Introducing a novel multi-objective optimization model for volunteer assignment in the post-disaster phase: Combining fuzzy inference systems with NSGA-II and NRGA

Each year, disasters (natural or man-made) cause a lot of damage and take many people's lives. In this situation, volunteers come to help. While the proper management is very effective in controlling crisis, lack can create another crisis. Therefore, we introduce model deal with volunteer assignment problem by considering two qualitative objective functions: The first one minimizing mean importance Emergency Department (ED) centers' unmet needs volunteers, second degree unsatisfied preferences selected volunteers. To evaluate introduced indexes, Fuzzy Inference Systems (FISs) are used encapsulate decision makers' knowledge as well human reasoning process. FISs embedded evolutionary algorithms for solving proposed model: Non-Dominated Sorting Genetic Algorithm II (NSGA-II) Ranked (NRGA). Also, 30 small-size problems, large-size randomly generated solved both metaheuristic algorithms. Using obtained data, performance NSGA-II NRGA measured compared based on four criteria: CPU Time, Number Non-dominated Solutions (NNS), Mean Ideal Distance (MID), Spacing Metric (SM). Statistical tests show that have same problems. However, faster, produces more optimal solutions. flexible enough adapt different scenarios just updating linguistic rules FISs. since employed produce set solutions, decision-makers easily choose most appropriate solution among Pareto front circumstances.