Abstract
In remanufacturing, the supply of used products and the demand for remanufactured products are usually mismatched because of the great uncertainties on both sides. In this paper, we propose a dynamic pricing (DP) policy to balance this uncertain supply and demand. Specifically, we study a remanufacturer’s problem of pricing a single class of cores with random price-dependent returns and random demand for the remanufactured products with backlogs. We model this pricing task as a continuous-time Markov decision process, which addresses both the finite and infinite horizon problems, and provide managerial insights by analysing the structural properties of the optimal policy. We then use several computational examples to illustrate the impacts of particular system parameters on pricing policy and the benefit of DP. In addition, the models are extended to account for the price adjustment costs. We show through numerical example that the nice structural properties do not exist any longer, and find when DP is better than static pricing.
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