The University of Southampton
University of Southampton Institutional Repository

Lot sizing with storage losses under demand uncertainty

Lot sizing with storage losses under demand uncertainty
Lot sizing with storage losses under demand uncertainty
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 heat production, in the context of which we compare the different approaches via computational experiments on real-world data.
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Koster, Arie
22c70cb3-4f20-4721-9694-1a45a623c2f8
Spiekermann, Nils
b770add8-31f6-4fa2-9cf8-d0fdf3b7fbc7
Coniglio, Stefano
03838248-2ce4-4dbc-a6f4-e010d6fdac67
Koster, Arie
22c70cb3-4f20-4721-9694-1a45a623c2f8
Spiekermann, Nils
b770add8-31f6-4fa2-9cf8-d0fdf3b7fbc7

Coniglio, Stefano, Koster, Arie and Spiekermann, Nils (2017) Lot sizing with storage losses under demand uncertainty Journal of Combinatorial Optimization

Record type: Article

Abstract

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 heat production, in the context of which we compare the different approaches via computational experiments on real-world data.

Text uploaded-to-pure - Accepted Manuscript
Download (83kB)
Text s10878-017-0147-8 - Version of Record
Available under License Creative Commons Attribution.
Download (530kB)

More information

Accepted/In Press date: 16 June 2017
e-pub ahead of print date: 4 July 2017

Identifiers

Local EPrints ID: 414901
URI: http://eprints.soton.ac.uk/id/eprint/414901
PURE UUID: 5476783a-6a13-4c2e-b060-a52c9f690c66

Catalogue record

Date deposited: 13 Oct 2017 16:30
Last modified: 19 Oct 2017 06:32

Export record

Contributors

Author: Arie Koster
Author: Nils Spiekermann

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×