Routing (electronic design automation)

Metaheuristic

Service (business)

Vehicle routing problem

Constraint (computer-aided design)

Economics

Computer science

Mathematical optimization

Computer network

Operations research

Mathematics

Genetic algorithm

Artificial intelligence

Geometry

Heuristic

Economy

Tabu search

Machine learning

Journal of industrial and systems engineering.

This paper studies the ambulance routing problem (ARP) in disaster situations when a large number of injured people from various locations require receiving treatments and medical aids. In such circumstances, many people summoning the ambulances but the capacity and number of emergency vehicles are not sufficient to visit all the patients at the same time. Therefore, a pivotal issue is to manage the fleet of ambulances to meet all the requests promptly and consequently mitigate human suffering. We considered three different categories of patients with various requirements. Moreover, the support ambulances are segmented into various classes based on their capabilities. A mathematical formulation is presented to obtain route plans with the aim of minimizing the latest service completion time among the patients. Since the patient’s condition gets worse and becomes life ­threatening over the time, semi-soft time window constraint is incorporated to reflect the penalties on late arrivals using survival function. Since the presented model belongs to the class of NP-hard problems, two efficient meta-heuristic algorithms based on genetic algorithm and tabu search are proposed to cope with real size problems. The experiments show that the proposed model could present proper routes and adopt the types of ambulances with the patients’ needs to increase the service quality. Moreover, the proposed metaheuristics are capable to find acceptable solutions for the problem in reasonable computational times.

Ambulance routing in disaster response scenario considering different types of ambulances and semi soft time windows