Optimizing a Multi-Depot Logistics Model for Emergency Supply of Critical Items in the Aftermath of a Disaster with Fuzzy Demands and Stochastic Waiting Times Using Genetic Algorithm

Critical items such as first aid, food and water play the most important role in reducing human suffering and death. Therefore, the design of a proper logistics model for reducing the delays between help requests and delivery of critical items is a necessary study. However, there are additional challenges and risks associated with the disaster supply chain management which needs to be developed to cope with the aftermath of natural disasters. Like most real-world problems, economic costs of pre-positioning critical items and providing a periodic review of warehouses in order to reduce the rotting of goods are considerable subjects for economizing the solutions. In this paper, the author presented a new logistics model that increases the rate of human survival in the aftermath of disasters by optimizing a logistics model with Fuzzy concepts and using Genetic Algorithm. The modeling framework considers a split and prioritized delivery policy so the multi-objective integer programming methodology is applied for the proposed problem. Then, a set of heuristic algorithms based on decomposition method developed for the sub-problems. Finally, through various numerical simulations, the author showed that the Genetic Algorithm methodology can optimize a real-world logistics model better than other methodologies