Inspired by a real-work case, this paper introduces a profit maximization model for dynamic lot sizing considering substitution and multiple usage of returns for refurbishing at different levels of quality or for disassembly to extract key parts that will be used in the manufacturing process. This model allows studying the interactions between different types of returns and decisions. To analyze the impact of uncertainty on these interactions, we develop a robust two-stage stochastic program with uncertainty on demands and returns. The resulting problems are mixed-integer linear programs that we solve using an efficient relax-and-fix and fix-and-optimize heuristic. Extensive numerical experiments are conducted to study the different trade-offs when integrating multi-usage of returns with substitution and accordingly derive managerial insights. The experiments have revealed, for example, that: (i) the profit margin of the refurbished items is the main determinant of the total profit and when such margin in high, the total profit becomes more sensitive to different cost variations; (ii) collection efficiency becomes very sensitive to collection cost and much less sensitive to refurbishing cost especially with large profit margins; (iii) when demand and returns are uncertain parameters, substitution becomes the best option as uncertainty and prices increase; (iv) when the gap between prices of different quality levels is high, lost sales occur mainly on the lowest quality product and downgrading decreases together with substitution; and (v) Sharing the production line between refurbishing and manufacturing for low-quality products is highly motivated by small upgrading costs and their substitution level increases with increasing upgrading costs.

A reverse logistics planning problem is modeled and analyzed. The model considers returns of a particular electronic device from customers. Some of the collected products are remanufactured or refurbished. Others are disassembled for their key parts which can be considered as good as new. New products are assembled either using new parts or extracted ones. There are two types dynamic demands: demands for remanufactured/refurbished products and demands for new products. Demand of remanufactured/refurbished products can be satisfied using new products in case of shortage. This is a one way downward substitution. The objective is to minimize total costs while satisfying all demands. This problem is formulated as a MILP. The numerical results show that: i) it is hard for a solver to find optimal solutions for the problem in reasonable computational times for several instances with relatively small time horizons and ii) substitution is justified for a certain range of cost and demand parameters.