A multi-stage stochastic programming model of lot-sizing and scheduling problems with machine eligibilities and sequence-dependent setups

Chen, Sheng I.; Su, Delvinia

doi:10.1007/s10479-019-03462-1

Cited by 16 publications

(1 citation statement)

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“…A hybrid robust-stochastic approach originates from Coniglio et al (2018) for the case of storage losses (as typical for energy applications) where the objective is to minimize expected costs, but recourse actions ensure worst-case feasibility. Likewise, lot sizing and scheduling with machine eligibility and sequence-dependent setups are considered by Chen and Su (2022) through multistage stochastic programming and a related analysis of classical measures (expected value of perfect information, value of the stochastic solution). Multistage lot sizing embedded within a rolling horizon procedure of a material requirements planning setting is studied experimentally in Thevenin et al (2021).…”

Section: Stochastic Programming For Lot Sizingmentioning

confidence: 99%

Comparison of different approaches to multistage lot sizing with uncertain demand

Bindewald

Dunke

Nickel

2023

Int Trans Operational Res

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We study a new variant of the classical lot sizing problem with uncertain demand where neither the planning horizon nor demands are known exactly. This situation arises in practice when customer demands arriving over time are confirmed rather lately during the transportation process. In terms of planning, this setting necessitates a rolling horizon procedure where the overall multistage problem is dissolved into a series of coupled snapshot problems under uncertainty. Depending on the available data and risk disposition, different approaches from online optimization, stochastic programming, and robust optimization are viable to model and solve the snapshot problems. We evaluate the impact of the selected methodology on the overall solution quality using a methodology‐agnostic framework for multistage decision‐making under uncertainty. We provide computational results on lot sizing within a rolling horizon regarding different types of uncertainty, solution approaches, and the value of available information about upcoming demands.

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Section: Stochastic Programming For Lot Sizingmentioning

confidence: 99%