Planning of emergency medical service stations and ambulances

Official URL: http://192.168.1.20/record=b1225697 (Table of Contents)

In this thesis, we address the Emergency Medical Service (EMS) stations location problem. Firstly, we propose a Backup Double Covering Model (BDCM) as a variant of the well-known Maximal Covering Location Problem where two types of service requests are fulfilled. The objective of the model is to maximize the total population serviced within t1 and t2 minutes (t1 < t2) using two distinct EMS stations where the total number of stations is limited. Our aim in doing so is to provide a backup station in case no ambulance is available in the closer station. Since this problem is intractable for large-scale instances we propose a Tabu Search (TS) approach to find good solutions in reasonable computation time. Three initialization approaches are utilized for comparison: random, a steepest-ascent algorithm, and an LP relaxation-based heuristic. In order to test the effectiveness of the proposed method, we conduct an extensive experimental study on a large number of randomly generated data set with different sizes and number of stations. We observe that the TS algorithm provides good results fast in comparison with the solutions obtained using the optimization software OPL Studio 5.5 equipped with ILOG CPLEX 11.0. Secondly, we propose the multi-period extension of BDCM (MPBDCM). This model requires a given number of stations to be opened in each period where the stations opened in any period cannot be closed in the subsequent periods. To solve this problem efficiently, we adopt a similar TS approach using the same initialization methods as in BDCM. Our extensive experimental study on randomly generated data sets reveal that the TS approach provides good results fast compared to the results obtained by using OPL Studio 5.5. Finally, we apply the TS approaches for planning both the single- and multi-period EMS stations location problem in Istanbul on the data obtained from the Directorate of Instant Relief and Rescue at the Istanbul Metropolitan Municipality.