Abstract
In this study we develop a stochastic multi-objective stochastic programming approach to deal with a supply chain planning under uncertainty. Supply chain cost parameters such as transportation cost, inventory holding cost, shortage cost, production cost in addition to some respects as employment, dismissal, workers productivity and training are all considered. Due to the important role of the lead time especially in supplying, the lead time between suppliers and factories and between factories and customer zones is assumed to be a function of transportation mode. Cost parameters and demand fluctuations are subject to uncertainty. To develop the model, two additional objective functions are added to the classical aggregate production planning. Therefore, the proposed multi-objective model includes the minimization of the expected total cost of supply chain, the minimization of the variability of the total cost of supply chain and the minimization of the expected value of the greenhouse gas emission level. Then, the proposed model is solved applying compromise programming method. Finally, a numerical example is solved to describe the proposed model.
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