Open AccessAmbulance routing in disaster response considering variable patient condition: NSGA-II and MOPSO algorithms
Author(s) -
Masoud Rabbani,
Nastaran Oladzad-Abbasabady,
Niloofar Akbarian-Saravi
Publication year - 2022
Publication title -
journal of industrial and management optimization
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.325
H-Index - 32
eISSN - 1553-166X
pISSN - 1547-5816
DOI - 10.3934/jimo.2021007
Subject(s) - sorting , economic shortage , computer science , particle swarm optimization , integer programming , genetic algorithm , service (business) , operations research , linear programming , vehicle routing problem , algorithm , routing (electronic design automation) , mathematical optimization , operations management , mathematics , machine learning , engineering , business , computer network , linguistics , philosophy , marketing , government (linguistics)
The shortage of relief vehicles capacity is a common issue throughout disastrous situations due to the abundance of injured people who need urgent medical aid. Hence, ambulances fleet management is highly important to save as many injured individuals as possible. In this regard, the present paper defines different patient groups based on their needs and characteristics. In order to provide the affected people with proper and timely medical aid, changes in their health status are also considered. A Mixed-integer Linear Programming (MILP) model is proposed to find the best sequence of routes for each ambulance and minimize the latest service completion time (SCT) as well as the number of patients whose condition gets worse because of receiving untimely medical services. Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to find high-quality solutions over a short time. In the end, Lorestan province, Iran, is considered as a case study to assess the model's performance and analyze the sensitivity of solutions with respect to the major parameters, which results in insightful managerial suggestions.