International Journal of Electrical and Computer Engineering (IJECE) Vol. 7, No. 5, October 2017, pp. 2791~2797 ISSN: 2088-8708, DOI: 10.11591/ijece.v7i5.pp2791-2797  2791 Journal homepage: http://iaesjournal.com/online/index.php/IJECE Comparison of Emergency Medical Services Delivery Performance using Maximal Covering Location and Gradual Cover Location Problems Mohd Hafiz Azizan 1 , Ting Loong Go 2 , W. A. Lutfi W. M. Hatta 3 , Cheng Siong Lim 4 *, Soo Siang Teoh 5 1, 2, 3, 4 Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia 5 School of Electrical and Electronic Engineering, Universiti Sains Malaysia, 14300 Nibong Tebal, Malaysia Article Info ABSTRACT Article history: Received Feb 14, 2017 Revised May 30, 2017 Accepted Aug 11, 2017 Ambulance location is one of the critical factors that determine the efficiency of emergency medical services delivery. Maximal Covering Location Problem is one of the widely used ambulance location models. However, its coverage function is considered unrealistic because of its ability to abruptly change from fully covered to uncovered. On the contrary, Gradual Cover Location Problem coverage is considered more realistic compared to Maximal Cover Location Problem because the coverage decreases over distance. This paper examines the delivery of Emergency Medical Services under the models of Maximal Covering Location Problem and Gradual Cover Location Problem. The results show that the latter model is superior, especially when the Maximal Covering Location Problem has been deemed fully covered. Keywords: Ambulance location model Gradual cover location problem Maximal coverage location problem Particle swarm optimization Copyright © 2017 Institute of Advanced Engineering and Science. All rights reserved. Corresponding Author: Cheng Siong Lim, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, 81310 UTM Skudai, Johor, Malaysia. Email: lcsiong@utm.my 1. INTRODUCTION The efficiency of Emergency Medical Services (EMS) is very important to ensure patients survivability [1–3]. One of the EMS efficiency measurements is ambulance response time (ART). Generally, an ambulances response time can be defined as, the interval from the time the call was received by the EMS provider, to the arrival of the ambulance to the emergency scene [4–7]. A demand point is considered covered if it can be served within a defined time or distance to any of the available facilities; while a demand point farther than the defined time or distance is considered as not covered. An ambulances location is one of the factors that directly affects the ART [8]. As a result, ambulance location model is one of the effective ways to improve ART. Ambulance location models can be categorised into deterministic, probabilistic and dynamic models [9]. One of the earliest deterministic models is Maximal Covering Location Problem (MCLP) which has been introduced by Church and Revelle [10]. Given a fixed number of facilities, MCLP is used to maximise the total coverage with limited resources. MCLP and its variants are the most widely used location models. In 1984, the reorganisation of EMS in Austin, Texas, using MCLP, saved $3.4 million of construction cost and $1.2 million of operating costs annually [11]. MCLP has been used for real life problems to solve hierarchically designed health systems [12-13], congested service systems [14] and bus stop allocations [15]. MCLP is a NP-Hard problem. Various approaches, such as exact method, heuristic and meta-heuristic, can be used to solve a MCLP problem. An exact execution of the method can guarantee the most optimal solution, but may have a longer