Sergei Savin

Professor Savin’s research expertise is centered on operational aspects of health care delivery, improving patient access to care, and optimal management of diagnostic and treatment capacity. His articles have appeared in Management Science, Operations Research, and Manufacturing and Service Operations Management, among others, and he also actively participates in editorial activities for several premier journals including Management Science, Operations Research, Manufacturing and Service Operations Management, and Production and Operations Management.

Professor Savin teaches a PhD course on optimization, the core MBA course on Business Analytics, and the core undergraduate course on Operations and Information Management.

Before joining the Wharton School in July 2009, Professor Savin was on the faculty at the Columbia Business School and the London Business School. He received a Ph.D. in Physics from the University of Pennsylvania in 1997 and a Ph.D. in Operations and Information Management from the Wharton School in 2001.

Hessam Bavafa, Lerzan Ormeci, Sergei Savin (Under Review), Optimal Mix of Elective Surgical Procedures Under Stochastic Patient Length of Stay.

Linda Green, Sergei Savin, Yina Lu (2013), Primary Care Physician Shortages Could Be Eliminated Through Use of Teams, NonPhysicians, and Electronic Communication , Health Affiars, 32, pp. 1119.

Houyuan Jiang, Zhan Pang, Sergei Savin (2012), PerformanceBased Contracting for Outpatient Medical Services , Manufacturing and Service Operations Management, 14 (4), pp. 654668.

Adam Powell, Sergei Savin, Nicos Savva (2012), Physician Workload and Hospital Reimbursement: Overworked Servers Generate Less Revenue per Patient , Manufacturing and Service Operations Management, 14 (4), pp. 512528.

TeckHua Ho, Sergei Savin, Christian Terwiesch (2011), Note: A Reply to “New Product Diffusion Decisions Under Supply Constraints” , Management Science, 57 (10), pp. 18111812.

Abstract: In our prior work on product diffusions in presence of a capacity constraint, we postulated that a firm operating in such an environment should always attempt to fulfill as much of the present demand as is possible with the capacity constraint. In other words, the firm would never have demand backlogged while accumulating inventory. In this note, we derive a sufficient condition for the optimality of such fulfillment policy.

Sergei Savin, Houyuan Jiang, Serguei Netessine (2010), Robust Newsvendor Competition Under Asymmetric Information, Operations Research, Articles in Advance, pp. 18.

Abstract: We generalize analysis of competition among newsvendors to a setting in which competitors possess asymmetric information about future demand realizations, and this information is limited to knowledge of the support of demand distribution. In such a setting, traditional expectationbased optimization criteria are not adequate, and therefore we focus on the alternative criterion used in the robust optimization literature: the absolute regret minimization. We show existence and derive closedform expressions for the robust optimization Nash equilibrium solution for a game with an arbitrary number of players. This solution allows us to gain insight into the nature of robust asymmetric newsvendor competition. We show that the competitive solution in the presence of information asymmetry is an intuitive extension of the robust solution for the monopolistic newsvendor problem, which allows us to distill the impact of both competition and information asymmetry. In addition, we show that, contrary to the intuition, a competing newsvendor does not necessarily benefit from having better information about its own demand distribution than its competitor has.

Fausto Gozzi, Carlo Marinelli, Sergei Savin (2009), On Controlled Linear Diffusions with Delay in a Model of Optimal Advertising under Uncertainty with Memory Effects, Journal of Optimization Theory and Applications, 142 (2009), 291321.

Abstract: We consider a class of dynamic advertising problems under uncertainty in the presence of carryover and distributed forgetting effects, generalizing the classical model of Nerlove and Arrow (Economica 29:129–142, 1962). In particular, we allow the dynamics of the product goodwill to depend on its past values, as well as previous advertising levels. Building on previous work (Gozzi and Marinelli in Lect. Notes Pure Appl. Math., vol. 245, pp. 133–148, 2006), the optimal advertising model is formulated as an infinitedimensional stochastic control problem. We obtain (partial) regularity as well as approximation results for the corresponding value function. Under specific structural assumptions, we study the effects of delays on the value function and optimal strategy. In the absence of carryover effects, since the value function and the optimal advertising policy can be characterized in terms of the solution of the associated HJB equation, we obtain sharper characterizations of the optimal policy.

Sergei Savin, Sabri Celik, Alp Muharremoglu (2009), Revenue Management with Costly Price Adjustments, Operations Research, 57 (2009), 12061219.

Abstract: We consider a novel variant of the perishable inventory profit management problem faced by a firm that sells a fixed inventory over a finite horizon in the presence of priceadjustment costs. In economics literature, such priceadjustment costs are widely studied and are typically assumed to include a fixed component (e.g., advertising costs), an inventorydependent component (e.g., inventory relabeling costs), as well as a component that depends on the magnitude of the price adjustment (e.g., cognitive and coordination managerial costs). We formulate the firm's profit management problem as a finitehorizon dynamic program in which the state of the system is described by the inventory level as well as the current price level. We derive firstorder properties of the optimal value function and give a complete characterization of optimal policies for the case of ample inventory. Through a set of examples we demonstrate the complex and counterintuitive nature of optimal priceadjustment policies. Consequently, we focus on developing easily computable and implementable heuristics with demonstrably good performance. To this end, we develop and solve a fluid model based on the original stochastic dynamics and propose three fluidbased heuristic policies. We derive expressions for the expected profit generated by each one of these heuristics when applied to the stochastic problem and derive sufficient conditions for the asymptotic optimality of the policies when the initial inventory levels and planning horizons are proportionally scaled up. We test the performance of the heuristics in a numerical study and demonstrate a robust, nearoptimal performance of one of the heuristics (which we call the “Fluid Time” heuristic) for a wide range of problem parameters. Finally, we demonstrate the importance of proper accounting of priceadjustment costs in several alternative business settings.

Omar Besbes and Sergei Savin (2008), Going Bunkers: Joint Route Selection and Refueling Problem, Manufacturing and Service Operations Management, 11 (2009), 694711.

Abstract: Managing shipping vessel profitability is a central problem in marine transportation. We consider two commonly used types of vessels—“liners” (ships whose routes are fixed in advance) and “trampers” (ships for which future route components are selected based on available shipping jobs)—and formulate a vessel profit maximization problem as a stochastic dynamic program. For liner vessels, the profit maximization reduces to the problem of minimizing refueling costs over a given route subject to random fuel prices and limited vessel fuel capacity. Under mild assumptions about the stochastic dynamics of fuel prices at different ports, we provide a characterization of the structural properties of the optimal liner refueling policies. For trampers, the vessel profit maximization combines refueling decisions and route selection, which adds a combinatorial aspect to the problem. We characterize the optimal policy in special cases where prices are constant through time and do not differ across ports and prices are constant through time and differ across ports. The structure of the optimal policy in such special cases yields insights on the complexity of the problem and also guides the construction of heuristics for the general problem setting.

Linda Green and Sergei Savin (2008), Reducing Delays for Medical Appointments: a Queueing Approach, Operations Research, 56 (2008), 152638.

Abstract: Many primary care offices and other medical practices regularly experience long backlogs for appointments. These backlogs are exacerbated by a significant level of lastminute cancellations or "noshows," which have the effect of wasting capacity. In this paper, we conceptualize such an appointment system as a singleserver queueing system in which customers who are about to enter service have a statedependent probability of not being served and may rejoin the queue. We derive stationary distributions of the queue size, assuming both deterministic as well as exponential service times, and compare the performance metrics to the results of a simulation of the appointment system. Our results demonstrate the usefulness of the queueing models in providing guidance on identifying patient panel sizes for medical practices that are trying to implement a policy of "advanced access."

Past Courses

OIDD101 INTRODUCTION TO OIDD

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 computerbased 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.

OIDD612 BUSINESS ANALYTICS

"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.

One of ten faculty nominated by the MBA student body for the Helen Kardon Moss Anvil Award, 2010 MBA Excellence in Teaching Award, 2010 “Goes Above and Beyond the Call of Duty” Award, 2010 Class of 1984 Award, 2010

“Economists Outline Strategy To Counter Primary Care Shortage”, American Medical Association News 01/18/2013 “Doctor Shortage? What Doctor Shortage?”, The Washington Post 01/15/2013 “Calculate Your Ideal Patient Load: How to Strike the Correct Balance”, American Medical Association News 05/12/2008 “An OpenAccess Doctor’s Office”, Business Week 02/12/2007