A stochastic programming model for supply chain planning

The paper proposes both deterministic and stochastic programming models for manufacturing planning for supply chain network. A two-stage scheme for stochastic programming models is proposed. The design objective is to maximize the total profit. For modeling we deal with uncertainty by making randomness in the data explicit. In general, we may have randomness in all of model data, such as "processing times", "the actual demands" etc. We propose that we have scenario trees, where each scenario has some probability of occurrence. A computational study involving different supply chain networks are presented to highlight the significance of the stochastic model.