Optimization for Lot-Sizing Problems Under Uncertainty: A Data-Driven Perspective - Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. IFIP WG 5.7 International Conference. Part II Access content directly
Conference Papers Year : 2021

Optimization for Lot-Sizing Problems Under Uncertainty: A Data-Driven Perspective

Abstract

In a manufacturing context, the lot-sizing problems (LSP) determine the quantity to produce over a planning horizon. Often, the parameters used in the LSP models are unknown when the decisions are made, and this uncertainty has a critical impact on the quality of the decisions. However, the large amount of data that can nowadays be collected from the shop floor allows inferring information on the LSP parameters and their variability. Therefore, a recent research trend is to properly account for the uncertainty in the LSP optimization models. This work presents a survey on data-driven optimization approaches for the LSPs. We also provide a comparison of some promising optimization methodologies in the context of data-driven modeling of LSPs.
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Dates and versions

hal-03337325 , version 1 (07-09-2021)
hal-03337325 , version 2 (09-06-2023)

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Paula Metzker, Simon Thevenin, Yossiri Adulyasak, Alexandre Dolgui. Optimization for Lot-Sizing Problems Under Uncertainty: A Data-Driven Perspective. APMS 2021: IFIP Advances in Information and Communication Technology, Sep 2021, Nantes, France. pp.703 - 709, ⟨10.1007/978-3-030-85902-2_75⟩. ⟨hal-03337325v1⟩
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