A Robust-Stochastic Optimization Approach for Designing Relief Logistics Operations under Network Disruption

After a natural disaster, medical supplies will be in high demand in the disaster-affected communities. Providing prompt and high-quality rescue resources is critical to the emergency relief network's overall quality. This study presents a mathematical optimization model for constructing a multi-period emergency relief system that minimizes the system's overall expected costs. The model considers location, allocation, and distribution decisions as well as flow of medical supplies and injured people. Medical supply distribution centers and roads are vulnerable to failure in the suggested model. Since certain parameters in the real world are unknown, the model parameters' uncertainty is explored. There are four sources of uncertainty regarding the number of injured people, demand, costs, and the probability of failure. To cope these uncertainties, a robust-stochastic optimization approach is used. Also, a case study focused on an earthquake in southern and western cities of Fars province is discussed to assess the efficacy of the suggested model. The findings demonstrate that the robust-stochastic approach is capable of effectively controlling cost and demand uncertainty, and that failing to account for uncertainty when planning relief logistics would be extremely deceptive. The planned relief system has the highest cost at the highest level of uncertainty, but it will offer a better protected solution to uncertainty with a greater level of robustness. The stochastic model has the lowest cost, but it is unable to produce the most conservative solution with the best uncertainty protection when there is a great deal of uncertainty in the system.