Robust formulations for economic lot-sizing problem with remanufacturing

Öykü Naz Attila, Agostinho Agra, Kerem Akartunali, Ashwin Arulselvan

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
2 Downloads (Pure)


In this paper, we consider a lot-sizing problem with the remanufacturing option under parameter uncertainties imposed on demands and returns. Remanufacturing has recently been a fast growing area of interest for many researchers due to increasing awareness on reducing waste in production environments, and in particular studies involving remanufacturing and parameter uncertainties simultaneously are very scarce in the literature. We first present a min-max decomposition approach for this problem, where decision maker’s problem and adversarial problem are treated iteratively. Then, we propose two novel extended reformulations for the decision maker’s problem, addressing some of the computational challenges. An original aspect of the reformulations is that they are applied only to the latest scenario added to the decision maker’s problem. Then, we present an extensive computational analysis, which provides a detailed comparison of the three formulations and evaluates the impact of key problem parameters. We conclude that the proposed extended reformulations outperform the standard formulation for a majority of the instances. We also provide insights on the impact of the problem parameters on the computational performance.
Original languageEnglish
Pages (from-to)496-510
Number of pages15
JournalEuropean Journal of Operational Research
Issue number2
Early online date18 Jun 2020
Publication statusPublished - 16 Jan 2021


  • integer programming
  • lot-sizing
  • robust optimization
  • extended reformulations
  • decomposition


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