Trading Time in a Congested Environment
@article{YangTrading2017,author = {Yang, Luyi and Debo, Laurens and Gupta, Varun},title = {Trading Time in a Congested Environment},journal = {Management Science},volume = {63},number = {7},pages = {2377-2395},year = {2017},doi = {10.1287/mnsc.2016.2436},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. }}Referral Priority Program: Leveraging Social Ties via Operational Incentives
@article{YangReferral2019,author = {Yang, Luyi and Debo, Laurens},title = {Referral Priority Program: Leveraging Social Ties via Operational Incentives},journal = {Management Science},volume = {65},number = {5},pages = {2231-2248},year = {2019},doi = {10.1287/mnsc.2018.3034},abstract = { The referral priority program—an emerging business practice adopted by a growing number of technology companies that manage a waitlist of customers—enables existing customers on the waitlist to gain priority access if they successfully refer new customers to the waitlist. Unlike more commonly used referral reward programs, this novel mechanism does not offer monetary compensation to referring customers, but leverages customers’ own disutility of delays to create referral incentives. Despite this appealing feature, our queueing-game-theoretic analysis finds that the effectiveness of such a scheme as a marketing tool for customer acquisition and an operational approach for waitlist management depends crucially on the underlying market conditions, particularly the base market size of spontaneous customers. The referral priority program does not generate referrals when the base market size is either too large or too small. When customers do refer, the program could actually backfire—namely, by reducing the system throughput and customer welfare—if the base market size is intermediately large. This phenomenon occurs because the presence of referred customers severely cannibalizes the demand of spontaneous customers. We also compare the referral priority program with the referral reward program when the service provider optimally sets the admission price. Numerical study suggests that the referral priority program is more profitable than the referral reward program when the base market size is intermediately small. The online appendix is available at https://doi.org/10.1287/mnsc.2018.3034. This paper was accepted by Serguei Netessine, operations management. }}Search Among Queues Under Quality Differentiation
@article{YangSearch2019,author = {Yang, Luyi and Debo, Laurens G. and Gupta, Varun},title = {Search Among Queues Under Quality Differentiation},journal = {Management Science},volume = {65},number = {8},pages = {3605-3623},year = {2019},doi = {10.1287/mnsc.2018.3112},abstract = { Customers looking for service providers often face search frictions and have to trade off quality and availability. To understand customers’ search behavior when they are confronted with a large collection of vertically differentiated, congested service providers, we build a model in which arriving customers conduct a costly sequential search to resolve uncertainty about service providers’ quality and queue length and select one to join by optimal stopping rules. Customers search, in part, because of variations in waiting time across service providers, which, in turn, is determined by the search behavior of customers. Thus, an equilibrium emerges. We characterize customers’ equilibrium search/join behavior in a mean field model as the number of service providers grows large. We find that reducing either the search cost or customer arrival rate may increase the average waiting time in the system as customers substitute toward high-quality service providers. Moreover, with lower search costs, the improved quality obtained by customers may not make up for the prolonged wait, therefore degrading the average search reward and, more importantly, decreasing customer welfare; when customers search, their welfare can even be lower than if they are not allowed to search at all.This paper was accepted by Gad Allon, operations management. }}The Economics of Line-Sitting
@article{CuiLineSitting2020,author = {Cui, Shiliang and Wang, Zhongbin and Yang, Luyi},title = {The Economics of Line-Sitting},journal = {Management Science},volume = {66},number = {1},pages = {227-242},year = {2020},doi = {10.1287/mnsc.2018.3212},abstract = { 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. }}Invite Your Friend and You’ll Move Up in Line: Optimal Design of Referral Priority Programs
@article{YangReferral2021,author = {Yang, Luyi},title = {Invite Your Friend and You’ll Move Up in Line: Optimal Design of Referral Priority Programs},journal = {Manufacturing \& Service Operations Management},volume = {23},number = {5},pages = {1139-1156},year = {2021},doi = {10.1287/msom.2020.0868},abstract = { Problem definition : This paper studies the optimal design of referral priority programs, in which customers on a waiting list can jump the line by inviting their friends to also join the waiting list. Academic/practical relevance : Recent years have witnessed a growing presence of referral priority programs as a novel customer-acquisition strategy for firms that maintain a waiting list. Different variations of this scheme are seen in practice, raising the question of what should be the optimal referral priority mechanism. Methodology : I build an analytical model that integrates queuing theory into a mechanism design framework in which the objective of the firm is to maximize the system throughput, that is, accelerate customer acquisition as much as possible. Results : My analysis shows that the optimal mechanism has one of the following structures: full priority; partial priority; first in, first out (FIFO); and strategic delay. A full-priority (partial-priority) scheme enables referring customers to get ahead of all (only some) nonreferring ones. A FIFO scheme does not provide any priority-based referral incentive. A strategic-delay scheme grants full priority to referring customers but artificially inflates the delay of nonreferring ones. I show that FIFO is optimal if either the base-market size or the referral cost is large. Otherwise, partial priority is optimal if the base-market size is above a certain threshold; full priority is optimal at the threshold base-market size; strategic delay is optimal if the base-market size is below the threshold. I also find that referrals motivate the firm to maintain a larger capacity and therefore can surprisingly shorten the average delay, even though more customers sign up and strategic delay is sometimes inserted. Managerial implications : My paper provides prescriptive guidance for launching an optimal referral priority program and rationalizes common referral schemes seen in practice. }}A Model of Queue Scalping
@article{YangScalping2021,author = {Yang, Luyi and Wang, Zhongbin and Cui, Shiliang},title = {A Model of Queue Scalping},journal = {Management Science},volume = {67},number = {11},pages = {6803-6821},year = {2021},doi = {10.1287/mnsc.2020.3865},abstract = { Recent years have witnessed the rise of queue scalping in congestion-prone service systems. A queue scalper has no material interest in the primary service but proactively enters the queue in hopes of selling his spot later. This paper develops a queueing-game-theoretic model of queue scalping and generates the following insights. First, we find that queues with either a very small or very large demand volume may be immune to scalping, whereas queues with a nonextreme demand volume may attract the most scalpers. Second, in the short run, when capacity is fixed, the presence of queue scalping often increases social welfare and can increase or reduce system throughput, but it tends to reduce consumer surplus. Third, in the long run, the presence of queue scalping motivates a welfare-maximizing service provider to adjust capacity using a “pull-to-center” rule, increasing (respectively, reducing) capacity if the original capacity level is low (respectively, high). When the service provider responds by expanding capacity, the presence of queue scalping can increase social welfare, system throughput, and even consumer surplus in the long run, reversing its short-run detrimental effect on customers. Despite these potential benefits, such capacity expansion does little to mitigate scalping and may only generate more scalpers in the queue. Finally, we compare and contrast queue scalping with other common mechanisms in practice—namely, (centralized) pay-for-priority, line sitting, and callbacks.This paper was accepted by Victor Martínez de Albéniz, operations management. }}Slugging: Casual Carpooling for Urban Transit
@article{CuiSlugging2021,author = {Cui, Shiliang and Li, Kaili and Yang, Luyi and Wang, Jinting},title = {Slugging: Casual Carpooling for Urban Transit},journal = {Manufacturing \& Service Operations Management},volume = {24},number = {5},pages = {2516-2534},year = {2022},doi = {10.1287/msom.2021.0988},abstract = { Problem definition: “Slugging,” or casual carpooling, refers to the commuting practice of drivers picking up passengers at designated locations and offering them a free ride in order to qualify for high-occupancy vehicle (HOV) lanes. Academic/practical relevance: It is estimated that tens of thousands of daily commuters rely on slugging to go to work in major U.S. cities. As drivers save commute time and passengers ride for free, slugging can be a promising Smart Mobility solution. However, little is known about the welfare, policy, and environmental implications of slugging. Methodology: We develop a stylized model that captures the essence of slugging. We characterize commuters’ equilibrium behavior in the model. Results: We find that slugging indeed makes commuters better off. However, the widely observed free-ride tradition is socially suboptimal. As compared with the social optimum, commuters always underslug in the free-slugging equilibrium when highway travel time is insensitive to slugging activities but may overslug otherwise. The socially optimal outcome can be achieved by allowing pecuniary exchanges between drivers and passengers. Interestingly, passengers may be better off if they pay for a ride than if they do not under free slugging. We also find that although policy initiatives to expand highway capacity or improve public transportation always increase social welfare in the absence of slugging, they may reduce social welfare in areas where free slugging is a major commuting choice. Nevertheless, these unintended consequences would be mitigated by the introduction of pecuniary exchanges. Finally, contrary to conventional wisdom, slugging as a form of carpooling can result in more cars on the road and thus, more carbon emissions. Managerial implications: Our results call upon the slugging community to rethink the free-ride practice. We also caution that slugging benefits commuters possibly to the detriment of the environment. }}In-Queue Priority Purchase: A Dynamic Game Approach
@article{WangDynamicPriority2021,author = {Wang, Zhongbin and Yang, Luyi and Cui, Shiliang and Wang, Jinting},title = {In-queue priority purchase: a dynamic game approach},journal = {Queueing Systems},volume = {97},pages = {343-381},year = {2021}}Design of Covid-19 Testing Queues
@article{https://doi.org/10.1111/poms.13673,author = {Yang, Luyi and Cui, Shiliang and Wang, Zhongbin},title = {Design of Covid-19 testing queues},journal = {Production and Operations Management},year = {2022},volume = {31},number = {5},pages = {2204-2221},keywords = {Covid-19, testing, queues, service discipline, priority},doi = {https://doi.org/10.1111/poms.13673},url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/poms.13673},eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/poms.13673},abstract = {Abstract 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) prioritizing the asymptomatic when testing demand is moderately low. This article is protected by copyright. All rights reserved}}Pooling Agents for Customer-Intensive Services
@article{Pooling2023,author = {Wang, Zhongbin and Yang, Luyi and Cui, Shiliang and \"{U}lk\"{u}, Sezer and Zhou, Yong-Pin},title = {Pooling Agents for Customer-Intensive Services },journal = {Operations Research},year = {2023},volume = {71},number = {3},pages = {860-875},abstract = { To Pool or Not to Pool? Analyzing Customer-Intensive Services with Strategic AgentsIn customer-intensive services where service quality increases with service time, service providers commonly pool their agents and give performance bonuses that reward agents for achieving greater customer satisfaction and serving more customers. Conventional wisdom suggests that pooling agents reduce customer wait time whereas performance bonuses motivate agents to produce high-quality services, both of which should boost customer satisfaction. However, in “Pooling Agents for Customer-Intensive Services,” Wang, Yang, Cui, Ülkü, and Zhou find that when agents act strategically, they may choose to speed up under pooling in an attempt to serve more customers, thus undermining service quality. If this happens, pooling can backfire and result in both lower customer satisfaction and agent payoff. Consequently, the researchers propose a simple practical solution to restore the efficiency of pooling. They propose pooling a portion of the performance bonuses (incentive pooling) in conjunction with pooling agents (operational pooling). }}Right to Repair: Pricing, Welfare, and Environmental Implications
@article{doi:10.1287/mnsc.2022.4401,author = {Jin, Chen and Yang, Luyi and Zhu, Cungen},title = {Right to Repair: Pricing, Welfare, and Environmental Implications},journal = {Management Science},volume = {69},number = {2},pages = {1017-1036},year = {2023},doi = {10.1287/mnsc.2022.4401},abstract = { The “right-to-repair” (RTR) movement calls for government legislation that requires manufacturers to provide repair information, tools, and parts so that consumers can independently repair their own products with more ease. The initiative has gained global traction in recent years. Repair advocates argue that such legislation would break manufacturers’ monopoly on the repair market and benefit consumers. They further contend that it would reduce the environmental impact by reducing e-waste and new production. Yet the RTR legislation may also trigger a price response in the product market as manufacturers try to mitigate the profit loss. This paper employs an analytical model to study the pricing, welfare, and environmental implications of RTR. We find that, as the RTR legislation continually lowers the independent repair cost, manufacturers may initially cut the new product price and then raise it. This nonmonotone price adjustment may further induce a nonmonotone change in consumer surplus, social welfare, and the environmental impact. Strikingly, the RTR legislation can potentially lead to a lose–lose–lose outcome that compromises manufacturer profit, reduces consumer surplus, and increases the environmental impact despite repair being made easier and more affordable. This paper was accepted by Charles Corbett, operations management. }}To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders
@article{doi:10.1287/isre.2022.1128,author = {Jin, Chen and Yang, Luyi and Hosanagar, Kartik},title = {To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders},journal = {Information Systems Research},volume = {34},number = {1},pages = {532-552},year = {2023},doi = {10.1287/isre.2022.1128},abstract = { Brushing—online merchants placing fake orders of their own products—has been a widespread phenomenon on major e-commerce platforms. One key reason why merchants brush is that it boosts their rankings in search results. Products with higher sales volume are more likely to rank higher. Additionally, rankings matter because consumers face search frictions and narrow their attention to only the few products that show up at the top. Thus, fake orders can affect consumer choice. In our paper, we find that if brushing gets more costly for merchants (e.g., due to stricter platform policies), it may sometimes surprisingly harm consumers as it may only blunt brushing by the merchant who sells a more popular product but intensify brushing by the merchant selling a less popular product. If search is less costly for consumers (e.g., due to improved search technologies), it may not always benefit consumers, either. Moreover, the design of the ranking algorithm is critical: placing more weight on sales-volume-related factors may trigger a nonmonotone change in consumer welfare; tracking recent sales only as opposed to cumulative sales does not always dial down brushing and, in fact, may sometimes cause the merchant selling a less popular product to brush more. }}