Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service
Capacity allocation for demand of different customer-product-combinations with cancellations, no-shows, and overbooking when there is a sequential delivery of service
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DOI：
10.1007/s10479-013-1324-5
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发表时间：
2013-07-01
影响因子：
4.8
通讯作者：
Kolisch, Rainer
中科院分区：
文献类型：
--
作者：
Schuetz, Hans-Joerg;Kolisch, Rainer
我们考虑一个问题，不同类别的客户可以提前预订不同类型的服务，服务公司必须立即响应预订请求确认或拒绝它。由于在服务日之前取消的可能性，或者在服务日没有出现，超额预订给定的能力是一个可行的决定。服务公司的目标是最大限度地提高利润的类类型的具体收入，退款取消或没有显示以及加班费。为计算后者，需要有关任用时间表的资料。在整个文件中，我们将涉及的问题，在放射服务的能力分配。从收入管理，超额预订和预约调度的思想，我们建模的问题作为一个马尔可夫决策过程中的离散时间，由于适当的聚合可以最佳地解决与迭代随机动态规划方法。在实验研究中，我们成功地应用该方法的一个真实的世界的问题与数据从放射科的医院。此外，我们比较的最佳政策，四个启发式的政策，其中一个是目前正在使用。我们可以证明，最优策略显着提高了目前使用的政策和嵌套的预订限制类型的政策非常接近的最优策略，因此建议在实践中使用。
We consider a problem where different classes of customers can book different types of services in advance and the service company has to respond immediately to the booking request confirming or rejecting it. Due to the possibility of cancellations before the day of service, or no-shows at the day of service, overbooking the given capacity is a viable decision. 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 the cost of overtime. For the calculation of the latter, information of the underlying appointment schedule is required. Throughout the paper we will relate the problem to capacity allocation in radiology services. Drawing upon ideas from revenue management, overbooking, and appointment scheduling we model the problem as a Markov decision process in discrete time which due to proper aggregation can be optimally solved with an iterative stochastic dynamic programming approach. In an experimental study we successfully apply the approach to a real world problem with data from the radiology department of a hospital. Furthermore, we compare the optimal policy to four heuristic policies, of whom one is currently in use. We can show that the optimal policy significantly improves the currently used policy and that a nested booking limit type policy closely approximates the optimal policy and is thus recommended for use in practice.