Managing Appointment Booking Under Customer Choices
Managing Appointment Booking Under Customer Choices
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DOI：
10.1287/mnsc.2018.3150
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发表时间：
2019-09-01
影响因子：
5.4
通讯作者：
Zhang, Bo
中科院分区：
文献类型：
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作者：
Liu, Nan;van de Ven, Peter M.;Zhang, Bo
由于网上预约平台的使用日益增加，我们研究如何向客户提供预约时段，以最大限度地提高预约时段的总数。我们开发了两个模型，非顺序提供和顺序提供，以捕捉不同类型的客户和调度系统之间的相互作用。在这两个模型中，调度器分别提供一组预约时段供到达的客户选择，或者提供多组预约时段供到达的客户选择。对于非顺序模型，我们确定了一个静态随机化的政策，这是渐近最优的系统需求和容量同时增加时，我们进一步表明，提供所有可用的插槽在任何时候都有一个常数因子的两个性能保证。对于顺序模型，我们推导出一个封闭的形式的最优策略的一个大类的实例，并开发一个简单，有效的启发式对于那些没有明确的最优策略的实例。通过比较这两个模型，我们的研究产生了有用的操作见解，以改善目前的预约预订流程。特别是，我们的分析揭示了一个有趣的等价性之间的顺序提供模型和非顺序提供模型与完美的客户偏好信息。这种等价性使我们能够在广泛的交互式调度环境中应用顺序提供。我们广泛的数值研究表明，顺序提供可以显着提高插槽填充率（平均6%-8%，在我们的测试情况下高达18%）相比，非顺序提供。鉴于在线和移动的预约平台的近期和持续增长，我们的研究结果对这些预约平台的用户界面设计特别有用。
Motivated by the increasing use of online appointment booking platforms, we study how to offer appointment slots to customers to maximize the total number of slots booked. We develop two models, nonsequential offering and sequential offering, to capture different types of interactions between customers and the scheduling system. In these two models, the scheduler offers either a single set of appointment slots for the arriving customer to choose from or multiple sets in sequence, respectively. For the nonsequential model, we identify a static randomized policy, which is asymptotically optimal when the system demand and capacity increase simultaneously, and we further show that offering all available slots at all times has a constant factor of two performance guarantee. For the sequential model, we derive a closed form optimal policy for a large class of instances and develop a simple, effective heuristic for those instances without an explicit optimal policy. By comparing these two models, our study generates useful operational insights for improving the current appointment booking processes. In particular, our analysis reveals an interesting equivalence between the sequential offering model and the nonsequential offering model with perfect customer preference information. This equivalence allows us to apply sequential offering in a wide range of interactive scheduling contexts. Our extensive numerical study shows that sequential offering can significantly improve the slot fill rate (6%-8% on average and up to 18% in our testing cases) compared with nonsequential offering. Given the recent and ongoing growth of online and mobile appointment booking platforms, our research findings can be particularly useful to inform user interface design of these booking platforms.