Data-driven remanufacturing planning with parameter uncertainty

Zhicheng Zhu, Yisha Xiang, Ming Zhao and Yue Shi

European Journal of Operational Research, 2023, vol. 309, issue 1, 102-116

Abstract: We consider the problem of remanufacturing planning in the presence of statistical estimation errors. Determining the optimal remanufacturing timing, first and foremost, requires modeling of the state transitions of a system. The estimation of these probabilities, however, often suffers from data inadequacy and is far from accurate, resulting in serious degradation in performance. To mitigate the impacts of the uncertainty in transition probabilities, we develop a novel data-driven modeling framework for remanufacturing planning in which decision makers can remain robust with respect to statistical estimation errors. We model the remanufacturing planning problem as a robust Markov decision process, and construct ambiguity sets that contain the true transition probabilities with high confidence. We further establish structural properties of optimal robust policies and provide insights for remanufacturing planning. A computational study on the NASA turbofan engine shows that our data-driven robust decision framework consistently yields better out-of-sample reward and higher reliability of the performance guarantee, compared to the nominal model that uses the maximum likelihood estimates of the transition probabilities without considering parameter uncertainty.

Keywords: Maintenance; Remanufacturing planning; Robust Markov decision process; Control-limit policy (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0377221723000504
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:309:y:2023:i:1:p:102-116

DOI: 10.1016/j.ejor.2023.01.031

Access Statistics for this article

European Journal of Operational Research is currently edited by Roman Slowinski, Jesus Artalejo, Jean-Charles. Billaut, Robert Dyson and Lorenzo Peccati

More articles in European Journal of Operational Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().