Services have been constantly evolving and operational efficiency has been a key initiative for progress. In this collection of academic papers, we investigate the efficiency of three different industry practices, to each of which we dedicate a chapter. Chapter 1 and Chapter 2 cover completed research, while the research covered in Chapter 3 is at a preliminary stage. In Chapter 1, we study priority queues to understand the determinants of social efficiency. Many service providers utilize priority queues. Many consumers revile priority queues. However, some form of priority service may be necessary to maximize social welfare. Consequently, it is useful to understand how the priority scheme chosen by a revenue-maximizing firm differs from the one a social planner would use. We examine this in a single server-queue with customers that draw their valuation from a continuous distribution and have a per-period waiting cost that is proportional to their realized valuation. The decision maker must post a menu offering a finite number of waiting-time and price pairs. There are then three dimensions on which a revenue maximizer and social planner can differ: coverage (i.e., how many customers in total to serve), coarseness (i.e., how many classes of service to offer), and classification (i.e., how to map customers to priority levels). We show that differences between the decision makers priority policies are all about classification. Both are content to offer very coarse schemes with just two priority levels, and they will have negligible differences in coverage. However, differences in classification are persistent. Further, a revenue maximizer may --- relative to the social planner --- have too few or too many high priority customers. Whether the revenue maximizer over- or under-stuffs the high priority class depends on a measure of consumer surplus that is captured by the mean residual life function of the valuation distribution. In addition, we show that there is a large class of valuation distributions for which a move from first-in, first-out service to a priority scheme that places those with higher waiting costs at the front of the line reduces consumer surplus. In Chapter 2, we study the impact of the increased availability of real-time information on the behavior of strategic agents and the implications of this phenomenon for service efficiency. The use of real-time information in on-demand services provides agents with access to an unprecedented amount of information about their competitors. We use data from one of the leading e-hailing taxi platforms in South America to study the real-time reactions of agents to the dynamic entry of new competitors in their serving zone. Information about competitor locations could potentially induce herding behaviour (because competitors' actions may convey information about market opportunity) or scattering (because the entry of competitors reduces the expected market share and the appeal of a serving zone). We find that the net response to the real-time information indicating entry of new competitors in a service zone is an increase in the scattering of the agents previously in the serving zone. The response is not homogenous and some agents are more likely to respond to entry. We find that those agents who are more likely to react to the real-time presence of competitors by scattering achieve higher utilization. We investigate the consequences of these behaviors for the efficiency of service systems. Finally, in Chapter 3, we analyze the effect of carry-on bag policy on the system efficiency. Air-carriers want to utilize their airplanes as much as possible. One of the obstacles against a high utilization is the delay due to the boarding process. Some of the low-cost carriers started to apply fees on carry-on bags so that passengers would be encouraged to check in their bags instead of taking these bags with theirselves to the board. In this study, we investigate the effects of this new policy on the air-carrier delay. We use the available data of U.S. Department of Transportation's (DOT) Bureau of Transportation Statistics (BTS) which contains flights from Frontier Airlines that applies this new policy. We observe that this policy change was successful in decreasing the departure delays. Furthermore, we propose the requirement of robustness analysis with additional factors that capture the dynamics of the industry more realistically.

Last modified

• 10/22/2018

Creator

• Can Ozkan

DOI

• https://doi.org/10.21985/N2Q44X

Subject

• Operations Management

Keyword

• Business administration

Date created

• 2017-01-01

Resource type

• Dissertation

Rights statement

• In Copyright