Yale School of Management

Seminar Series

Operations

The Yale Operations Seminar Series presents recent research papers in operations. The goal is to bring researchers from other universities to the Yale campus to stimulate exchange of ideas and deepen understanding of operations trends. These seminars are geared towards faculty and PhD students interested in operations research and management. Leading operations scholars, both internal and external will present their latest research. Doctoral students will meet with faculty prior to these seminars to review the papers and related literature. Participation in this seminar is required throughout the program.

Operations Seminar Series

Faculty Seminar Series Inaugurated on September 11, 2014

Faculty Coordinator: Associate Professor Vahideh Manshadi – 203.436.5026 
Faculty Support Coordinator: Elizabeth Viloudaki – 203.436.5798
Tuesday Series 11:35 a.m.-12:50 p.m., Edward P. Evans Hall, 165 Whitney Avenue, New Haven, CT, Qian and Yu Classroom #4420.
An email notice with abstract and paper will go out in advance of each talk in the series.  Lunch will be provided.

Current Semester - Fall 2019

Tuesday, September 10th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Oguzhan Alagoz (Visiting Professor of Operations at Kellogg School of Management and Proctor and Gamble Bascom Professor of Industrial Systems Engineering, University of Wisconsin-Madison)
A Mathematical Modeling Framework to Personalize Mammography Screening Decisions
Breast cancer is the most common non-skin cancer and the second leading cause of cancer-death in US women. Although mammography is the most effective modality for breast cancer diagnosis, it has several potential risks, including high false positive rates, which are not very rare. Therefore, the balance of benefits and risks, which depend on personal characteristics, is critical in designing a mammography screening schedule. In contrast to prior research and existing guidelines which consider population-based screening recommendations, we propose a personalized mammography screening policy based on the prior screening history and personal risk characteristics of women. We formulate a finite-horizon partially observable Markov decision process (POMDP) model for this problem. Our POMDP model incorporates two methods of detection (self or screen), age-specific unobservable disease progression, and age-specific mammography test characteristics. We use a validated micro-simulation model based on real data in estimating the parameters and solve this POMDP model optimally for individual patients. Our results show that our proposed personalized screening schedules outperform the existing guidelines with respect to the total expected quality-adjusted life years, while significantly decreasing the number of mammograms. We further find that the mammography screening threshold risk increases with age. We derive several structural properties of the model, including the sufficiency conditions that ensure the existence of a control-limit policy. Finally, we briefly describe our findings using two extensions to the basic POMDP model. The first extension personalizes breast cancer screening while accounting for the nonadherence of women to screening recommendations and the second extension considers allocating limited breast cancer screening resources, a common problem in developing countries and resource-limited settings.

Tuesday, September 24th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Florin Ciocan (Assistant Professor of Technology and Operations Management, INSEAD)
Interpretable Optimal Stopping
Optimal stopping is the problem of deciding when to stop a stochastic system to obtain the greatest reward, arising in numerous application areas such as finance, healthcare and marketing. State-of-the-art methods for high-dimensional optimal stopping involve approximating the value function or the continuation value, and then using that approximation within a greedy policy. Although such policies can perform very well, they are generally not guaranteed to be interpretable; that is, a decision maker may not be able to easily see the link between the current system state and the policy's action. In this paper, we propose a new approach to optimal stopping, wherein the policy is represented as a binary tree, in the spirit of naturally interpretable tree models commonly used in machine learning. We formulate the problem of learning such policies from observed trajectories of the stochastic system as a sample average approximation (SAA) problem. We prove that the SAA problem converges under mild conditions as the sample size increases, but that computationally even immediate simplifications of the SAA problem are theoretically intractable. We thus propose a tractable heuristic for approximately solving the SAA problem, by greedily constructing the tree from the top down. We demonstrate the value of our approach by applying it to the canonical problem of option pricing, using both synthetic instances and instances calibrated with real S&P-500 data. Our method obtains policies that (1) outperform state-of-the-art non- interpretable methods, based on simulation-regression and martingale duality, and (2) possess a remarkably simple and intuitive structure.

Tuesday, October 29th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
John Birge (Jerry W. and Carol Lee Levin Distinguished Service Professor of Operations Management, University of Chicago, Booth)
Dynamic Learning in Strategic Pricing Games
In monopoly pricing situations, firms should optimally vary prices to learn demand. The variation must be sufficiently high to ensure complete learning. In competitive situations, however, varying prices provides information to competitors and may reduce the value of learning. Such situations may arise in the pricing of new products such as pharmaceuticals and digital goods. This paper shows that firms in competition can learn efficiently in certain equilibrium actions which involve adding noise to myopic estimation and best-response strategies. The paper then discusses how this may not be the case when actions reveal information quickly to competitors. The paper provides a setting where this effect can be strong enough to stop learning so that firms optimally reduce any variation in prices and choose not to learn demand. The result can be that the selling firms achieve a collaborative outcome instead of a competitive equilibrium. The result has implications for policies that restrict price changes or require disclosures.

Tuesday, November 12th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Foad Iravani (Assistant Professor of Operations Management, University of Washington, Foster)

Tuesday, December 3th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Karl Sigman (Professor of Industrial Engineering and Operations Research, Columbia University)
 

Upcoming Semester - Spring 2020

Tuesday, January 28th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Shouqiang Wang (Assistant Professor, Operations Management, University of Texas at Dallas, Naveen School of Management)

Tuesday, February 11th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
David Shmoys (Laibe/Acheson Professor of Business Management & Leadership Studies School of Operations Research and Information Engineering Department of Computer Science, Cornell University)

Tuesday, February 25th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Jeho Lee (Professor, Seoul National University Business School)

Tuesday, March 24th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Yaron Shaposhnik (Assistant Professor, University of Rochester, Simon)

Tuesday, April 7th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Jiawei Zhang
(Professor of Information, Operations and Management Sciences, New York University, Stern)

Tuesday, April 21th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Hummy Song
(Assistant Professor of Operations, Information and Decisions, University of Pennsylvania, Wharton)

Tuesday, May 5th, 2019, 11:45 am–12:45 pm, Room #4200 Qian and Yu Classroom
Robert Shumsky
(Professor of Operations Management and Faculty Co-director Master of Health Care Delivery Science Program, Dartmouth, Tuck)
 

Past Semesters - Spring 2019

Karen Smilowitz (Industrial Engineering and Management Sciences, Northwestern University)
Leveraging network structure in covering path problems: An application to school bus routing
In the face of critical budget cuts, many public school districts are looking to reduce transportation expenditures and keep cuts away from the classroom.  Our research team at Northwestern University has been exploring such challenges in partnership with Evanston / Skokie District 65, a preK-8 public school district north of Chicago.  In this talk, I will provide a broad overview of the partnership and present our initial work on the School Bus Routing Problem (SBRP).  The SBRP has been studied by the operations research community for over fifty years, identifying creative routing and scheduling approaches for school districts.  The SBRP itself is a composite of five decision sub-problems:  data preparation, bus stop selection, bus route generation, school bell time adjustment, and route scheduling.
As a first step towards a comprehensive solution approach, we introduce the covering path problem on a grid (CPPG) which finds a cost-minimizing path connecting a subset of points in a grid such that each point is within a predetermined distance of a point from the chosen subset. We leverage the geometric properties of the grid graph which captures the road network structure in many transportation problems, including our motivating setting of school bus routing.  With this network structure, we are able to develop efficient methods to find feasible, high quality solution paths for the CPPG.  Our results for the stylized grid setting establish important building blocks for more general settings.
Tuesday, May 7th, 2019, 11:45 am–12:45 pm, Room #2410

Roman Kapuscinski (John Psarouthakis Research Professor of Manufacturing Management, University of Michigan)
Strategic Behavior of Suppliers in the Face of Production Disruptions
To mitigate supply disruption risks, some manufacturers adopt a flexible sourcing strategy, where they have an option of sourcing from multiple suppliers and assigning different roles (regular and backup) to the suppliers. The paper evaluates the costs and benefits associated with such a flexible sourcing plan when suppliers are strategic price setters. It finds that suppliers’ strategic behavior dramatically changes the outcome.   Rather than promoting the suppliers’ competition, the flexible-sourcing plan may incentivize the suppliers to compete less aggressively.
Minimum Advertised Price Policy: Economic Analysis and Implications
During last twenty years, many brick-and-mortar retailers face competition from online retailers and local discounters. Customers are able to experience products in a brick-and-mortar store but purchase online for lower prices. As a result, brick-and-mortar retailers' sales decrease and they stop promoting or carrying such products. For manufacturers, however, brick-and-mortar retailers play a crucial role by showcasing and advertising products to customers. Resale Price Maintenance (RPM) and Minimum Advertised Price (MAP) are two commonly-used policies intended to protect retailers' margin. In this paper, we build a stylized model to study RPM and MAP under various market situations. In particular, we explore which policy is more beneficial for the manufacturer, retailers and consumers.
Tuesday, April 30th, 2019, 11:45 am-12:45 pm, Room #4410

Martin Lariviere (John L. and Helen Kellogg Professor of Operations, Kellogg School of Business, Northwestern University)
Priority Queues and Consumer Surplus
We examine whether priority queues benefit or hurt customers in a setting in which customers are privately informed of their per-unit-time waiting cost. Implementing a priority queue thus means posting a menu of expected waits and out-of-pocket prices that are incentive compatible. Whether priorities increase or decrease consumer surplus relative to first-in, first-out service depends on the model of customer utility and on the distribution of customer waiting costs. If all customers have the same value of the service independent of their waiting costs, priorities essentially always lower consumer surplus. If a customer’s value of the service is an increasing function of her waiting cost, priorities lower surplus if the distribution of waiting costs has a decreasing mean residual life. If the mean residual life is increasing, then priorities make consumers better off. We show that the results across utility models are linked by an elasticity measure. If an appropriate measure of waiting cost is elastic, consumer surplus falls with priorities. We also explore how priorities impact individual customers and show that they potentially make all customers worse off. It is possible that low priority customers may pay a higher out-of-pocket price than they would under first-in, first-out service. 
Tuesday, April 9, 2019, 11:45-12:45, Room #2410

Jose Guajardo (Haas School of Business, University of California, Berkeley)
How Do Usage and Payment Behavior Interact in Rent-to-Own Business Models? Evidence from Developing Economies
The diffusion of technological innovations in developing economies has been facilitated by the use of rent-to-own business models, which give flexibility to customers by allowing them to make incremental payments over time. Understanding the implications of this flexibility is a fundamental problem for an increasing number of firms operating in these markets. In this paper, we empirically analyze how consumer usage and payment behaviors interact in an application of rent-to-own to the distribution of solar lamps in developing countries. By exploiting the longitudinal variation in the data—and hence accounting for intrinsic differences between customers—the analysis led to three main insights. First, higher usage rates lowered the probability of late payments by customers. Our characterization of this engagement effect enhances existing knowledge of the drivers of payment behavior in these environments. Second, customers often “bundled” payments, making advance payments for future product access. We showed that bundling the initial payment led to lower usage rates (bundling effect), suggesting that firms may not benefit from advance payments upfront, and that they should closely track usage patterns from these customers. Finally, we showed that first-period usage information can improve the accuracy of predictive models of default and that observing usage rates in subsequent periods does not lead to further improvements. Overall, the analysis highlights the importance for firms of jointly tracking and analyzing payment and usage behavior by customers, particularly in initial stages of the adoption process.
Tuesday, March 5, 2019, 11:45-12:45, Room #4410

Mohamed Mostagir (Assistant Professor of Technology and Operations, Ross School of Business, University of Michigan)
Dynamic Contest Design: Theory, Experiments, and Applications
Contests are a common mechanism for extracting effort from participants. Their use is widespread in a variety of settings like workplace promotions, crowdsourcing innovation, and healthcare quality.  One of the pivotal aspects of contest design is the contest's information structure: what information should the contest designer provide to participants and when should this information be revealed?  The answers to these questions have important consequences on the behavior of players and the outcome of the contest, as well as implications for institutional and policy design.  We derive the contest's optimal information disclosure policy within a large class of policies and design a novel experiment to evaluate how these policies perform in the lab.
Tuesday, February 19, 2019, 11:45-12:45, Room #4410

Junior Operations Conference

February 8 and 9, 2018 

  • Maria Ibanez (Harvard)- “Discretionary Task Ordering: Queue Management in Radiological Services”
  • Ali Makhdoumi (MIT)- “Fast and Slow Learning from Reviews”
  • Irene Lo (Columbia)- “Dynamic Matching in School Choice: Efficient Seat Reassignment after Late Cancellations”
  • Daniel Freund (Cornell)- “Models and Algorithms for Transportation in the Sharing Economy”
  • Chloe Glaeser (Wharton)- “Optimal Retail Location: Empirical Methodology and Application to Practice”
  • Joann De Zheger (Stanford)- “Pay It Forward: Sustainability in Smallholder Commodity Supply Chains”

Fall 2018

Marcelo Olivares (Industrial Engineering, Universidad de Chile)
Managing Worker Utilization in Service Platforms: An Empirical Study of an Outbound Call-Center
-joint with Andres Musalem and Daniel Yung

In many service industries, providing prompt response to customers can be an important competitive advantage, especially when customers are time-sensitive. When demand for the service is variable and the staffing requirements cannot be adjusted quickly, capacity decisions require making a trade-off between the responsiveness to customers versus controlling operating costs through worker utilization. To break this trade-off, the service system can operate as a platform with access to a large pool of employees with flexible working hours that are compensated through piece-rates. Examples of these service platforms can be found in transportation, food delivery and customer contact centers, among many others. While this business model can operate at low levels of utilization without increasing operating costs, a different trade-off emerges: in settings where employee training and experience is important, the service platform must control employee turnover, which may increase when employees are working at low levels of utilization. Hence, to make staffing decisions and managing workload, it is necessary to understand both customer behavior (measuring their sensitivity to service times) and employee retention. We analyze this trade-off in the context of an outbound call-center that operates with a pool of flexible agents working remotely, selling auto insurance. We develop an econometric approach to model customer behavior in the context of an out-bound call center, that captures special features of out-bound calls, time-sensitivity and the effect of employee experience. A survival model is used to measure how agent retention is affected by the assigned workload. These empirical models of customers and agents are combined to illustrate how to balance time-sensitivity and employee experience, showing that both effects are relevant in practice to plan workload and staffing in a service platform.
Tuesday, December 4th, 2018, 4:15-5:45 pm, Room #2230

Peter Kolesar (Professor Emeritus, Columbia University, Member, Columbia Water Center)
Breaking the Deadlock: Improving Water-Release Policies on the Delaware River through Operations Research
The Delaware River provides half of New York City's drinking water and impacts water supply for Philadelphia and central New Jersey.  The upper river is a habitat for wild trout and American shad and has suffered three 100-year floods in recent years. The water releases from three New York City dams on the Delaware River's headwaters impact the reliability of the city’s water supply, the potential for downriver floods, and the quality of the aquatic habitat. We describe the efforts of an environmental coalition to revise the Delaware’s release policies to benefit river habitat and fisheries without increasing the New York City's drought risk. Changes in the release policies were constrained by the dictates of a 1954 US Supreme Court decree and the 1961 multi-state/federal Delaware River Basin Compact which mandated unanimity among the states of New York, New Jersey, Pennsylvania and Delaware -- and New York City. These five parties to the 1954 Supreme Court decree have sometimes conflicting water objectives. We describe the analyses, activism and the politics that led to the Delaware River Basin Commission to implement the Flexible Flow Management Program (FFMP), an operations research-based water release framework in October 2007, and substantially improve it in 2017. In addition to meeting its habitat improvement goals and drought- risk constraint, the FFMP algorithm modestly increases flood mitigation and is substantially simpler to administer.  We will discuss the recent dispute between New York City and Jew Jersey over future water rights, and our OR based proposals for further improvements in the FFMP.
Thursday, November 29, 2018, 11:45-12:45 pm,  Room #4420

Yash Kanoria (Associate Professor, Decision, Risk and Operations Division, Columbia Business School, Columbia University)
State Dependent Control of Ride Hailing Systems
-joint with Sid Banerjee and Pengyu Qian

Inspired by ride-hailing and bike-sharing systems, we study the design of state-dependent controls for a closed queueing network model. We focus on the assignment policy, where the platform can choose which nearby vehicle to assign to an incoming customer; if no units are available nearby, the request is dropped. The vehicle becomes available at the destination after dropping the customer. We study how to minimize the proportion of dropped requests in steady state. We propose a family of simple state-dependent policies called Scaled MaxWeight (SMW) policies that dynamically manage the geographical distribution of supply. We prove that under the complete resource pooling (CRP) condition (analogous to the condition in Hall's marriage theorem), each SMW policy leads to exponential decay of demand-dropping probability as the number of supply units scales to infinity. Further, there is an SMW policy that achieves the *optimal* exponent among all assignment policies, and we analytically specify this policy in terms of the customer arrival rates for all source-destination pairs. The optimal SMW policy maintains high supply levels near structurally under-supplied locations. We also propose data-driven approaches for designing SMW policies and demonstrate excellent performance in simulations based on the NYC taxi dataset.
Thursday, November 15, 2018, 4:15-5:45 pm,  Room #2230

Philipp Afèche (Associate Professor of Operations Management, Rotman School of Management, University of Toronto)
Ride-Hailing Networks with Strategic Drivers: The Impact of Platform Control Capabilities on Performance
-joint with Costis Maglaras and Zhe Liu.  (The talk is based on a paper that is currently being revised.)
Motivated by ride-hailing platforms such as Uber and Lyft, we study the problem of matching demand (riders) with self-interested capacity (drivers) over a spatial network. We focus on the performance impact of two operational platform control capabilities, demand-side admission control and supply-side repositioning control, considering the interplay with two practically important challenges: Significant spatial demand imbalances prevail for extended periods of time; and drivers are self-interested and strategically decide whether to join the network, and if so, when and where to reposition when not serving riders. We obtain the following results in the context of a game-theoretic fluid model of a two-location, four-route network.  1. We characterize and compare the steady-state system equilibria under three control regimes, ranging from minimal platform control to centralized admission and repositioning control. 2. We provide a new result on the interplay of platform admission control and drivers’ repositioning decisions: It may be optimal to strategically reject demand at the low-demand location even if drivers are in excess supply, to induce repositioning to the high-demand location. 3. We derive tight upper bounds on the platform’s and the drivers’ gains due to platform controls; these gains are more significant under moderate capacity and significant cross-location demand imbalance.
Thursday, October 25, 2018, 4:15-5:45 pm,  Room #2230

Andre Calmon (Assistant Professor of Technology and Operations Management and the Patrick and Valentine Firmenich Fellow for Business and Society at INSEAD)
Operations Strategy at the Base of the Pyramid: Consumer Education and Reverse Logistics in a Durable Goods Supply Chain
We model and analyze the value of different operations strategies for a social enterprise that distributes durable life-improving technologies to low-income consumers in the Base of the Pyramid (BOP). This distributor considers two strategies: (i) improved consumer education and (ii) improved reverse logistics. We prove that (ii) is always beneficial for the distributor, while (i) may be detrimental. Moreover, we show that this effect is weakened (strengthened) if the consumers' financial distress is high (low) and the distributor highly values product adoptions relative to profits. We use this framework to contrast the value of strategies (i) and (ii) in the BOP for non-profit organizations that primarily value adoption of life-improving products, social enterprises that value both profit and adoption, and for-profit businesses that primarily value profit. We compare these strategies with those in high-income markets where consumers do not suffer extreme financial distress.
Thursday, October 4, 2018, 4:15-5:45 pm, Room #2230

Shane G. Henderson (Professor and Director, School of Operations Research and Information Engineering)
Citi Bike Planning
-joint with Daniel Freund, Nanjing Jian, Eoin O’Mahony and David Shmoys

Cornell’s School of Operations Research and Information Engineering has been working with the bike-sharing company Citi Bike since Citi Bike began operations in New York City in 2013. We provide data analysis and advice about strategy and operations, not just to Citi Bike, but also to its parent company Motivate that operates several bike-sharing programs around the USA. I will describe some of our modeling work with Citi Bike, focusing on a suite of models (not just simulation models) that informs the decision about where to position both racks and bikes around the approximately 750 stations in NYC.
Thursday, September 27, 2018, 4:15-5:45 pm, Room #2230

Spring 2018

Rene Caldentey (Professor of Operations Management, The University of Chicago , Booth)
On the Optimal Design of a Bipartite Matching System
-joint with Philipp Afeche (U. Toronto) and Varun Gupta (U Chicago)
In this talk, we explore the optimal design of matching topologies for a multi-class multi-server queueing system in which each customer class has a specific preference over server types. We investigate the performance of the system from the perspective of a central planner who must decide the set of feasible customer-server pairs that can be matched together under fairness constraints for both customers and servers.
Thursday, February 22, 2018, 4:15-5:45 pm, Room #2230

Johannes Ledolter (Professor of Management Sciences and Statistics & Actuarial Scince, Robert Thomas Holmes Professor, The University of Iowa, Tippie College of Business)
Data Science Projects: Treatment of Visual Loss and Effects of Traumatic Brain Injury
In this talk I review and evaluate useful methods in data science. Data science helps us understand the information in both big and small data sets. In business applications, data science helps us gain a competitive advantage; targeted marketing, made possible by the analysis of millions of transaction data, is a prime example. Data science also helps physicians develop efficient personalized medical treatments and helps researchers understand the links between concussions and blast exposure and injuries to the brain and the visual system. I have been involved in studies from ophthalmology where data science methods have proved useful. These studies focus on the thickness of the retinal nerve fiber layer. The analysis of the thickness in healthy eyes and diseased eyes helps with diagnosis and provides treatment guidance for glaucoma, multiple sclerosis and retinal diseases such as age-related macular degeneration and diabetic eye disease. Thickness measurements also reflect the effects of concussions and traumatic brain injury. Athletes and members of the military are prime candidates for traumatic brain injury. Many deceased NFL players have chronic traumatic encephalopathy (CTE), a degenerative brain disorder associated with repetitive head trauma. Our research indicates that the effects of concussions and blast exposure can be detected earlier in the optic nerve (the gateway to the brain) and the thickness of the retinal nerve fiber layers. I discuss two studies and describe the statistical methods used. The first study involves nerve layer thickness comparisons of glaucoma and normal subjects. The second study on Iowa football players explores whether differences in the thickness of the retinal nerve fiber layers are related to player positions.
Thursday, March 1, 2018, 4:15-5:45 pm, Room #2230

Paat Rusmevichientong (Professor, Data Sciences and Operations, University of Southern California, Marshall School of Business)
A New Approach in Approximate Dynamic Programming for Revenue Management of Reusable Products)
-joint with Huseyin Topaloglu and Mika Sumida (Cornell Tech)

We present a new approach in approximate dynamic programming for revenue management of reusable products. The problem is motivated by emerging industries that rent out computing capacity and fashion items, where customers request products on-demand, use the products for a random duration of time, and afterward return the products back to the firm. The goal is to find a policy that determines what products to offer to each customer to maximize the total expected revenue over a finite selling horizon. For this problem, the firms must simultaneously consider the inventories of available products, along with the products that are currently in use by other customers. So, the resulting dynamic programming formulation is intractable because of the high-dimensional state variable.Using a novel approach for constructing an affine approximation to the value functions, we present a policy that is guaranteed to obtain at least 50% of the optimal expected revenue. Our construction is based on a simple and efficient backward recursion. We provide computational experiments based on the parking transaction data in Seattle. Our numerical experiments demonstrate that the practical performance of our policy is substantially better than its worst-case performance guarantee.
Thursday, March 29, 2018, 4:15-5:45 pm, Room #2230

Hamed Mamani (Associate Professor of Operations Management, Premera Endowed Professor, University of Washington, Foster School of Business)
Payment Models in Healthcare
Healthcare spending accounts for 17.5% of the GDP in the United States and has been rising year after year, which makes this a significant concern for society. There have been many proposals specifically related to the reimbursement models from insurers to providers to curb the healthcare cost. However, many of these proposals are not studied or thoroughly evaluated before they are implemented. To this extent, we consider two healthcare payments: bundled payment (BP) and reference pricing (RP), where we investigate the current policies in healthcare payments, propose new payments that can better align the incentives of the stakeholders to the system optimum outcomes, compare and contrast different payment systems, and support the derivations with empirical evidence. The current reimbursement system for hospitals is mainly fee-for-service (FFS), under which the hospital is reimbursed for every procedure, test, etc. This payment model motivates the hospitals to treat patients more than is needed and to perform unnecessary tests that contribute to excessive healthcare expenditures. There have been many proposals for alternative payment models especially from the Center for Medicare and Medicaid services (CMS). One way of controlling healthcare expenditures is by modifying the reimbursement schemes to share some of the insurers’ risk with providers. In the first part of this talk we consider bundled payment, which is a prime example of such policies. The second part of the talk is related to impacting healthcare spending through cost-sharing mechanisms with patients, which affects the providers’ market shares and, subsequently their pricing strategies. We study reference pricing in this area, and questions that we are trying to address are: what is the impact of RP and if it can achieve its premises or are there unintended consequences?
Thursday, March 29, 2018, 4:15-5:45 pm, Room #2230

Pinar Keskinocak (Willam W. George Chair and ADVANCE Professor and Interim Associate Dean for Faculty Development & Scholarship, College of Enginering and Director of the Center for Health and Humanitarian Systems, Georgia Tech Steward School of Industrial and System Engineering)
Quantitative Models Embedded in Decision-Support Tools for Healthcare Applications
We will discuss a few examples from healthcare, where quantitative models embedded in decision-support tools can improve the quality of decision-making (for patients, physicians, or caregivers) and patient outcomes. Examples will come from a variety of applications, including (i) organ transplant decisions for patients and physicians (considering the survival curve estimates of accepting an organ and undergoing transplant, versus remaining on the waiting list hoping to receive a better quality organ in the future), (ii) catch-up scheduling for vaccinations, (iii) prenatal screening for Down Syndrome, (iv) scheduling of patients to receive a combination of services.

Fall 2017

Nicholas Arnosti (Assistant Professor, Decision, Risk and Operations, Columbia Business School)
“TBA”

Georgia Perakis  (Professor of Operations Research, Statistics and Operations Management, MIT Sloan School of Management)
Peak-End Demand Models and their Impact on Hard Promotion Planning Problems
Promotions are a highly effective marketing tool that can have a significant impact on a retailer’s profit. A strong understanding of how changes in the price affect consumers' purchasing behavior can lead to more effective promotions policies and as a result, to a substantial increase in profit for retailers. Incorporating important consumer behavioral effects in the demand model is crucial in order to better explain the relationship between price and consumer demand. In this talk, we will present a new demand model that relies not only on current and past period prices but also on the minimum price set within a set of past periods. Furthermore, using these as features and employing machine learning tools, we show that this new demand model predicts actual sales more accurately than current methods. We test our prediction approach on sales data from a large retailer and demonstrate that there is a 9% relative improvement in the precision of the demand prediction.
This new demand model also allows us to determine the optimal promotion strategy more efficiently. That is, subsequently, we suggest a compact Dynamic Programming (DP) approach that uses the proposed demand model and examine when this DP solves the problem optimally. That is, we establish some commonly used demand models (including the one proposed in this talk) and illustrate under what conditions the proposed DP solves the promotion planning optimization problem exactly.  In fact, under those conditions we find that for those demand models the optimal promotion strategy is to either fully promote or not promote an item at all. For demand models where these conditions do not hold, we provide an analytical guarantee and illustrate that still the proposed DP yields near optimal solutions fast. Furthermore, on the same sales data we tested our demand prediction approach on, we demonstrate that the proposed DP yields on average a 9.1% increase in profit relative to the retailer's current practices.

Gad Allon (Professor of Operations, Information and Decisions, Wharton, UPenn)
Managing Service Systems in the Presence of Social Networks
We study the optimal service-level differentiation for service organizations whose customers engage in communication through social networks. In our setting, customers learn about service quality from both their experience and the experiences of others as reported through their social networks. We characterize a firm's optimal service differentiation policy in such settings. We then study the value of knowing the structure of the social network. We demonstrate that the value of knowing the social network structure critically depends on the correlation between customers' economic value and their social influence. The social network value is higher if the correlation is lower. We then use a novel dataset with more than 6,000 service providers across 11 cities to empirically show that many service providers face negative correlations and can benefit significantly from obtaining information on how their customers engage in social networks. Furthermore, we empirically demonstrate that a service provider's price range is an important indicator of how their customers' economic value and social influence correlate, with service providers who target high-end markets having more negative correlations.

Mohsen Bayati (Associate Professor of Operations, Information and Technology, Graduate School of Business, Stanford University)
Avoiding the Exploration-Exploitation Tradeoff in Personalized Decision-Making
Growing availability of data has enabled practitioners to tailor decisions at the individual-level. This involves learning a model of decision outcomes conditional on individual-specific covariates (contexts). Recently, \emph{contextual bandits} have been introduced as a framework to study these online and sequential decision making problems. This literature predominantly focuses on algorithms that balance an exploration-exploitation tradeoff, since greedy policies that exploit current estimates without any exploration may be sub-optimal in general. However, exploration-free greedy policies are desirable in many practical settings where experimentation may be prohibitively costly or unethical (e.g. clinical trials).
In this talk we show that, for a general class of context distributions, the greedy policy benefits from a natural exploration obtained from the varying contexts and becomes asymptotically optimal under some assumptions on problem parameters. Motivated by these results, we introduce Greedy-First, a new algorithm that uses only observed contexts and rewards to determine whether to follow a greedy policy or to explore. We prove that this algorithm is asymptotically optimal without any additional assumptions. Through simulations we demonstrate that Greedy-First successfully reduces experimentation and outperforms existing (exploration-based) algorithms.

Arash Asadpour (Assistant Professor of Information, Operations and Management Sciences, NYU, Stern School of Business)
Concise Bidding and Multidimensional Budget Constraints
A major challenge faced by marketers attempting to optimize their advertising campaigns is to deal with budget constraints. The problem is even harder in the face of multidimensional budget constraints, particularly in the presence of many decision variables involved and the interplay among the decision variables through such constraints. Concise bidding strategies help advertisers deal with this challenge by introducing fewer variables to act on. In this talk, we discuss the problem of finding optimal concise bidding strategies for advertising campaigns with multiple budget constraints. We provide approximation algorithms based on ideas from dynamic programming and also a very fast dependent randomized rounding of the LP relaxation that runs in linear time and may be of independent interest.
Our results do not rely on any concavity assumption about the value or the cost functions. Moreover, our results are not limited to cost-per-click models and (as opposed to uniform bidding strategies as the current state of the art) can apply to pay-per-impression or pay-per-conversion models, too. We accompany our theoretical results with experimental findings that show an improvement between 1% to 6% over uniform bidding in case of one budget constraint (and up to 35% for multidimensional budget constraints), and an average increase of 5% over an enhanced version of uniform bidding designed for the problems with multiple budget constraints.

Spring 2017

Gad Allon (Professor of Operations, Information and Decisions, U Penn, The Wharton School)
Two Operations Management Problems in Criminology 
We consider two operations management problems in criminology: using pretrial release and split sentencing to minimize crime subject to a constraint on jail overcrowding, and using technology (ballistic imaging and DNA matching, respectively) to maximize violent (gun and sexual assault, respectively) crime solving subject to a capacity constraint. We use data from Los Angeles County (jails), Stockton CA (ballistic imaging) and Detroit (sexual assault kits) to guide the development of new operations management models to address these problems.

Mor Armony (Professor of Information, Operations, and Management Sciences, NYU, Stern School of Business)
Pooling Queues with Work Averse Servers
Contrary to the classical theory of operations management, recent case studies in healthcare, call centers, and retail indicate that pooling queues may not necessarily result in less expected work in process. In this paper, we propose that this phenomenon may arise when servers are work averse and have some discretion over their choice of service capacity. We distinguish two types of work aversion, namely workload aversion and busyness aversion, and show that dedicated configurations yield less expected work in process than pooled configurations when servers exhibit high degrees of workload aversion or low degrees of busyness aversion. We also find that busyness aversion tends to hurt more to the point that it could negate the operational benefits of queue pooling at their highest potential. Overall, our work suggests that service system designers may need to consider the servers' type and extent of work aversion as well as their degree of capacity choice discretion before pooling their workload.

Michael Steele (Professor of Statistics, Professor of Operations, Information and Decisions, UPenn, The Wharton School)
Expectations: What Makes Them Great?
Everyone knows the Saint Petersburg paradox, but knowledge of the paradox is not always taken to heart when algorithms are designed for making real-time sequential decisions.  There is vast --- and beautiful ---- literature that pays unflinching devotion to decision rules that are designed with the single objective of maximizing an expected reward. The Saint Petersburg paradox suggests that these rule might be disastrous, but in practice things tend to work out quite reasonably.
The question is: Why?  In this talk, I will review some new theoretical work that helps to explain the puzzle. Typically, the key issue is the identification of those dynamic programs where the resulting decision rules are mathematically well-behaved from a probabilistic point of view. In one general class of problems, the Saint Petersburg terror is abated by finding good, easily computed, bounds on the variance of the realized reward. In other contexts, which include several classical combinatorial problems and some well-studied inventory management models, one can even show that the realized rewards are asymptotically normal. The menace of the Saint Petersburg paradox is not entirely overcome, but at least one gains some understanding of why so many dynamic programming solutions have served us so well for so long.

Ilan Lobel (Professor of Operations Management in the Department of Information, Operations and Management Sciences, NYU, Stern School of Business)
Feature-based Dynamic Pricing
We consider the problem faced by a firm that receives highly differentiated products in an online fashion and needs to price them in order to sell them to its customer base. Products are described by vectors of features and the market value of each product is linear in the values of the features. The firm does not initially know the values of the different features, but it can learn the values of the features based on whether products were sold at the posted prices in the past. This model is motivated by a question in online advertising, where impressions arrive over time and can be described by vectors of features. We first consider a multi-dimensional version of binary search over polyhedral sets, and show that it has exponential worst-case regret. We then propose a modification of the prior algorithm where uncertainty sets are replaced by their Lowner-John ellipsoids. We show that this algorithm has a worst-case regret that is quadratic in the dimensionality of the feature space and logarithmic in the time horizon.

Junior Operations Conference

February 2 and 3, 2017

  • John Silberholz (MIT)- “An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer”
  • Hamsa Bastani (Stanford)- “Online Decision-Making Algorithms for Data-Driven Operations”
  • Swati Gupta (MIT)- “Learning Combinatorial Structures”
  • Ali Aouad (MIT)- “Revenue Management in Face of Choice Heterogeneity”
  • Negin Golrezaei (USC)- “Real-Time Optimization of Personalized Assortments”

2016

Noah Gans (Professor of Operations, Information and Decisions, UPenn, The Wharton School)
Parametric Forecasting and Stochastic Programming Models for Call-Center Workforce Scheduling

Brian Tomlin
(Professor of Business Administration, Dartmouth University,Tuck School of Business)
Dual Co-Product Technologies: Implications for Process Development and Adoption

Assaf Zeevi (Professor of Decision, Risk and Operations, Graduate School of Business, Columbia University)
Chasing Bandits: (a bit of) Theory and (some) Applications

Dimitris Bertsimas (Massachusetts Institute of Technology)
Personalized Diabetes Management Using Electronic Medical Records

Pen Sun (Professor in Decision Sciences, Fuqua School of Business, Duke University)
Optimal Contract to induce Continued Effort

Jan Van Mieghem (Professor of Operations, Kellogg School of Management, Northwestern)
Collaboration and Multitasking in Processing Networks: Humans versus Machines

Mustafa Akan (Assistant Professor of Operations Management, The Tepper School of Business, Carnegie Mellon University)
Towards an Equitable Alloction of Organs Among End-Stage Liver Disease Patients

Moshe Kress (Professor of Operations Research, The Naval Postgraduate School)
When is Information Sufficient for Action? Search with Unreliable Yet Informative Intelligence

L. Beril Toktay (Brady Family Chairholder and the Scheller College of Business ADVANCE Professor, Georgia Institute of Technology)
Drawn from Efficient Implementation of Collective Extended Producer Responsibility Legislation and Design Incentives under Collective Extended Producer Responsibility: A Network Perspective

Vivek Farias (Associate Professor of Operations Management, The Sloan School of Management, MIT)
Optimal A-B Testing

Gerard Cachon (Fred R. Sullivan Professor of Operations, Information, and Decisions The Wharton School, University of Pennsylvania)
The Role of Surge Pricing on a Service Platform with Self-Scheduling Capacity

Joseph Milner (Professor of Operations Management, The Rotman School of Management, University of Toronto)
Dynamic Patient Scheduling for Multi-Appointment Health Care Programs

Forrest Crawford (Department of Biostatistics, Yale School of Public Health)
Link-tracing studies of hidden networks in epidemiology  and public health

Junior Operations Conference

January 14 and 15, 2016

  • Ken Moon (Stanford GSB) – “Randomized Markdowns and Online Monitoring”​
  • Ying Liu (NYU) – "Intertemporal Pricing under Minimax Regret"
  • Kimon Drakopoulos (MIT) – “Control of Contagion Processes on Networks”
  •  Daniel Guetta (Columbia) – “Multi-Item Two Echelon Distribution Systems with Random Demands: Bounds and Effective Strategies”
  • Dennis Zhang (Northwesten) – “Does Social Interaction Improve Service Quality? Field Evidence from Massive Open Online Education”
  • Yaron Shaposhnik (MIT) – “Exploration vs. Exploitation: Reducing Uncertainty in Operational Problems”
  • Mohammed Fazel-Zarandi (Toronto) – “Can Supply Chain Flexibility Facilitate Information Sharing?”

2015

Margaret Brandeau (Professor of Medicine and Engineering, Stanford University)
Public Health Preparedness: Our Multi-Billion Dollar Problem/Opportunity

Abraham Seidmann (Xerox Professor of Computers and Information Systems, Electronic Commerce, and Operations Management WE Simon Graduate School of Business Administration, University of Rochester)
The Process Implications of using Telemedicine for Chronically Ill Patients: Analyzing Key Consequences for Patients and the Medical Specialists Practices

Sergei Savin (Associate Professor, University of Pennsylvania, The Wharton School)
Managing Office Revisit Intervals and Patient Panel Sizes in Primary Care

Christopher S. Tang (Carter Professor of Business Administration, UCLA Anderson School)
Economic Value of Market Information for Farmers in Developing Countries

Stephen Chick (Novartis Chaired Professor of Healthcare Management Technology and Operations Management Area, INSEAD)
A Bayesian Decision - Theoretical Model of sequential Clinical Trials with Delayed Responses

Francis de Vericourt (Associate Professor of Technology and Operations Management, INSEAD/ESMT)
TBA

Costis Maglaras (Columbia Business School)
Observational Learning and Abandonment in Congested Systems

Omar Besbes (Columbia University)
Should information collection affect operational decisions? The case of inventory management

Itai Ashlaghi (Assistant Professor of Operations Management, MIT Sloan School of Business)
Incentive Constraints in Random Matching Markets

Junior Operations Conference

February 6, 2015

  • Maxime Cohen (MIT) – “When Data Analytics Meets Promotion Pricing”
  • Daniela Saban (Columbia) – “Procurement Mechanisms for Differentiated Products”
  • Angelo Mancini (Chicago) – “Dynamic Release Management: A Quasi-Open-Loop Approach”
  • Yiangos Papanastasiou  (LBS) – “Crowdsourcing Exploration”

2014

Retsef Levi (J. Spencer Standish (1945) Professor of Management, Professor Operations Management, Sloan School of Management, MIT)
Systematic Approach to Manage Risks of Economically Motivated Food and Drug Adulteration in Supply Chains in China

Felipe Caro (Associate Professor in Decisions, Operations, and Technology Management, UCLA Anderson)
Adoption of a markdown decision support system at Zara

Terry Taylor (Milton W. Terrill Associate Professor of Business Administration, UC Berkeley)
Supplier Evasion of a Buyer's Audit: Implications for Motivating Supplier Social and Environmental Responsibility

Hyun-soo Ahn (Associate Professor of Operations and Management Science, Ross School of Business/University ofMichigan)
The role of cost modeling in competitive bid procurement

Carri W. Chan (Associate Professor, Decision, Risk and Operations, Columbia University)
Critical Care in hospitals: When to introduce a step down unit?

Laurens Debo (Associate Professor of Operations, University of Chicago, Booth School of Business)
Inferring Quality from Wait Time

Sameer Hasija (Assistant Professor of Technology and Operations, INSEAD)
Fleet Management Coordination in Decentralized Humanitarian Operations

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