دانلود مقاله ISI انگلیسی شماره 79782
عنوان فارسی مقاله

برنامه نویسی پویای تقریبی برای تخصیص ظرفیت در صنعت خدمات

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
79782 2012 12 صفحه PDF سفارش دهید محاسبه نشده
خرید مقاله
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عنوان انگلیسی
Approximate dynamic programming for capacity allocation in the service industry
منبع

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

Journal : European Journal of Operational Research, Volume 218, Issue 1, 1 April 2012, Pages 239–250

کلمات کلیدی
تخصیص ظرفیت؛ خدمات؛ عملیات مراقبت های بهداشتی - برنامه نویسی پویای تقریبی؛ یادگیری تقویت؛ فرایند تصمیم گیری نیمه مارکوف
پیش نمایش مقاله
پیش نمایش مقاله برنامه نویسی پویای تقریبی برای تخصیص ظرفیت در صنعت خدمات

چکیده انگلیسی

We consider a problem where different classes of customers can book different types of service in advance and the service company has to respond immediately to the booking request confirming or rejecting it. The objective of the service company is to maximize profit made of class-type specific revenues, refunds for cancellations or no-shows as well as cost of overtime. For the calculation of the latter, information on the underlying appointment schedule is required. In contrast to most models in the literature we assume that the service time of clients is stochastic and that clients might be unpunctual. Throughout the paper we will relate the problem to capacity allocation in radiology services. The problem is modeled as a continuous-time Markov decision process and solved using simulation-based approximate dynamic programming (ADP) combined with a discrete event simulation of the service period. We employ an adapted heuristic ADP algorithm from the literature and investigate on the benefits of applying ADP to this type of problem. First, we study a simplified problem with deterministic service times and punctual arrival of clients and compare the solution from the ADP algorithm to the optimal solution. We find that the heuristic ADP algorithm performs very well in terms of objective function value, solution time, and memory requirements. Second, we study the problem with stochastic service times and unpunctuality. It is then shown that the resulting policy constitutes a large improvement over an “optimal” policy that is deduced using restrictive, simplifying assumptions.

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