Optimized Ambulance Allocation Using Hybrid PSOGA for Improving the Ambulance Service

Abstract

Emergency Medical Service (EMS) is an integral part of the healthcare system that works dedicatedly to save the lives of people. The prime responsibility of any EMS provider is to offer a timely response to the person in need. The timely assistance increases the chances of survivability of a person. In India, escalating count of road accidents has increased the demand for ambulance services to provide pre-hospital treatment or transportation of victims to the hospital. This paper presents a new optimization strategy of Hybrid Particle Swarm Optimization and Genetic Algorithm (HPSOGA) for ambulance allocation to reduce the ambulance response time. Considering a set of assumptions, the authors have applied the new strategy for allocating 50 ambulances to 11 base stations in Southern Delhi. The working environment of EMS which includes stochastic requests, travel time, and dynamic traffic conditions have been taken into account to attain accurate results. The new optimization strategy of HPSOGA has been implemented in a MATLAB environment to find an optimized allocation plan with minimum response time. With the proposed algorithm the authors have been able to reduce the average response time by 11.61%. The paper also presents the comparison of HPSOGA, Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) for the stated problem. The algorithms are compared in terms of objective value (response time), convergence rate, and constancy repeatability to conclude that HPSOGA performs better than the other two algorithms.