An extended mixed-integer programming formulation and dynamic cut generation approach for the stochastic lot sizing problem

Huseyin Tunc, Onur Kilic, S. Armagan Tarim, Roberto Rossi

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.
Original languageEnglish
Pages (from-to)492-506
JournalINFORMS Journal on Computing
Volume30
Issue number3
Early online date21 Sept 2018
DOIs
Publication statusE-pub ahead of print - 21 Sept 2018

Keywords / Materials (for Non-textual outputs)

  • stochastic lot sizing
  • static-dynamic uncertainty
  • extended formulation
  • dynamic cut generation

Fingerprint

Dive into the research topics of 'An extended mixed-integer programming formulation and dynamic cut generation approach for the stochastic lot sizing problem'. Together they form a unique fingerprint.

Cite this