On Robust Lot Sizing Problems with Storage Deterioration, with Applications to Heat and Power Cogeneration

Coniglio, Stefano; Koster, Arie M. C. A.; Spiekermann, Nils

doi:10.1007/978-3-319-45587-7_3

Cited by 2 publications

(1 citation statement)

References 11 publications

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“…This results in a significant solution time speed-up. For multistage lot sizing with storage losses (as typical for applications in the energy sector) and nonlinear objective, Coniglio et al (2016) propose a two-stage model under budgeted uncertainty with first-stage decisions on production and second-stage decisions on storage. Robust MP models for multistage inventory control are developed by Thorsen and Yao (2017) for budgeted uncertainty and based on the central limit theorem.…”

Section: Robust Optimization 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: Robust Optimization For Lot Sizingmentioning

confidence: 99%

Comparison of different approaches to multistage lot sizing with uncertain demand

Bindewald

Dunke

Nickel

2023

Int Trans Operational Res

View full text Add to dashboard Cite

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Lot sizing with storage losses under demand uncertainty

2017

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We address a variant of the single item lot sizing problem affected by proportional storage (or inventory) losses and uncertainty in the product demand. The problem has applications in, among others, the energy sector, where storage losses (or storage deteriorations) are often unavoidable and, due to the need for planning ahead, the demands can be largely uncertain. We first propose a two-stage robust optimization approach with second-stage storage variables, showing how the arising robust problem can be solved as an instance of the deterministic one. We then consider a two-stage approach where not only the storage but also the production variables are determined in the second stage. After showing that, in the general case, solutions to this problem can suffer from acausality (or anticipativity), we introduce a flexible affine rule approach which, albeit restricting the solution set, allows for causal production plans. A hybrid robust-stochastic approach where the objective function is optimized in expectation, as opposed to in the worst-case, while retaining robust optimization guarantees of feasibility in the worst-case, is also discussed. We conclude with an application to This work is partially supported by the German Federal Ministry for Economic Affairs and Energy, BMWi, Grant 03ET7528B. Comb Optim (2018) 36:763-788 heat production, in the context of which we compare the different approaches via computational experiments on real-world data.

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On Robust Lot Sizing Problems with Storage Deterioration, with Applications to Heat and Power Cogeneration

Cited by 2 publications

References 11 publications

Comparison of different approaches to multistage lot sizing with uncertain demand

Comparison of different approaches to multistage lot sizing with uncertain demand

Lot sizing with storage losses under demand uncertainty

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