چكيده به لاتين
Abstract
The occurrence of unforeseen disaster and illnesses in various parts of the world, especially in large cities, always affects the lives of many people. Most incidents or sudden illnesses require immediate relief due to their direct effects in human life and the shortest time of the service can have a significant impact on the outcome of the relief. It should be noted that finding the right solution with regard to financial and therapeutic restrictions is so efficient in providing relief services. Recognizing the issue of relief and understanding is helpful in making the decision.
The process of planning, managing, and controlling the flow of relief supplies for disaster victims and patients is called relief logistics. The relief logistics seeks to provide relief services to the injured people with the resources available.
The decision-making integrity of the relief logistics with regard to optimization models ultimately leads to better conditions. On the other hand, given the unpredictability of the relief request for more appropriate planning, it is necessary to analyze the uncertain conditions, which will make the decision more difficult. We consider integrated and multi-level detection, emergency allocation and routing in uncertainty conditions, which always have a robust response to the least varied circumstances. In the proposed model, demand is defined from emergency stations in a potential and fuzzy space to represent actual conditions. According to the results of the second model, the proposed model leads to a lower cost, and the third model increases the likelihood of demand satisfaction, and each of them has a comparative advantage.
In order to describe the problem space in a good way, a comprehensive algorithm is presented in which different tools and techniques are used to describe the problem and, finally to have a formulation for it. To solve the problem, the SA and NSGA-II algorithms are also used, which will be used to solve single-objective and multi-objective problems, respectively. Finally, after designing randomly generated instance problems and studying a case study in Tehran, computational results will be presented in the form of sensitivity analysis of key parameters and the provision of managerial suggestions and decisions.
Keywords: Emergency Locations, Ambulance Routing, Metaheuristic Algorithm, Uncertainty Space
