A robust stochastic possibilistic programming model for dynamic supply chain network design with pricing and technology selection decisions

This work considers a multi-period multi-echelon multi-product dynamic supply chain network design problem for both strategic and tactical decisions. Strategic decisions include the determination of the locations of the facilities, the capacities of the open facilities, and the capacities of the dedicated and the flexible technologies, and the tactical decisions include the determination of the prices of the products, the flows of materials and products among the locations, and the quantities of the products to produce in each plant in each period. The demand at each customer zone is modeled by a logit price-response function and is approximated by a piecewise linear function. A mixed-integer nonlinear programming model is developed to maximize the expected net present value while making these decisions. A robust possibilistic stochastic programming approach is used to deal with price-sensitive demands under hybrid, i.e., disruption and operational, uncertainties. The model considers the effect of robustness level on technology selection and price decisions, and enables tradeoffs between the robustness level and the expected net present value. The applicability of the model and the performance of the solution approach are examined through computational experiments. The results show that the optimal technology investment is a function of the types of uncertainties and the flexible-to-dedicated technology cost ratio. The results also show a significant advantage of the proposed robust possibilistic stochastic programming model over the other models in the simultaneous controllability of the possibilistic and scenario variabilities. The sensitivity of some key parameters in the model are analyzed in the computational experiments.