Research Interests: service operations, stochastic processes and the control of queueing systems
Noah Gans’s research focuses on service operations, and he has a particular interest in the management of telephone call centers. He has been Department Editor of Stochastic Models and Simulation at Management Science and the President of Manufacturing and Service Operations Management Society (MSOM). At Wharton, Noah coordinates the OID Department’s PhD Program, and he teaches an MBA core course on Business Analytics, as well as MBA elective courses on Analytics for Services and for Revenue Management.
Abstract: Using airlines as a backdrop, we study optimal overbooking policies with endogenous customer demand, i.e., when customers can internalize their expected cost of being "bumped". We first consider the traditional setting in which compensation for bumped passengers is fixed and booking limits are the airline's only form of control. We provide sufficient conditions under which demand endogeneity leads to lower overbooking limits in this case. We then consider the broader problem of joint control of ticket price, bumping compensation and booking limit. We show that price and compensation can act as substitutes, which reduces the general problem to a more tractable one-dimensional search for optimal overbooking compensation, and effectively allows the value of flying to be decoupled from the cost of being bumped. Finally, we extend our analysis to the case of auction-based compensation schemes, and demonstrate that these generally outperform fixed compensation schemes. Numerical experiments to gauge magnitudes suggests that fixed-compensation policies that account for demand endogeneity can significantly outperform those that do not, and that auction-based policies bring smaller but significant additional gains.
Stephen E Chick, Noah Gans, Ozge Yapar (Work In Progress), Risk-sharing Agreements For New Medical Treatments.
Abstract: Any estimate of a new medical treatment’s value that relies on clinical-trial data can have significant residual uncertainty. So-called post-marketing data, captured after the treatment has entered the market and is used by the general population, can augment clinical-trial data to better validate its safety, efficacy, and economic value. To better manage the risks associated with this post-trial residual uncertainty, new price updating mechanisms are under consideration around the world. We analyze these new risk-sharing arrangements.
Stephen E Chick, Noah Gans, Ozge Yapar (Under Revision), Bayesian Sequential Learning for Clinical Trials of Multiple Correlated Medical Interventions.
Abstract: We integrate emerging trends intended to improve clinical trial design: design for cost-effectiveness, which ensures health-economic improvement of a new intervention over the current standard intervention; adaptive design, which dynamically adjusts the sample size and allocation of patients to different interventions; and multi-arm trial design, which compares multiple interventions simultaneously. Our goal is to identify a sequential sampling policy that dynamically decides the interventions to which patients should be allocated, as well as when to stop patient recruitment, in order to maximize the expected population-level benefit minus the cost of the trial. The literature on sequential sampling develops indices that either accommodate correlation among the mean rewards of alternatives or are based on optimal stopping times that can dynamically change as samples are taken, but not both. We develop the first tractable allocation and stopping rules whose indices capture both correlation and dynamic stopping times, and our numerical experiments demonstrate the value of considering both problem elements in the context of clinical trials.
Alessandro Arlotto, Stephen E Chick, Noah Gans (2014), Optimal Hiring and Retention Policies for Heterogeneous Workers Who Learn, Management Science, 60 (1), pp. 110-129.
Abstract: We study the hiring and retention of heterogeneous workers who learn over time. We show that the problem can be analyzed as an infinite-armed bandit with switching costs, and we apply results from Bergemann and Välimäki (2001) to characterize the optimal hiring and retention policy. For problems with Gaussian data, we develop approximations that allow the efficient implementation of the optimal policy and the evaluation of its performance. Our numerical examples demonstrate that the value of active monitoring and screening of employees can be substantial.
Nitin Bakshi, Stephen E Flynn, Noah Gans Thwarting Nuclear Terrorism Through Container Inspections.
Abstract: Bakshi, Nitin, Steven Flynn, and Noah Gans (2010). Thwarting Nuclear Terrorism Through Container Inspections. Administrative & Regulatory Law News 37, 10-11.
Nitin Bakshi, Stephen E Flynn, Noah Gans (2011), Estimating the Operational Impact of Container Inspections at International Ports, Management Science, 57, 1-20.
Abstract: A US law mandating nonintrusive imaging and radiation detection for 100% of U.S.-bound containers at international ports has provoked widespread concern that the resulting congestion would hinder trade significantly. Using detailed data on container movements, gathered from two large international terminals, we simulate the impact of the two most important inspection policies that are being considered. We find that the current inspection regime being advanced by the U.S. Department of Homeland Security can only handle a small percentage of the total load. An alternate inspection protocol that emphasizes screening—a rapid primary scan of all containers, followed by a more careful secondary scan of only a few containers that fail the primary test—holds promise as a feasible solution for meeting the 100% scanning requirement.
Abstract: Gans, Noah, Nan Liu, Avishai Mandelbaum, Haipeng Shen, and Han Ye (2010). Service Times in Call Centers: Agent Heterogeneity and Learning with Some Operational Consequences, James O. Berger, T. Tony Cai, and Ian Johnstone eds., IMS Collections 6, 99-123.
Nitin Bakshi and Noah Gans (2010), Securing the Containerized Supply Chain: Analysis of Government Incentives for Private Investment, Management Science, 56 (2), pp. 219-233.
Stephen E Chick and Noah Gans (2008), Economic Analysis of Simulation Selection Problems, Management Science, 55, 421 - 437.
Abstract: Ranking and selection procedures are standard methods for selecting the best of a finite number of simulated design alternatives, based on a desired level of statistical evidence for correct selection. But the link between statistical significance and financial significance is indirect and poorly understood. This paper presents a new approach to the simulation selection problem, one that maximizes the expected net present value (NPV) of decisions made when using stochastic simulation. We provide a framework for answering these managerial questions: When does a proposed system design, whose performance is unknown, merit the time and money needed to develop a simulation to infer its performance? For how long should the simulation analysis continue before a design is approved or rejected? We frame the simulation selection problem as a “stoppable” version of a Bayesian bandit problem that treats the ability to simulate as a real option prior to project implementation. For a single proposed system, we solve a free boundary problem for a heat equation that approximates the solution to a dynamic program that finds optimal simulation project stopping times and that answers the managerial questions. For multiple proposed systems, we extend previous Bayesian selection procedures to account for discounting and simulation-tool development costs.
Noah Gans, George Knox, Rachel TA Croson (2007), Simple Models of Discrete Choice and Their Performance in Bandit Experiments, M&SOM, Vol 9, 383-408.
Abstract: Recent operations management papers model customers as solving multiarmed bandit problems, positing that consumers use a particular heuristic when choosing among suppliers. These papers then analyze the resulting competition among suppliers and mathematically characterize the equilibrium actions. There remains a question, however, as to whether the original customer models on which the analyses are built are reasonable representations of actual consumer choice. In this paper, we empirically investigate how well these choice rules match actual performance as people solve two-armed Bernoulli bandit problems. We find that some of the most analytically tractable models perform best in tests of model fit. We also find that the expected number of consecutive trials of a given supplier is increasing in its expected quality level, with increasing differences, a result consistent with the models’ predictions as well as with loyalty effects described in the popular management literature.
The Senior Capstone Project is required for all BAS degree students, in lieu of the senior design course. The Capstone Project provides an opportunity for the student to apply the theoretical ideas and tools learned from other courses. The project is usually applied, rather than theoretical, exercise, and should focus on a real world problem related to the career goals of the student. The one-semester project may be completed in either the fall or sprong term of the senior year, and must be done under the supervision of a sponsoring faculty member. To register for this course, the student must submit a detailed proposal, signed by the supervising professor, and the student's faculty advisor, to the Office of Academic Programs two weeks prior to the start of the term.
Quantum engineering - the design, fabrication, and control of quantum coherent devices - has emerged as a multidisciplinary field spanning physics, electrical engineering, materials science, chemistry, and biology, with the potential for transformational advances in computation, secure communication, and nanoscale sensing. This course surveys the state of the art in quantum hardware, beginning with an overview of the physical implementation requirements for a quantum computer and proceeding to a synopsis of the leading contenders for quantum building blocks, including spins in semiconductors, superconducting circuits, photons, and atoms. The course combines background material on the fundamental physics and engineering principles required to build and control these devices with readings drawn from the current literature, including promising architectures for scaling physical qubits into larger devices and secure communication networks, and for nanoscale sensing applications impacting biology, chemistry, and materials science.
For students who are studying a specific advanced subject area in electrical engineering. Students must submit a proposal outlining and detailing the study area, along with the faculty supervisor's consent, to the graduate group chair for approval. A maximum of 1 c.u. of ESE 899 may be applied toward the MSE degree requirements. A maximum of 2 c.u.'s of ESE 899 may be applied toward the Ph.D. degree requirements.
For students working on an advanced research program leading to the completion of master's thesis or Ph.D. dissertation requirements.
OIDD 101 explores a variety of common quantitative modeling problems that arise frequently in business settings, and discusses how they can be formally modeled and solved with a combination of business insight and computer-based tools. The key topics covered include capacity management, service operations, inventory control, structured decision making, constrained optimization and simulation. This course teaches how to model complex business situations and how to master tools to improve business performance. The goal is to provide a set of foundational skills useful for future coursework atWharton as well as providing an overview of problems and techniques that characterize disciplines that comprise Operations and Information Management.
"Managing the Productive Core: Business Analytics" is a course on business analytics tools and their application to management problems. Its main topics are optimization, decision making under uncertainty, and simulation. The emphasis is on business analytics tools that are widely used in diverse industries and functional areas, including operations, finance, accounting, and marketing.
This course covers a range of analytical methods that are useful tools for capacity management in services, and it will provide you with insights into the economics of a range of services businesses including (i) High-level planning models that account for multiple dimensions of service capacity, (ii) Low-level models of system congestion that capture the relationship between capacity choices, quality of service and, in some cases, system revenue, (iii) Statistical estimation and forecasting models to characterize key measures of future supply and demand.
This course introduces you to the essential concepts and techniques required tounderstand and implement revenue management (RM). The need for repeated, rapid and cycles of estimation and optimization has driven the development of a set of analytical tools that are particularly well suited for RM. This course focuses on those tools. Prerequisites: Students who have already taken OIDD 612 and STAT 613 should be well equipped for this class. Other students should have a solid understanding of elementary probability, statistics and constrained optimization. For questions regarding the specifics of your background, please contact the instructor.
Despite record low shipments of personal computers last quarter, an informal survey of Wharton faculty says that PCs -- including laptops and desktops -- are not going away any time soon.Knowledge @ Wharton - 2013/04/23