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Mathematical Modeling of Data Based on the Demand of Emergency Services of Hospital in order to Maximize the Coverage and Minimize the Operational Costs
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Hadid Mahmoodi    , Saman Pishvaee , Peyman Sanberian , Maryam Shoar |
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Abstract: (2310 Views) |
Introduction: In today's high-traffic world, where humans are constantly confronted with various dangers around the world, a proper response system with the proper functioning of pre-hospital medical emergencies is of paramount importance. A major challenge for emergency services worldwide is dispatching ambulances to patients appropriately in terms of limited resources and patient safety.
Methods: The mathematical model was first introduced based on the ambulance distribution process in East Tehran Emergency Area and solved using GAMS software based on Epsilon constraint technique and Pareto model analysis. The objective functions are to maximize coverage and minimize costs. To deal with the uncertainties of the parameters, a robust optimization approach was used and the Epsilon method was used for two purposes of modeling. To clarify the model and illustrate the application of the above mathematical model in the real world, a case study was conducted in the eastern region.
Results: Based on the proposed model, the number of stations in East Tehran can be reduced from 39 to 19, but the number of ambulances in each station is almost twice as high as the average of 4 or 5 ambulances per station.
Conclusion: Reducing the number of bases can have a significant impact on the cost of stationary station equipment, personnel amenities, the cost of time consumed and the property used to build the station, thus saving more stations to reduce time. Responding to demands established increased productivity. |
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| Keywords: linear programmin model, emergency medical system, constraints, uncertainty, reliability, maximization of cover, minimization of operational costs |
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Full-Text [PDF 384 kb]
(530 Downloads)
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Type of Study: Applicable |
Subject:
Special
Received: 2019/10/7 | Accepted: 2019/12/2 | Published: 2020/09/21
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