Evacuation Operation Management System Using Multi-Objective Artificial Bee Colony

Abstract

Disaster risk reduction and management organizations create evacuation plans to make sure that impacted persons are swiftly and effectively moved to safer locations, like evacuation centers or shelters. This plays a crucial role in reducing the amount of fatalities and damage brought on by disasters. Disaster situations are characterized by a high degree of unpredictability, which makes it difficult to improve disaster relief efforts. Specific issues that develop during and after catastrophes should be able to be managed by the organization in charge of disaster management activities. This study developed a web-based mobile application that can be used as a system for emergency evacuation management. The Multi-Objective Artificial Bee Colony (MOABC) algorithm is considered to find optimal solutions to emergency evacuation sheltering. Upon trials with 80 test data each, it shows that an accuracy of 88.75% to 93.75% was gained. It can be concluded that the use of MOABC in identifying evacuation centers is an effective method for optimizing the multi-objective problem with parameters of population size and evacuation capacity. Additionally, the study presented the process of navigating the system wherein the users can easily identify the available evacuation centers. The study recommends integrating the Pareto-based ABC approach to seek better optimal solutions for evacuation optimization problem applications. Future work could also focus on the improvement of encoding and representation of solutions for spatial optimization problems.