Designing a Multi-Echelon Multi-Period Supply Chain under Uncertainty: A Simulation-Optimization Approach

Presence of many constraints and existing uncertainties in the real world is always a barrier against properly using the potentials of supply chain to attain its main purpose such as timely responding to customers’ demands and meeting their satisfactions. This study investigates the effects of different scenarios and decisions (policies) made on supply chain performance. A Simulation-optimization approach has been used for making optimal decisions under uncertainty. According to the uncertainties in the discrete stochastic simulation and also system complexity, meta-innovative genetic algorithm was used to find the preferred parameters of supply chain. Moreover, Taguchi method is used for setting up the optimal simulation-optimization algorithm parameters. Finally, results indicate the efficiency of the proposed algorithm. The proposed algorithm of this study help policy makers to minimize demand lost under different circumstances and data uncertainty.