Optimizing location, routing and inventory decisions in an integrated supply chain network under uncertainty

This study extends a mathematical model that integrates the location, allocation, inventory replenishment and routing decisions simultaneously. To cope with inherent uncertainty of parameters, we implement a continuous-time Markov process and derive the performance measures of the system. Using the obtained results, the problem is formulated as a mixed integer nonlinear programing model (MINLP), where the total costs of location, transportation and inventory are minimized. In addition, we develop a simulated annealing (SA) based meta-heuristic algorithm to tackle the computational complexity of the problem. Finally, several computational experiments are provided to assess effectiveness of the proposed algorithm and the model.