Robust Remanufacturing Planning with Parameter Uncertainty

TL;DR

This paper introduces a robust, data-driven framework for remanufacturing planning that accounts for statistical estimation errors in transition probabilities, improving worst-case performance and reliability.

Contribution

It develops a novel robust Markov decision process model with ambiguity sets for transition probabilities, enhancing decision robustness under data uncertainty.

Findings

Better worst-case performance in remanufacturing planning.

Higher reliability of performance guarantees.

Structural properties of optimal robust policies established.

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 probability distributions with high confidence. We further establish structural properties of optimal robust policies and insights for remanufacturing planning. A computational study on the NASA turbofan engine shows that our data-driven decision framework consistently yields better worst-case performances and higher reliability of the performance guarantee