Sharing delay information in service systems: a literature survey

Service providers routinely share information about upcoming waiting times with their customers, through delay announcements. The need to effectively manage the provision of these announcements has led to a substantial growth in the body of literature which is devoted to that topic. In this survey paper, we systematically review the relevant literature, summarize some of its key ideas and findings, describe the main challenges that the different approaches to the problem entail, and formulate research directions that would be interesting to consider in future work

Essays on Service Operations Management

This dissertation studies three different problems service firms can face. The first chapter looks at the optimal way to price reservations and services when customers make reservations in advance, while they are uncertain about the future value of service, to avoid waiting on the day of service. We show that charging customers the full price as non-refundable deposit when they make reservations and charging zero for service when they show up to claim their reservations is optimal for the firm. When the firm faces very large potential market, then it is better for the firm to not take reservations and accept only walk-ins. The second chapter looks at a problem of how to mitigate worker demotivations due to fairness concerns, when workers have intrinsic difference in quality, and higher quality server tends to be overcrowded by customers willing to receive higher quality service. We suggest distributing workload fairly between workers and compensating workers per workload as potential remedies and show which remedy works well under what operational conditions. We show that compensating workers per customer they serve results in high customer expected utility and expected quality. However, when customers also care about fairness and dislike receiving inferior service compared to other customers, then there does not exist a single remedy that results in both high customer expected utilization and high expected quality. In the third chapter, we study how a service firm should choose its advertising strategy when the service quality is not perfectly known to the customers. We model customers\u27 learning process using a Markov chain, and show that when customers do not perfectly learn the quality of service from advertisements, then the firm is better off by advertising actively when customers\u27 initial belief about service quality is low. Oppositely, when customers initially believe the service quality to be high, then it is better for the firm to stay silent and not use advertisement to signal its quality. In all three chapters, we use game theory to model the interactions among the participants of the problem and find the equilibrium outcomes

Strategic behavior and revenue management of cloud services with reservation-based preemption of customer instances

Cloud computing is a multi billion dollar industry, based around outsourcing the provisioning and maintenance of computing resources. In particular, Infrastructure as a Service (IaaS) enables customers to purchase virtual machines in order to run arbitrary software. IaaS customers are given the option to purchase priority access, while providers choose whether customers are preempted based on priority level. The customer decision is based on their tolerance for preemption. However, this decision is a reaction to the provider choice of preemption policy and cost to purchase priority. In this work, a non-cooperative game is developed for an IaaS system offering resource reservations. An unobservable $M|G|1$ queue with priorities is used to model customer arrivals and service. Customers receive a potential priority from the provider, and choose between purchasing a reservation for that priority and accepting the lowest priority for no additional cost. Customers select the option which minimizes their total cost of waiting. This decision is based purely on statistics, as customers cannot communicate with each other. This work presents the impact of the provider preemption policy choice on the cost customers will pay for a reserved instance. A provider may implement a policy in which no customers are preempted (NP); a policy in which all customers are subject to preemption (PR); or a policy in which only the customers not making reservations are subject to preemption (HPR). It is shown that only the service load impacts the equilibrium possibilities in the NP and PR policies, but that the service variance is also a factor under the HPR policy. These factors impact the equilibrium possibilities associated to a given reservation cost. This work shows that the cost leading to a given equilibrium is greater under the HPR policy than under the NP or PR policies, implying greater incentive to purchase reservations. From this it is proven that a provider maximizes their potential revenue from customer reservations under an HPR policy. It is shown that this holds in general and under the constraint that the reservation cost must correspond to a unique equilibrium.2020-06-03T00:00:00

QUEUING SYSTEMS WITH STRATEGIC AND LEARNING CUSTOMERS

In many service systems customers are strategic and can make their own decisions. In particular, customers can be delay-sensitive and they will leave the system if they think the waiting time is too long. For the service provider, it is important to understand customers’ behaviors and choose the appropriate system design. This dissertation consists of two research projects. The first project studies the pooling decision when customers are strategic. It is generally accepted that operating with a combined (i.e., pooled) queue rather than separate (i.e., dedicated) queues is beneficial mainly because pooling queues reduces long-run average sojourn time. In fact, this is a well-established result in the literature when jobs cannot make decisions and servers and jobs are identical. An important corollary of this finding is that pooling queues improves social welfare in the aforementioned setting. We consider an observable multi-server queueing system which can be operated with either dedicated queues or a pooled one. Customers are delay-sensitive and they decide to join or balk based on queue length information upon arrival. In this setting, we prove that, contrary to the common understanding, pooling queues can considerably increase the long-run average sojourn time so that the pooled system results in strictly smaller social welfare (and strictly smaller consumer surplus) than the dedicated system under certain conditions. Specifically, pooling queues leads to performance loss when the arrival-rate-to-service-rate ratio and the relative benefit of service are both large. We also prove that performance loss due to pooling queues can be significant. Our numerical studies demonstrate that pooling queues can decrease the social welfare (and the consumer surplus) by more than 95%. The benefit of pooling is commonly believed to increase with the system size. In contrast to this belief, our analysis shows that when delay-sensitive customers make rational joining decisions, the magnitude of the performance loss due to pooling can strictly increase with the system size. The second project studies the learning behavior when customers don’t have full information of the service speed. We consider a single-server queueing system where customers make join- ing and abandonment decisions when the service rate is unknown. We study different ways in which customers process service-related information, and how these impact the performance of a service provider. Specifically, we analyze forward-looking, myopic and naive information process- ing behaviors by customers. Forward-looking customers learn about the service rate in a Bayesian framework by using their observations while waiting in the queue. Moreover, they make their abandonment decisions considering both belief and potential future payoffs. On the other hand, naive customers ignore the available information when they make their decisions. We prove that regardless of the way in which the information is processed by customers, a customer’s optimal joining and abandonment policy is of threshold-type. There is a rich literature that establishes that forward-looking customers are detrimental to a firm in settings different than queueing. In contrast to this common understanding, we prove that for service systems, forward-looking customers are beneficial to the firm under certain conditions.Doctor of Philosoph

ESSAYS IN STOCHASTIC MODELING AND OPTIMIZATION

Stochastic modeling plays an important role in estimating potential outcomes where randomness or uncertainty is present. This type of modeling forecasts the probability distributions of potential outcomes by allowing for random variation in one or more inputs over time under different conditions. One of the classic topics of stochastic modeling is queueing theory.Hence, the first part of the dissertation is about a stylized queueing model motivated by paid express lanes on highways. There are two parallel, observable queues with finitely many servers: one queue has a faster service rate, but charges a fee to join, and the other is free but slow. Upon arrival, customers see the state of each queue and choose between them by comparing the respective disutility of time spent waiting, subject to random shocks. This framework encompasses both the multinomial logit and exponomial customer choice models. Using a fluid limit analysis, we give a detailed characterization of the equilibrium in this system. We show that social welfare is optimized when the express queue is exactly at (but not over) full capacity; however, in some cases, revenue is maximized by artificially cre- ating congestion in the free queue. The latter behaviour is caused by changes in the price elasticity of demand as the service capacity of the free queue fills up. The second part of the dissertation is about a new optimal experimental design for linear regression models with continuous covariates, where the expected response is interpreted as the value of the covariate vector, and an “error” occurs if a lower- valued vector is falsely identified as being better than a higher-valued one. Our design optimizes the rate at which the probability of error converges to zero using a large deviations theoretic characterization. This is the first large deviations-based optimal design for continuous decision spaces, and it turns out to be considerably simpler and easier to implement than designs that use discretization. We give a practicable sequential implementation and illustrate its empirical potential

Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution

This is the peer reviewed version of the following article: Cao, P., Wang, Y. and Xie, J. (2019), Priority Service Pricing with Heterogeneous Customers: Impact of Delay Cost Distribution. Prod Oper Manag, 28: 2854-2876., which has been published in final form at https://doi.org/10.1111/poms.13086. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.National Natural Science Foundation of Chin

Information design in service systems and online markets

In mechanism design, the firm has an advantage over its customers in its knowledge of the state of the system, which can affect the utilities of all players. This poses the question: how can the firm utilize that information (and not additional financial incentives) to persuade customers to take actions that lead to higher revenue (or other firm utility)? When the firm is constrained to ``cheap talk,'' and cannot credibly commit to a manner of signaling, the firm cannot change customer behavior in a meaningful way. Instead, we allow firm to commit to how they will signal in advance. Customers can then trust the signals they receive and act on their realization. This thesis contains the work of three papers, each of which applies information design to service systems and online markets. We begin by examining how a firm could signal a queue's length to arriving, impatient customers in a service system. We show that the choice of an optimal signaling mechanism can be written as a infinite linear program and then show an intuitive form for its optimal solution. We show that with the optimal fixed price and optimal signaling, a firm can generate the same revenue as it could with an observable queue and length-dependent variable prices. Next, we study demand and inventory signaling in online markets: customers make strategic purchasing decisions, knowing the price will decrease if an item does not sell out. The firm aims to convince customers to buy now at a higher price. We show that the optimal signaling mechanism is public, and sends all customers the same information. Finally, we consider customers whose ex ante utility is not simply their expected ex post utility, but instead a function of its distribution. We bound the number of signals needed for the firm to generate their optimal utility and provide a convex program reduction of the firm's problem