Adaptation and approximate strategies for solving the lot-sizing and scheduling problem under multistage demand uncertainty

Eduardo Curcio, Pedro Amorim, Qi Zhang, Bernardo Almada-Lobo

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

This work addresses the lot-sizing and scheduling problem under multistage demand uncertainty. A flexible production system is considered, with the possibility to adjust the size and the schedule of lots in every time period based on a rolling-horizon planning scheme. Computationally intractable multistage stochastic programming models are often employed on this problem. An adaptation strategy to the multistage setting for two-stage programming and robust optimization models is proposed. We also present an approximate heuristic strategy to address the problem more efficiently, relying on multistage stochastic programming and adjustable robust optimization. In order to evaluate each strategy and model proposed, a Monte Carlo simulation experiment under a rolling-horizon scheme is performed. Results show that the strategies are promising in solving large-scale problems: the approximate strategy based on adjustable robust optimization has, on average, 6.72% better performance and is 7.9 times faster than the deterministic model.

Original languageEnglish (US)
Pages (from-to)81-96
Number of pages16
JournalInternational Journal of Production Economics
Volume202
DOIs
StatePublished - Aug 2018

Bibliographical note

Funding Information:
This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project “POCI-01-0145-FEDER-006961”, and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. The first author would like also to acknowledge the support of the Erasmus Mundus Programme .

Funding Information:
This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project “POCI-01-0145-FEDER-006961” and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013. The first author would like also to acknowledge the support of the Erasmus Mundus Programme.

Publisher Copyright:
© 2018 Elsevier B.V.

Keywords

  • Adjustable robust optimization
  • GLSP
  • Lot-sizing and scheduling problem
  • Multistage stochastic programming
  • Rolling-horizon

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