English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

1PhD student, Department of Industrial Management, Tehran North Branch, Islamic Azad University, Tehran, Iran 2Associate Professor, School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran Corresponding Author: pishvaee@iust.ac.ir 3Assistant Professor, Department of Anesthesiology, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran 4Assistant Professor, Department of Industrial Management, Faculty of Management, Tehran North Azad University, Tehran, Iran 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 prehospital 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. Article Info Abstract

parastār va pizishk dar razm

Mathematical Modeling of Data Based on the Demand of Emergency Services of Hospital in order to Maximize the Coverage and Minimize the Operational Costs