To investigate the long-term impact of food delivery services on the restaurant industry, we model a restaurant serving food to two streams of customers: tech-savvy customers who have access to delivery service, and traditional customers who are not tech-savvy enough to use delivery service and only walk in by themselves. We study a Stackelberg game, in which the restaurant first sets the price of the food; the platform then sets the delivery fee; and, last, rational customers decide whether to walk in, balk, or use delivery service if they have access to one. We show that the platform does not necessarily increase demand for the restaurant but may just change the composition of customers, as the segment of tech-savvy customers grows. Hence, paying the platform for bringing in customers may hurt the restaurant's profitability. Furthermore, under conditions of no coordination between the restaurant and the platform, we show, somewhat surprisingly, that more customers having access to the delivery service may hurt the platform itself and the society when the delivery service is sufficiently convenient and the pool of delivery workers is large enough. This is because the restaurant can become a delivery-only kitchen and raise its food price, leaving a little surplus for the platform and consumers. But this would not happen when the pool of delivery workers could be capped. It is implied that limiting the number of delivery workers provides a simple yet effective means for the platform to improve its own profit while benefiting social welfare at the same time.

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  • "Waiting Time Management in a Healthcare Open-Shop Network", with Opher Baron, Avishai Mandelbaum, Jianfu Wang, Galit B. Yom-Tov and Nadir Arber

    • Draft available upon request. ​

Motivated by a large healthcare clinic that provides periodic health screening services, we study how to improve customers' experience in an open-shop service network using routing and priority policies combined with information technologies. We consider from both macro, system-level, such as total wait, and micro, station-level, such as waits at single stations. We study how to balance these macro- and micro-level performance measures. We empirically evaluate automation efforts done by the clinic to improve the streamline of patients using automatic routing and queue communication using SMS notifications and find that such practices have negligible impacts on performance measures. We, therefore, use a stylized queueing model and simulations to study priority policies for open-shop networks that consider customers’ profiles. We demonstrate that such policies may outperform the station-level FCFS policy by simultaneously improving both the macro- and micro-level performance measures. One of the challenges we uncover is the tension between pooling considerations that promote last-minute decisions and behavioral considerations that promote early decisions to enable customers smooth transfer from one station to another. To overcome this tension, we propose a buffer strategy that postpones the routing decisions, which leaves the system more flexibility and gathers more information for decision making. We demonstrate the efficacy of this policy in improving and balancing these performance measures.

  • "Distracted Waiting: M/M/1 Queue with Discounted Waiting Cost", with Martin J. Lemuel and Di Yin

    • Draft available upon request.​

We investigate the phenomenon that service providers use monetary discounts to keep customers waiting in long queues. For instances, Haidilao gives waiting customers a task such as folding paper cranes that rewards them with a free dish upon completion; some retailers offer customers who are waiting for check out an opportunity to finish a survey in exchange for a discount at the cashier. We consider an M/M/1 queue operated by a profit-maximizing service provider that offers a discount to strategic customers whose waiting cost depends on the discount received. We identify the profit-maximizing discount follows a threshold strategy and show that the service provider benefits from this discount strategy only if the market of price-sensitive customers is large enough. Further, we find that such a discount scheme works best for services with relatively low service rewards and service rates coupled with very impatient customers, such as the restaurant industry.