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A two-stage stochastic programming model for lot-sizing and scheduling under uncertainty

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  • Hu, Zhengyang
  • Hu, Guiping

Abstract

A lot-sizing and scheduling problem with sequence-dependent setups is addressed in this paper. In the production system, manufactories receive raw materials from upstream sites, and after production, the final products are shipped to downstream sites and customers. The key is to find a good production planning so that their cost is minimized. A two-stage stochastic programming model is formulated to minimize the total production, inventory and backorder costs. The first stage decides the baseline production including the production quantity of each product and the sequence of production while the second stage focuses on the possible updates of baseline production such as overtime production. The goal is to find the best sequence of production quantities under random demand with backorders allowed. Uncertainty is explicitly represented with a scenario tree then selecting the most representative scenarios in order to obtain a smaller subset while preserving essential properties. Both setup time and setup cost are product dependent. A case study for a manufacturing company producing braking equipment has been conducted to illustrate and validate the model. The results show that the stochastic model outperforms the deterministic model, especially when there are sufficient production resources.

Suggested Citation

  • Hu, Zhengyang & Hu, Guiping, 2016. "A two-stage stochastic programming model for lot-sizing and scheduling under uncertainty," International Journal of Production Economics, Elsevier, vol. 180(C), pages 198-207.
    • Handle: RePEc:eee:proeco:v:180:y:2016:i:c:p:198-207
    DOI: 10.1016/j.ijpe.2016.07.027
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    References listed on IDEAS

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    Cited by:

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    2. Sheng-I Chen & Delvinia Su, 2022. "A multi-stage stochastic programming model of lot-sizing and scheduling problems with machine eligibilities and sequence-dependent setups," Annals of Operations Research, Springer, vol. 311(1), pages 35-50, April.
    3. Franco Quezada & Céline Gicquel & Safia Kedad-Sidhoum, 2022. "Combining Polyhedral Approaches and Stochastic Dual Dynamic Integer Programming for Solving the Uncapacitated Lot-Sizing Problem Under Uncertainty," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 1024-1041, March.
    4. 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.
    5. Li, Xinchao & Lu, Shan & Li, Zhe & Wang, Yue & Zhu, Li, 2022. "Modeling and optimization of bioethanol production planning under hybrid uncertainty: A heuristic multi-stage stochastic programming approach," Energy, Elsevier, vol. 245(C).
    6. Máté Hegyháti & Krisztián Attila Bakon & Tibor Holczinger, 2023. "Optimization with uncertainties: a scheduling example," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 31(4), pages 1239-1263, December.
    7. Li, Jianbin & Liu, Lang & Luo, Xiaomeng & Zhu, Stuart X., 2023. "Interactive bundle pricing strategy for online pharmacies," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 177(C).
    8. Curcio, Eduardo & Amorim, Pedro & Zhang, Qi & Almada-Lobo, Bernardo, 2018. "Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty," International Journal of Production Economics, Elsevier, vol. 202(C), pages 81-96.

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