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Two-stage stochastic lot-sizing problem under cost uncertainty

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  • Zhili Zhou
  • Yongpei Guan

Abstract

For production planning problems, cost parameters can be uncertain due to marketing activities and interest rate fluctuation. In this paper, we consider a single-item two-stage stochastic lot-sizing problem under cost parameter uncertainty. Assuming cost parameters will increase or decrease after time period p each with certain probability, we minimize the total expected cost for a finite horizon problem. We develop an extended linear programming formulation in a higher dimensional space that can provide integral solutions by showing that its constraint matrix is totally unimodular. We also project this extended formulation to a lower dimensional space and obtain a corresponding extended formulation in the lower dimensional space. Final computational experiments demonstrate that the extended formulation is more efficient and performs more stable than the two-stage stochastic mixed-integer programming formulation. Copyright Springer Science+Business Media New York 2013

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  • Zhili Zhou & Yongpei Guan, 2013. "Two-stage stochastic lot-sizing problem under cost uncertainty," Annals of Operations Research, Springer, vol. 209(1), pages 207-230, October.
    • Handle: RePEc:spr:annopr:v:209:y:2013:i:1:p:207-230:10.1007/s10479-013-1333-4
    DOI: 10.1007/s10479-013-1333-4
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    References listed on IDEAS

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    Cited by:

    1. Yongjian Li & Xiaoqiang Cai & Lei Xu & Wenxia Yang, 2016. "Heuristic approach on dynamic lot-sizing model for durable products with end-of-use constraints," Annals of Operations Research, Springer, vol. 242(2), pages 265-283, July.
    2. Fernando Rojas & Víctor Leiva & Peter Wanke & Camilo Lillo & Jimena Pascual, 2019. "Modeling lot-size with time-dependent demand based on stochastic programming and case study of drug supply in Chile," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-24, March.
    3. Kris Lieckens & Nico Vandaele, 2016. "Differential evolution to solve the lot size problem in stochastic supply chain management systems," Annals of Operations Research, Springer, vol. 242(2), pages 239-263, July.
    4. Songsong Chen & Feixiang Gong & Mingqiang Zhang & Jindou Yuan & Siyang Liao & Hongyin Chen & Dezhi Li & Shiming Tian & Xiaojian Hu, 2021. "Planning and Scheduling for Industrial Demand-Side Management: State of the Art, Opportunities and Challenges under Integration of Energy Internet and Industrial Internet," Sustainability, MDPI, vol. 13(14), pages 1-18, July.

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