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

Paula Metzker (1, 2, 3, 4) , Simon Thevenin (2, 4) , Yossiri Adulyasak (1) , Alexandre Dolgui (2, 4)

1 HEC Montréal - HEC Montréal
2 LS2N - équipe SLP - Systèmes Logistiques et de Production
3 GERAD - Groupe d’études et de recherche en analyse des décisions
4 IMT Atlantique - DAPI - Département Automatique, Productique et Informatique

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.

Mots clés: Data-driven optimization; Lot-sizing problem; Optimization under uncertainties

Domaines: Combinatoire [math.CO]

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⟩