This paper studies an emerging business model of line-sitting in which customers seeking service can hire others (line-sitters) to wait in line on behalf of them. We develop a queueing-game-theoretic model that captures the interaction among customers, the line-sitting firm, and the service provider to examine the impact of line-sitting on the service provider’s revenue and customer welfare. We also contrast line-sitting with the well-known priority purchasing scheme, as both allow customers to pay extra to skip the wait. Our main results are as follows. First, we find that both accommodating line-sitting and selling priority can bring in extra revenue for the service provider, although by different means—selling priority increases revenue mainly by allowing the service provider to practice price discrimination that extracts more customer surplus, whereas line-sitting does so through demand expansion, attracting customers who would not otherwise join. Second, the priority purchasing scheme tends to make the customer population worse off, whereas line-sitting can be a win–win proposition for both the service provider and the customers. Nevertheless, having the additional option of hiring line-sitters does not always benefit customers as a whole because the demand expansion effect also induces negative congestion externalities. Finally, despite the fact that the service provider collects the priority payment as revenue but not the line-sitting payment, which accrues to the third-party line-sitting firm, we demonstrate that, somewhat surprisingly, accommodating line-sitting can raise more revenue for the service provider than directly selling priority. This paper was accepted by Charles Corbett, operations management.

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In the event of a virus outbreak such as Covid‐19, testing is key. However, long waiting lines at testing facilities often discourage individuals from getting tested. This paper utilizes queueing‐game‐theoretic models to study how testing facilities should set scheduling and pricing policies to incentivize individuals to test, with the goal to identify the most cases of infection. Our findings are as follows. First, under the first‐in‐first‐out discipline (FIFO), the common practice of making testing free attracts the most testees but may not catch the most cases. Charging a testing fee may surprisingly increase case detection. Second, even though people who show symptoms are more likely to carry the virus, prioritizing these individuals over asymptomatic ones (another common practice) may let more cases go undetected than FIFO testing does. Third, we characterize the optimal scheduling and pricing policy. To maximize case detection, testing can be made free but one should also (partially) prioritize individuals with symptoms when testing demand is high and switch to (partially) prioritize the asymptomatic when testing demand is moderately low.

No abstract

The first in, first out (FIFO) queue discipline respects the order of arrivals, but is not efficient when customers have heterogeneous waiting costs. Priority queues, in which customers with higher waiting costs are served first, are more efficient but usually involve undesirable queue-jumping behaviors that violate bumped customers’ property rights over their waiting spots. To have the best of both worlds, we propose time-trading mechanisms, in which customers who are privately informed about their waiting costs mutually agree on the ordering in the queue by trading positions. If a customer ever moves back in the queue, she will receive an appropriate monetary compensation. Customers can always decide not to participate in trading and retain their positions as if they are being served FIFO. We design the optimal mechanisms for the social planner, the service provider, and an intermediary who might mediate the trading platform. Both the social planner’s and the service provider’s optimal mechanisms involve a flat admission fee and an auction that implements strict priority. If a revenue-maximizing intermediary operates the trading platform, it should charge a trade participation fee and implement an auction with some trade restrictions. Therefore, customers are not strictly prioritized. However, relative to a FIFO system, the intermediary delivers value to the social planner by improving efficiency, and to the service provider by increasing its revenue. This paper was accepted by Noah Gans, stochastic models and simulation.