scholarly journals A robust counterpart approach to the bi-objective emergency medical service design problem

2014 ◽  
Vol 38 (3) ◽  
pp. 1033-1040 ◽  
Author(s):  
Zhi-Hai Zhang ◽  
Hai Jiang
2019 ◽  
Vol 20 (2) ◽  
pp. 95
Author(s):  
Diah Chaerani ◽  
Siti Rabiatul Adawiyah ◽  
Eman Lesmana

Bi-objective Emergency Medical Service Design Problem is a problem to determining the location of the station Emergency Medical Service among all candidate station location, the determination of the number of emergency vehicles allocated to stations being built so as to serve medical demand. This problem is a multi-objective problem that has two objective functions that minimize cost and maximize service. In real case there is often uncertainty in the model such as the number of demand. To deal the uncertainty on the bi-objective emergency medical service problem is using Robust Optimization which gave optimal solution even in the worst case. Model Bi-objective Emergency Medical Service Design Problem is formulated using Mixed Integer Programming. In this research, Robust Optimization is formulated for Bi-objective Emergency Medical Service Design Problem through Robust Counterpart formulation by assuming uncertainty in demand is box uncertainty and ellipsoidal uncertainty set. We show that in the case of bi-objective optimization problem, the robust counterpart remains computationally tractable. The example is performed using Lexicographic Method and Branch and Bound Method to obtain optimal solution. 


2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Phantakan Tansuwannarat ◽  
Pongsakorn Atiksawedparit ◽  
Arrug Wibulpolprasert ◽  
Natdanai Mankasetkit

Abstract Background This work was to study the prehospital time among suspected stroke patients who were transported by an emergency medical service (EMS) system using a national database. Methods National EMS database of suspected stroke patients who were treated by EMS system across 77 provinces of Thailand between January 1, 2015, and December 31, 2018, was retrospectively analyzed. Demographic data (i.e., regions, shifts, levels of ambulance, and distance to the scene) and prehospital time (i.e., dispatch, activation, response, scene, and transportation time) were extracted. Time parameters were also categorized according to the guidelines. Results Total 53,536 subjects were included in the analysis. Most of the subjects were transported during 06.00-18.00 (77.5%) and were 10 km from the ambulance parking (80.2%). Half of the subjects (50.1%) were served by advanced life support (ALS) ambulance. Median total time was 29 min (IQR 21, 39). There was a significant difference of median total time among ALS (30 min), basic (27 min), and first responder (28 min) ambulances, Holm P = 0.009. Although 91.7% and 88.3% of the subjects had dispatch time ≤ 1 min and activation time ≤ 2 min, only 48.3% had RT ≤ 8 min. However, 95% of the services were at the scene ≤ 15 min. Conclusion Prehospital time from EMS call to hospital was approximately 30 min which was mainly utilized for traveling from the ambulance parking to the scene and transporting patients from the scene to hospitals. Even though only 48% of the services had RT ≤ 8 min, 95% of them had the scene time ≤ 15 min.


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