Risk-averse optimization for managing inventory in closed-loop supply chains

Abstract

This thesis examines a closed-loop multi-stage inventory problem with remanufacturing option. A random fraction of used products is returned by consumers to the manufacturer after a certain number of stages. But the manufacturer may or may not collect any returned item. Demand can be satisfied through two channels: manufacturing new products and remanufacturing used products (cores). A control policy specifies the numbers of cores to collect and remanufacture, and the number of new products to manufacture, at each stage. The state space consists of the serviceable product and core inventory levels, and the amounts of future returns. We study this problem from the perspectives of risk-neutral and risk-averse decision-makers, in both cases of lost sales and backordering. We incorporate the dynamic coherent risk measures into our risk-averse problem formulation. We establish that it is always optimal to prefer remanufacturing to manufacturing under a mild condition. Numerical results indicate that a statedependent threshold policy may be optimal for the core inventory. However, such a policy need not be optimal for the serviceable product inventory. We also conduct numerical experiments to evaluate the performance of several heuristics that are computationally less demanding than the optimal policy: a certainty equivalent controller (CEC), a myopic policy (MP), a no-recovery policy (NRP), a full-collection policy (FCP), and a fixed threshold policy (FTP). CEC, MP, and NRP have a distinct computational advantage over FCP and FTP, whereas FCP and FTP significantly outperform all the other heuristics with respect to objective value, in our numerical experiments.