The Trump administration’s 2018 announcement to freeze the EPA standard threatened to widen its gap from the CARB standard and cause a split market where automakers offer differentiated car models in CARB and non-CARB states. Inspired by this crisis, we adopt a game-theoretic model where two regulators set efficiency standards in their respective markets. We show that horizontal negotiations and vertical negotiations can both unify a split market and reduce emissions.

Bike lane expansion promotes cycling and reduces car traffic, but narrows vehicle lanes and amplifies congestion. We study the bike lane planning problem considering the conflicting effects. In an extensive case study in Chicago, we present a consistent estimator for travel-time function and optimize new bike lane locations while enforcing traffic equilibrium. We estimate 25 miles of new bike lanes increase cycling ridership by 76%, with at most an 8% increase in driving time between each OD pair.

Efficiently managing inventories requires an accurate estimation of stockout costs. This estimation is complicated by challenges in determining how to compensate consumers monetarily so that they will maintain the same level of utility had stockouts not occurred. This paper presents an analysis of these compensation costs, as applied to the design of optimal stockout rates by an online retailer selling to consumers aesthetically substandard fruits and vegetables rejected by mainstream grocers.

We examine a grocery retailer's management of premade food. The retailer's objective is to maximize the direct profit plus (weighted) customer welfare generated the food product. The retailer chooses: the shelf life, FIFO vs. LIFO issuance, and whether or not to time-stamp items. Our first main result is that LIFO issuance is universally optimal. Second, the retailer time-stamps items if the disposal cost for unsold items is low or the retailer puts sufficient weight on customer welfare.

The covert nature of sex trafficking provides a barrier to generating large-scale, data-driven insights to inform law enforcement, policy and social work. We leverage massive deep web data (collected from leading commercial sex websites) with a novel machine learning framework to study how and where sex worker recruitment occurs. We provide a geographical network view of commercial sex supply chains, highlighting deceptive recruitment-to-sales pathways that signal high trafficking risk.

Firms use social impact claims to attract workers, but the credibility of these claims is understudied. We suggest that when social impact is presented as corporate purpose, firm capacity is a key source of credibility. Using an online job board, we use topic modeling to confirm that (i) firms present social impact as purpose, (ii) purpose claims attract job seekers, and (iii) the latter effect is moderated by firm size. We experimentally confirm that perceptions of capacity drive our results.

30 million tons of plastic reach the ocean each year, most from developing countries. We study novel business models to address this problem. Firms profitably recycle plastic to reduce ocean pollution while positively impacting local communities. They sell (a) plastic offsets and (b) segregated plastic. We analyze a supply chain model of (a), (b) or both. Adopting both attains larger environmental and social impacts and profitability. We use empirical data to unveil additional insights.

A critical challenge faced by smallholder farmers is that the need for immediate cash often forces them to sell their crops at sub-optimal times. This paper develops a game-theoretic model to examine how cash constraints influence farmers' selling decisions across the harvest and lean seasons, as well as to analyze the efficacy of government loan programs in improving farmers' revenue. Finally, we use field data of Bengal gram farmers in India to empirically validate and quantify our insights.

We study the investment relations among the renewable, flexible, and storage capacities. We optimize the joint operations of these three types of resources. We then optimize the investment mix of these resources and examine the investment relations among them. We find that whether storage complements or substitutes other resources depends on how storage reduces operating cost and whether the potential cost reduction is constrained by charging or discharging.

The rapid expansion of distributed energy resources (DERs) is one of the most significant changes to electricity systems. We study in this paper two models to aggregate DERs. In the first model, a profit-seeking aggregator procures electricity from DERs, and sells them in the wholesale market. In the second model, a uniform two-part pricing policy is applied to DER owners, while the aggregator becomes fully regulated but is guaranteed positive profit. Both models are shown to be fully efficient.

Motivated by trends in the healthcare industry, we study the integration of Acute Care (AC) and Post-Acute Care (PAC). Patients' outcomes depend on both the AC length of stay and the PAC efforts. A central controller that manages both AC and PAC may choose to discharge a patient earlier and ``compensate'' for this by greater PAC efforts. We characterize the optimal effort integration and its dependencies on patient's operational severity, and the extent to which AC and PAC are complements/substitutes.

We propose a method to enhance post-approval safety surveillance of pharmaceuticals. Clinical trials typically do not provide sufficient evidence for flagging rare safety issues. Our approach combines pre-approval clinical trial results with post-approval surveillance data for common and rare adverse effects to decide whether to flag the rare reaction, weighing type I and type II error costs. We analytically show when our approach is most valuable and numerically demonstrate its effectiveness.

In the blood donor management problem, the blood bank incentivizes donors to donate, given blood inventory levels. We propose an optimization model to design donor incentivization schemes that account for the blood inventory dynamics and the donor's donation process. By adopting the Pipeline Queue paradigm, we have a tractable convex reformulation. Numerical results show the advantages of the optimal policy compared with benchmark policies in reducing both shortages and wastage.

Clinical trials are a critical step for the development of new drugs, both in

cost and in terms of elapsed time to bring the potential drug to the market. Since clinical trials are increasingly going global, optimizing the supply chain can bring huge benefits. We use a MDP to model the inventory problem between the central depot and the regional depots that will then supply the investigation sites. The main decision is when to resupply and how much inventory to send to each regional depot.

The Organ Procurement & Transplantation Network (OPTN) is migrating all US organ allocation policies to a novel continuous-distribution model. We introduce a novel analytical framework to illuminate policy tradeoffs and enable exploration of the efficient frontier of policies. Jointly with the OPTN, we applied our framework to the design of a new allocation policy for lungs. Starting in 2023, all deceased-donor lungs in the US will be allocated according to this policy.

We develop a model for a regional decision-maker to analyze the requirement of medical equipment capacity in the early stages of a spread of infections. We use a stochastic differential equation to capture the growth of infections in a community spread and shutdown model. We develop results to determine shutdown time, to show how to compensate for limited medical equipment capacity, and to show how capacity-sharing across regions can deliver a peak-timing benefit beyond traditional risk pooling.

During the early weeks of the COVID-19 pandemic, hospitals managed surge capacities by transferring patients among different hospitals within the same health network, repurposing operating rooms as ICU beds, and cancelling elective surgeries. Using publicly available data we develop an analytical framework to study how these policies can be implemented, individually or in combination, in order to optimize the pandemic response in a region, while delivering care to the uninfected patients.

The classical SIR model and its variants have succeeded in predicting infectious diseases' spread. To better capture the COVID-19 outbreak, we extend the SIR model to impose a testing capacity. We study how to choose the best type of testing method, how to allocate limited testing capacity over time and across symptomatic and asymptomatic people. We use the COVID-19 data and a sliding window method to calibrate our model and point out its public policy implications.

For COVID-19 detection, point-of-care tests are cheap and quick but fail policymakers’ accuracy requirements. We propose a robust optimization methodology for optimally combining results from cheap tests for increased diagnostic accuracy. Combining three rapid tests increases area under the curve by 6% compared with the best performing individual test for antigen detection. We demonstrate that robust optimization is a powerful tool to avoid overfitting and improve out-of-sample performance.

Adaptive approval is a novel regulatory program that enables earlier patient access to new drugs for rare diseases. The program has been in place for almost a decade, however, industry participation has been surprisingly low. We present a Stackelberg game-theoretic model to understand why industry participation has been low. Our results inform healthcare policymakers on ways to redesign adaptive approval programs to likely increase industry participation and improve patients' welfare.

We present a Markov decision model for simultaneously managing the treatment of multiple chronic conditions. We first provide a general framework, then demonstrate under which conditions model complexity can be dramatically reduced--including to that of an index optimal policy. We present a case study on patients with age related macular degeneration, and demonstrate that following our framework could reduce symptoms by 38% and direct medical costs by 23%.

Mobile labs are a promising approach to improving access to health. Although there is a variety of use cases for mobile labs, their usage has been primarily in emergency deployment. This means mobile labs are at risk of being idle if not used in non-emergency settings. We analyse operational models for non-emergency mobile lab deployment. Our results show substantial impact can be generated and help inform decision-making regarding pathogen prioritization and operational models.

Motivated by recent development in predictive analytics, we study how to utilize future demand information to design optimal routing strategies when facing demand surges. We consider a multi-class multi-pool parallel server system with partial flexibility, where overflowing a customer to a non-primary server-pool can be associated with efficiency loss and other costs. Our results explicitly characterize how to incorporate future demand into routing decisions and quantify the benefit of doing so.

Motivated by the need for decision support tools for workload prediction and capacity planning in hospitals under the COVID-19 pandemic, we consider a queueing network consisting of two many-server stations, which models the flow of patients between medical/surgical and intensive care unit wards. We approximate the steady-state customer count using the stationary distribution of the associated diffusion model, which can be computed efficiently and be used as a real-time decision support tool.

Motivated by the challenges in census prediction in data-driven settings for hospital resource management, this paper introduces and studies the patient census calibration problem for hospital networks operating in a random environment. We use the expectation-maximization method to efficiently solve the calibration problem. We present simulation results that demonstrate the efficiency and accuracy of the method, and theoretical analyses that provide large-sample statistical guarantees/

Capitation payment models have been increasingly adopted by the payers in the U.S. healthcare market during the past decade. Through a Difference-in-Difference (DID) design, this paper empirically demonstrates that Medicare Advantage (MA), the largest healthcare capitation program in the U.S., inadvertently incentivizes MA health plans to reallocate parts of the capitation payments from the sick to cross subsidize the healthy, a practice to which we refer as reverse cross subsidization.

Emergency transportation systems in developing countries often lack the capacity and coordination to serve patients.

We study the market entrance decision of an entrepreneur into an ETS in a region where independent ambulance providers compete for demand. The entrepreneur may decide (i) to acquire own ambulances to become a competing service provider, (ii) to operate a pure platform that exclusively coordinates existing providers, or (iii) to coordinate and compete by combining both strategies.

In partnership with IU Health, the largest health system in Indiana with 16 hospitals, we jointly developed a suite of advanced data and decision analytics to support a novel internal travel nursing program. This work addresses a long-standing gap in healthcare between state-of-art data decision support analytics and operational processes. Four months after implementation of our integrated machine learning and optimization tool demonstrated 5% lower understaffing and annualized savings of $900K.

Given the increase in the demand for temporary healthcare workers and their additional cost burden, we propose a two-stage stochastic optimization framework to inform recruitment decision making for a provider facing a period of highly uncertain demand. The optimal recruitment decisions are analytically characterized under a general setting. A case study is conducted to illustrate the application of our framework in an inpatient ward. We also show potential savings from adoption of our model.

This paper develops a queueing game model to investigate the impact of virtual triage in the acute care setting. We find that, when virtual triage excessively recommends emergency (primary) care, it could bring about a decrease in ED (GP) visits. Another finding is for arbitrary self-triage accuracy, the adoption of informative virtual triage can worsen system performance. To unlock the operational benefits, we characterize the optimal virtual triage accuracy subjective to the ROC curve.