Design of product recovery networks under uncertainty

Abstract

The design of product recovery networks has received growing attention in the past decades. Because of the number of processing activities involved and the variability in the value of the components, these problems present a high level of uncertainty. This paper proposes a stochastic programming-based approach in which a deterministic model for product recovery network design can be extended to account for uncertainties explicitly. Because almost all existing approaches for solving such problems are either restricted to deterministic situations or can deal only with a modest number of scenarios for the uncertain problem parameters, a solution approach integrating a recently proposed sampling method with an acceleration strategy is developed. A computational study involving a large-scale product recovery network is presented to demonstrate the significance of the developed stochastic model as well as the efficiency of the proposed solution approach.