Considering pricing and uncertainty in designing a reverse logistics network

Companies try to maximise their profits by recovering returned products of highly uncertain quality and quantity. In this paper, a reverse logistics network for an original equipment manufacturer (OEM) is presented. Returned products are selected for remanufacturing or scrapping, based on their quality and proportional prices are offered to customers. A mixed integer nonlinear programming (MINLP) model is proposed to determine the location of collection centres, the optimum price of returned products and the sorting policy. The risk in the objective function is measured using the conditional value at risk (CVaR) metric. CVaR measures the risk of an investment in a conservative way by considering the maximum lost. The results are analysed for various values of the risk parameters (α, and λ). These parameters indicate that considering risk affects prices, the classification of returned products, the location of collection centres and, consequently, the objective function. The model performs more conservatively when the weight of the CVaR part (λ) and the value of the confidence level α are increased. The results show that better profits are obtained when we take CVaR into account.