Advance reservations and information sharing in queues with strategic customers

In many branches of the economy, including transportation, lodging, and more recently cloud computing, users can reserve resources in advance. Although advance reservations are gaining popularity, little is known about the strategic behavior of customers facing the decision whether to reserve a resource in advance or not. Making an advance reservation can reduce the waiting time or the probability of not getting service, but it is usually associated with an additional cost. To evaluate this trade-off, we develop a game-theoretic framework, called advance reservation games, that helps in reasoning about the strategic behavior of customers in systems that allow advance reservations. Using this framework, we analyze several advance reservation models, in the context of slotted loss queues and waiting queues. The analysis of the economic equilibria, from the provider perspective, yields several key insights, including: (i) If customers have no a-priori information about the availability of servers, then only customers granted service should be charged a reservation fee; (ii) Informing customers about the exact number of available servers is less profitable than only informing them that servers are available; (iii) In many cases, the reservation fee that leads to the equilibrium with maximum possible profit leads to other equilibria, including one resulting with no profit; (iv) If the game repeats many times and customers update their strategy after observing actions of other customers at previous stage, then the system converges to an equilibrium where no one makes an advance reservation, if such an equilibrium exists. Else, the system cycles and yields positive profit to the provider Finally, we study the impact of information sharing in M/M/1 queues with strategic customers. We analyze the intuitive policy of sharing the queue length with customers when it is small and hiding it when it is large. We prove that, from the provider perspective, such a policy is never optimal. That is, either always sharing the queue length or always hiding it maximizes the average number of customers joining the queue

Resource allocation in service and logistics systems

Resource allocation is a problem commonly encountered in strategic planning, where a typical objective is to minimize the associated cost or maximize the resulting profit. It is studied analytically and numerically for service and logistics systems in this dissertation, with the major resource being people, services or trucks. First, a staffing level problem is analyzed for large-scale single-station queueing systems. The system manager operates an Erlang-C queueing system with a quality-of-service (QoS) constraint on the probability that a customer is queued. However, in this model, the arrival rate is uncertain in the sense that even the arrival-rate distribution is not completely known to the manager. Rather, the manager has an estimate of the support of the arrival-rate distribution and the mean. The goal is to determine the number of servers needed to satisfy the quality of service constraint. Two models are explored. First, the constraint is enforced on an overall delay probability, given the probability that different feasible arrival-rate distributions are selected. In the second case, the constraint has to be satisfied by every possible distribution. For both problems, asymptotically optimal solutions are developed based on Halfin-Whitt type scalings. The work is followed by a discussion on solution uniqueness with a joint QoS constraint and a given arrival-rate distribution in multi-station systems. Second, an extension to Naor’s analysis on the joining or balking problem in observable M=M=1 queues and its variant in unobservable M=M=1 queues is presented to incorporate parameter uncertainty. The arrival-rate distribution is known to all, but the exact arrival rate is unknown in both cases. The optimal joining strategies are obtained and compared from the perspectives of individual customers, the social optimizer and the profit maximizer, where differences are recognized between the results for systems with deterministic and stochastic arrival rates. Finally, an integrated ordering and inbound shipping problem is formulated for an assembly plant with a large number of suppliers. The objective is to minimize the annual total cost with a static strategy. Potential transportation modes include full truckload shipping and less than truckload shipping, the former of which allows customized routing while the latter does not. A location-based model is applied in search of near-optimal solutions instead of an exact model with vehicle routing, and numerical experiments are conducted to investigate the insights of the problem.Operations Research and Industrial Engineerin

Can Yardstick Competition Reduce Waiting Times?

Yardstick competition is a regulatory scheme for local monopolists (e.g., hospitals), where the monopolist's reimbursement is linked to performance relative to other equivalent monopolists. This regulatory scheme is known to provide cost-reduction incentives and serves as the theoretical underpinning behind the hospital prospective reimbursement system used throughout the developed world. This paper uses a game-theoretic queueing model to investigate how yardstick competition performs in service systems (e.g., hospital emergency departments), where in addition to incentivizing cost reduction the regulator wants to incentivize waiting time reduction. We first show that the form of cost-based yardstick competition used in practice results in inefficiently long waiting times. We then demonstrate how yardstick competition can be appropriately modified to achieve the dual goal of cost and waiting-time reduction. In particular, we show that full efficiency (first-best) can be restored if the regulator makes the providers' reimbursement contingent on their service rates and is also able to charge a provider-specific "toll" to consumers. More importantly, if such a toll is not feasible, as may be the case in healthcare, we show that there exists an alternative and particularly simple yardstick-competition scheme, which depends on the average waiting time only, that can significantly improve system efficiency (second-best). This scheme is easier to implement as it does not require the regulator to have detailed knowledge of the queueing discipline. We conclude with a numerical investigation that provides insights on the practical implementation of yardstick competition for hospital Emergency Departments and also present a series of modelling extensions

Essays on Service Information, Retrials and Global Supply Chain Sourcing

In many service settings, customers have to join the queue without being fully aware of the parameters of the service provider (for e.g., customers at check-out counters may not know the true service rate prior to joining). In such blind queues\u27\u27, customers typically make their decisions based on the limited information about the service provider\u27s operational parameters from past experiences, reviews, etc. In the first essay, we analyze a firm serving customers who make decisions under arbitrary beliefs about the service parameters. We show, while revealing the service information to customers improves revenues under certain customer beliefs, it may however destroy consumer welfare or social welfare. When consumers can self-organize the timing of service visits, they may avoid long queues and choose to retry later. In the second essay, we study an observable queue in which consumers make rational join, balk and (costly) retry decisions. Retrial attempts could be costly due to factors such as transportation costs, retrial hassle and visit fees. We characterize the equilibrium under such retrial behavior, and study its welfare effects. With the additional option to retry, consumer welfare could worsen compared to the welfare in a system without retrials. Surprisingly, self-interested consumers retry too little (in equilibrium compared to the socially optimal policy) when the retrial cost is low, and retry too much when the retrial cost is high. We also explore the impact of myopic consumers who may not have the flexibility to retry. In the third essay, we propose a comprehensive model framework for global sourcing location decision process. For decades, off-shoring of manufacturing to China and other low-cost countries was a no-brainer decision for many U.S. companies. In recent years, however, this trend is being challenged by some companies to re-shore manufacturing back to the U.S., or to near-shore manufacturing to Mexico. Our model framework incorporates perspectives over the entire life cycle of a product, i.e., product design, manufacturing and delivering, and after-sale service support, and we use it to test the validity of various competing theories on global sourcing. We also provide numerical examples to support our findings from the model

Surge pricing on a service platform under spatial spillovers: evidence from Uber

Ride-sharing platforms employ surge pricing to match anticipated capacity spillover with demand. We develop an optimization model to characterize the relationship between surge price and spillover. We test predicted relationships using a spatial panel model on a dataset from Ubers operation. Results reveal that Ubers pricing accounts for both capacity and price spillover. There is a debate in the management community on the ecacy of labor welfare mechanisms associated with shared capacity. We conduct counterfactual analysis to provide guidance in regards to the debate, for managing congestion, while accounting for consumer and labor welfare through this online platform.First author draf

Control Mechanisms in Queueing Systems with Nonlinear Waiting Costs

In many queueing systems, customers have been observed to exhibit strategic behavior. Each customer gains a value when receiving a product or getting served and suffers when incurring a delay. We consider a nonlinear waiting cost function to capture the sensitivity of customers toward delay. We investigate customers' behavior and system manager's strategy in two different settings: (1) customers are served in a service system, or (2) they receive a product in a supply chain. In the first model, we study an unobservable queueing system. We consider that customers are impatient, and are faced with decision problems whether to join a service system upon arrival, and whether to remain or renege at a later time. The goal is to address two important elements of queueing analysis and control: (1) customer characteristics and behavior, and (2) queueing control. The literature on customer strategic behavior in queues predominately focuses on the effects of waiting time and largely ignores the mixed risk attitude of customer behavior. Empirical studies have found that customers’ risk attitudes, their anticipated time, and their wait time affect their decision to join or abandon a queue. To explore this relationship, we analyze the mixed risk attitude together with a non-linear waiting cost function that includes the degree of risk aversion. Considering this behavior, we analyze individuals' joint balking and reneging strategy and characterize socially optimal strategy. To determine the optimal queue control policy from a revenue-maximizer perspective, which induces socially optimal behavior and eliminates customer externalities, we propose a joint entrance-fee/abandonment-threshold mechanism. We show that using a pricing policy without abandonment threshold is not sufficient to induce socially optimal behavior and in many cases results in a profit lower than the maximum social welfare the system can generate. Also, considering both customer characteristics and queue control policy, our findings suggest that customers with a moderate anticipation time provide higher expected revenue, acknowledging the importance of understanding customer behavior with respect to both wait time and risk attitude in the presence of anticipation time. In the second model, we consider a two-echelon production inventory system with a single manufacturer and a single distribution center (DC) where the manufacturer has a finite production capacity. There is a positive transportation time between the manufacturer and the DC. Each customer gains a value when receiving the product and suffers a waiting cost when incurring a delay. We assume that customers' waiting cost depends on their degree of impatience with respect to delay (delay sensitivity). We consider a nonlinear waiting cost function to show the degree of risk aversion (impatience intensity) of customers. We assume that customers follow the strategy p where they join the system and place an order with probability p. We analyze the inventory system with a base-stock policy in both the DC and the manufacturer. Since customers and supply chain holder are strategic, we study the Stackelberg equilibrium assuming that the DC acts as a Stackelberg leader and customers are the followers. We first obtain the total expected revenue and then derive the optimal base-stock level as well as the optimal price at the DC