Designing a bi-objective decision support model for the disaster management

Abstract

This paper addresses the allocation and scheduling of the relief teams as one of the main issues in the response phase of the disaster management. In this study, a Bi-Objective Mixed Integer Programming (BOMIP) model is proposed to assign and schedule of the relief teams in the disasters. The first objective function aims to minimize the sum of weighted completion times of the incidents and the second objective function also minimizes the sum of weighted tardiness of the relief operations. In order to more similar to the real-world, time-windows for the incidents and damaged routes are considered in this research. Furthermore, the actual relief time of an incident by the relief team is calculated according to the position on the corresponding relief team and fatigue effect. Due to NP-hardness of the considered problem, the proposed model cannot present the optimal solution in the reasonable time. Thus, NSGA-II and PSO algorithms are applied to solve the problem. Furthermore, the obtained results of the proposed algorithms are compared with respect to different performance metrics in large size test problems. Finally, in order to investigate the impact of some parameters on the Pareto frontier, the sensitivity analysis and the managerial suggestions are provided.

A bi-objective humanitarian logistics model considering equity in the affected zones: application to a recent earthquake in Mexico

“…Recently, AUGMECON method has been implemented to provide solutions for bi-objective location-andinventory problems within a context of supply chains [36]. Hence, to develop this method, the following monoobjective formulation is obtained by relaxing the objective function given by equation ( where 𝐵 and 𝑆 represent a bound and a slack variable, respectively.…”

“…The authors implement a commercial solver on a small randomly-generated instance. Finally, Nayeri et al [36] address a bi-objective location-and-scheduling problem for relief teams in humanitarian logistics to minimize the completion time of a set of incidents and the weighted tardiness of relief operations. The problem includes different completion times for each pair incident-team and time windows to attend the incidents.…”

Human Performance Modelling and Mission Planning Optimisation incorporating Human Performance

“…Nayeri et al (2019) introduced physical fatigue effects into disaster management for the first time by extending their earlier work (Nayeri et al, 2021) on allocating/scheduling disaster relief teams to consider those effects. Nayeri et al's (2021) model assumed a time window of the rescue response (which is a time-critical one) and solved the problem using non-dominated sorting genetic and multi-objective particle swarm optimisation algorithms. Their bi-objective model was similar in form to the one in Tirkolaee et al (2020).…”

Application of decision tree algorithm mining model in career planning goal simulation

“…Nayeri, S. et al pointed out that the postgraduate education system is not perfect, the postgraduates lack the awareness of career planning, the school career planning system is not perfect, and the teachers are not professional enough. This shows that the school does not pay much attention to the postgraduate career planning system, and the postgraduates' planning awareness is relatively weak [6]. Cl, A. and others believe that graduate students lack self-awareness, professionalism, and social cognition, due to their high academic qualifications, they will stand at a relatively high starting point when choosing employment, which leads to high expectations and inability to find suitable jobs [7].…”