Research Interests: capacity and patient flow management in health care operations, diffusion models for new products and services, revenue management
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.
Abstract: We develop a model of crowdfunding dynamics that maximizes revenue for a given fundraising campaign by optimizing both the pledge level sought from donors or backers and the duration of the campaign. Our model aligns with the patterns of backer/donor arrival and pledging observed on crowdfunding platforms, such as Kickstarter. Using our model, we calibrate the revenue lost from using pre-specified pledge levels or campaign durations. We show that under the optimal design, the pledge level sought decreases as the goal of a campaign increases, with a more pronounced effect for both very low and very high campaign goals. We further demonstrate how uncertainty in pledge accumulation improves campaign revenue and aids campaign success. In particular, we show that campaigns with high goals benefit from highly uncertain environments more than campaigns with low goals.
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, Non-Physicians, and Electronic Communication, Health Affiars, 32, pp. 11-19.
Houyuan Jiang, Zhan Pang, Sergei Savin (2012), Performance-Based Contracting for Outpatient Medical Services, Manufacturing and Service Operations Management, 14 (4), pp. 654-668.
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. 512-528.
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. 1-8.
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 expectation-based 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 closed-form 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), 291-321.
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 infinite-dimensional 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.
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 price-adjustment costs. In economics literature, such price-adjustment costs are widely studied and are typically assumed to include a fixed component (e.g., advertising costs), an inventory-dependent 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 finite-horizon dynamic program in which the state of the system is described by the inventory level as well as the current price level. We derive first-order 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 price-adjustment 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 fluid-based 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, near-optimal 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 price-adjustment 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), 694-711.
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.
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 constitutes the second part of a two-part sequence and serves as a continuation of the summer math camp. Mathematical optimization provides a unifying framework for studying issues of rational decision-making, optimal design, effective resource allocation and economic efficiency. It is a central methodology of many business-related disciplines, including operations research, marketing, accounting, economics, game theory and finance. In many of the disciplines, a solid background in optimization theory is essential for doing research. This course provides a rigorous introduction to the fundamental theory of optimization. It examines optimization theory in two primary settings: static optimization and optimization over time (dynamic programming). Applications from problem areas in which optimization plays a key role are also introduced. The goal of the course is to provide students with a foundation sufficient to use basic optimization in their own research work and/or to pursue more specialized studies involving optimization theory. The course is designed for entering doctoral students. The prerequisites are calculus, linear algebra and some familiarity with real analysis, as covered in summer math camp. Other concepts are developed as needed throughout the course.