Bi-Level Scenario-based Location-Allocation-Inventory Models for Post-Crisis Conditions and Solving with Electromagnetic and Genetic Algorithms

Incidents that occur suddenly due to natural and human functions and impose hardships on society are called crises. As the Earth’s climate changes have increased the number of natural crises, including earthquakes, floods, hurricanes, etc., in recent years, human beings have felt the need for crisis management and the necessary planning in critical situations more than ever. This research aims to model and solve the problem of location, allocation, and inventory in post-crisis conditions. To meet this purpose, first, we have conducted a review of the previous papers. Then, we have identified the research gaps in management and planning in critical situations. In this study, uncertain budgets and demands and bi-level programming decision-making are the innovations. As a result, we have developed mixed-integer linear mathematical models to cover the research gaps. Finally, several problems have been solved in small dimensions by GAMS software and large-sized problems by genetic and electromagnetic meta-heuristic algorithms. Then, we analyzed the algorithms’ performance which indicates the genetic algorithm is better than the electromagnetic algorithm in this issue.