A dynamic spare parts ordering and pricing policy based on stochastic programming with multi-choice parameters

This paper studies the uncertain and random factors in real-life spare parts supply networks which are abstract and complex dynamical systems, then quantifies these factors in a mathematical model. To seek a dynamic spare parts ordering and pricing policy from a distributor's viewpoint, stochastic programming with multi-choice parameters is applied to formulate this objective optimization problem. In our model, the optimal objective is to maximize the total expected profit of the members of the spare parts supply network, and the decision variables are distributor's selling price and ordering quantity in different periods. By using the methods of expectation operator of the fuzzy variable, Lagrange interpolating polynomial and global criteria, the model is solved, and the optimal ordering and pricing policy is obtained. The results of the numerical example and contrast experiments validate the feasibility and efficiency of the proposed model. Some significant conclusions drawn from the results of parameter sensitivity analysis can be referred by management practitioners. This general model can be applied in other fields of supply chain management, where random and uncertain factors need to be considered.