An Extended Mixed-Integer Programming Formulation and Dynamic Cut Generation Approach for the Stochastic Lot-Sizing Problem

Huseyin Tunc (), Onur A. Kilic (), S. Armagan Tarim () and Roberto Rossi ()
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Huseyin Tunc: Department of Policy and Strategy Studies, Hacettepe University, Ankara, Turkey
Onur A. Kilic: Department of Operations, University of Groningen, 9700 AV Groningen, Netherlands
S. Armagan Tarim: Department of Management, Cankaya University, Ankara, 06800 Turkey; Cork University Business School, University College Cork, T12 K8AF, Ireland
Roberto Rossi: Business School, University of Edinburgh, Edinburgh EH8 9JS, United Kingdom

INFORMS Journal on Computing, 2018, vol. 30, issue 3, 492-506

Abstract: We present an extended mixed-integer programming formulation of the stochastic lot-sizing problem for the static-dynamic uncertainty strategy. The proposed formulation is significantly more time efficient as compared to existing formulations in the literature and it can handle variants of the stochastic lot-sizing problem characterized by penalty costs and service level constraints, as well as backorders and lost sales. Also, besides being capable of working with a predefined piecewise linear approximation of the cost function—as is the case in earlier formulations—it has the functionality of finding an optimal cost solution with an arbitrary level of precision by means of a novel dynamic cut generation approach.

Keywords: stochastic lot sizing; static-dynamic uncertainty; extended formulation; dynamic cut generation (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:30:y:2018:i:3:p:492-506

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