دانلود مقاله ISI انگلیسی شماره 79763
ترجمه فارسی عنوان مقاله

حل جابجایی آمبولانس پویا و اعزام مشکل با استفاده از برنامه نویسی پویای تقریبی

عنوان انگلیسی
Solving the dynamic ambulance relocation and dispatching problem using approximate dynamic programming
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
79763 2012 11 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : European Journal of Operational Research, Volume 219, Issue 3, 16 June 2012, Pages 611–621

ترجمه کلمات کلیدی
OR در سرویس های بهداشتی - وسایل نقلیه اضطراری؛ محل آمبولانس؛ برنامه نویسی پویای تقریبی؛ بهینه سازی تصادفی
کلمات کلیدی انگلیسی
OR in health services; Emergency vehicles; Ambulance location; Approximate dynamic programming; Stochastic optimization
پیش نمایش مقاله
پیش نمایش مقاله  حل جابجایی آمبولانس پویا و اعزام مشکل با استفاده از برنامه نویسی پویای تقریبی

چکیده انگلیسی

Emergency service providers are supposed to locate ambulances such that in case of emergency patients can be reached in a time-efficient manner. Two fundamental decisions and choices need to be made real-time. First of all immediately after a request emerges an appropriate vehicle needs to be dispatched and send to the requests’ site. After having served a request the vehicle needs to be relocated to its next waiting location. We are going to propose a model and solve the underlying optimization problem using approximate dynamic programming (ADP), an emerging and powerful tool for solving stochastic and dynamic problems typically arising in the field of operations research. Empirical tests based on real data from the city of Vienna indicate that by deviating from the classical dispatching rules the average response time can be decreased from 4.60 to 4.01 minutes, which corresponds to an improvement of 12.89%. Furthermore we are going to show that it is essential to consider time-dependent information such as travel times and changes with respect to the request volume explicitly. Ignoring the current time and its consequences thereafter during the stage of modeling and optimization leads to suboptimal decisions.