Approximation Balancing Policies for Inventory Systems with Remanufacturing

Abstract

We consider a single-product, periodic-review inventory system with remanufacturable returned products, in which the serviceable product used to fulfill stochastic customer demand can be either manufactured from new parts or remanufactured from returned products. Demand and returns follow general stochastic processes and may be correlated, nonstationary, and evolving across different periods. The system costs include remanufacturing and manufacturing costs as well as inventory holding and demand backlogging costs. The objective is to minimize the expected total discounted cost over a finite planning horizon. The optimal policy for this model has a simple structure but its computation can be practically intractable because of the large and high-dimensional state space. Henceforth, we propose an efficient approximation algorithm based on cost balancing techniques to compute manufacturing and remanufacturing quantities in each period and show that the expected cost under this remanufacturing balancing policy is at most twice of the optimal one. Moreover, a numerical study demonstrates that the balancing policy generally performs much better than its worst-case bound. The analysis and results are also extended to cases with capacitated manufacturing and remanufacturing and multiple types of returned products.