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