel-batch scheduling problems," European Journal of Operational Research, Elsevier, vol. 307(1), pages 82-102.
    2. Zhengyang Hu & Viren Parwani & Guiping Hu, 2021. "Closed-Loop Supply Chain Network Design under Uncertainties Using Fuzzy Decision Making," Logistics, MDPI, vol. 5(1), pages 1-16, March.
    3. Hu, Yuting & Li, Shukai & Dessouky, Maged M. & Yang, Lixing & Gao, Ziyou, 2022. "Computationally efficient train timetable generation of metro networks with uncertain transfer walking time to reduce passenger waiting time: A generalized Benders decomposition-based method," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 210-231.
    4. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).

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