Integrating dynamic acquisition pricing and remanufacturing decisions under random price-sensitive returns

As uncertainties increase in both the acquisition of used products and the demand of remanufactured products, balancing supply and demand has become more important for a remanufacturing firm. Therefore, the remanufacturing firm needs to combine acquisition management with remanufacturing planning. Used products are often collected from a large number of end users, and acquisition pricing is adopted to control the return quantity of the used product. In this paper, we study a multiperiod acquisition pricing and remanufacturing decision problem under random price-sensitive returns. First, the problem is formulated into a two-decision periodic review inventory model, the decomposition property of which is proved, and thus, the problem can be decomposed into two subproblems with single decision variable. Second, we acquire the solution structure of the optimal remanufacturing quantity, which is a basic inventory policy not influenced by random returns. Next, we analyze characteristics of the optimal acquisition price and derive a monotonic pricing policy depending on the starting level of the whole inventory in each period. Further, an algorithm is designed to calculate the optimal inventory level and acquisition price of each period, which holds a lower computational complexity. Finally, numerical examples are provided to show the effectiveness of the algorithm and to conduct managerial insights of main parameters.