Stochastic program for disassembly lot-sizing under uncertain component refurbishing lead times

Planning disassembly operations for a given demand in components is challenging in practice because the quality of recovered components is very uncertain, and thus the duration of refurbishing operations is unpredictable. In this paper, we address the capacitated disassembly lot-sizing problems under uncertain refurbishing durations. More precisely, we consider a two-level disassembly system with a single type of end-of-life product, a dynamic demand, and stochastic refurbishing lead times for all components. To deal with the static decision frameworks, this problem is modeled as a two-stage stochastic Mixed-Integer Linear Program (MILP), where the objective is to minimize the expected total cost. To alleviate the scalability issues, we propose a reformulation of the inventory constraint that significantly reduces the number of scenarios. In addition, to solve large scale problems, we couple this reformulation with Monte-Carlo sampling. We provide a rolling horizon approach to deal with the static decision framework, where disassembly decisions are updated when new information unfolds. Experimental results show the effectiveness of the proposed models and the convergence of the resulting Sample Average Approximation (SAA) estimator. (C) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper addresses the capacitated disassembly lot-sizing problems under uncertain refurbishing durations. By modeling the problem as a two-stage stochastic Mixed-Integer Linear Program (MILP) and utilizing a reformulation of the inventory constraint and Monte-Carlo sampling, scalability issues are effectively alleviated. Experimental results demonstrate the effectiveness of the proposed models and the convergence of the resulting Sample Average Approximation (SAA) estimator.