Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach

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Meta-heuristic algorithms for resource Management in Crisis Based on OWA approach Abdolreza Asadi Ghanbari 1 & Hossein Alaei 2

# Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract In crisis management, Threat Evaluation (TE) and Resource Allocation (RA) are two key components. To build an automated system in this area after modelling Threat Evaluation and Resource Allocation processes, solving these models and finding the optimal solution are further important issues. In this paper, Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithms (SPEA-II) are employed to solve a multi-objective multi-stage Resource Allocation problem. These Algorithms have been compared using normalized values of the objectives by generational distance, spread, hyper-volume, cardinality and actual computational times. It is found that the non-dominated solutions obtained by SPEA-II are better than NSGA-II both in terms of convergence and diversity but at the expense of computational time. Here, the fuzzy inference systems and the decision tree have been used to conduct threat evaluation process. Finally, Ordered Weighted Averaging (OWA) with maximum Bayesian entropy method for determining the operator weights has been used to pick the final choice among optimal options. We plan to use the proposed method in this paper for crisis management in Iranian Red Crescent organization during fire fighting. Two real studies have been done and results have been presented. Keywords Resource Management in Crisis (RMC) . Dynamic resource allocation (DRA) . Multi-objective optimization (MOO) . Non-dominated sorting genetic algorithm-II (NSGA-II) . Strength Pareto evolutionary algorithm-II (SPEA-II) . Maximum Bayesian entropy OWA (MBEOWA)

1 Introduction Selection of technologies for solving RMC problem depends on the structure of the problem and spatial-temporal constraints. In an RMC system after having TE done, the responses to threats should be designed and then executed at proper times (a process known as RA) [57]. In TE process, needed arguments should be transparent i.e. system’s line of reasoning should be clear to decision-makers. On the other hand, as RA model is designed to support human operators in real world conditions, it should be able to cope with numerous and heterogeneous objectives which help the operator

* Hossein Alaei [email protected] Abdolreza Asadi Ghanbari [email protected] 1

Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

2

Faculty of Entrepreneurship, University of Tehran, 14155-6619, Tehran, Iran

make appropriate decisions. This implies that RA is required to be formulated as a multi-objective optimization (MOO) problem which we will refer to as MOO-RA from now on. Additionally it may be necessary to execute designed responses at different stages so RA model should be multistage as well. Execution scheduling is another important issue in response planning, which deals with determining fe