We introduce and study a fair assortment planning problem, where any two products with similar merits are offered similar visibility. We propose a framework to find near-optimal solutions to this problem, using the Ellipsoid method and a separation oracle to its dual. We then develop two approximate separation oracles, which result in a polynomial-time 1/2-approx. algorithm and an FPTAS for our problem. We conclude with a case study on a movie dataset, showing the efficacy of our algorithms.

Social planners often aim to equitably and efficiently ration a social good among agents whose (possibly correlated) demands realize sequentially. We design a simple adaptive policy that simultaneously achieves the best-possible guarantees on the expected minimum fill rate and the minimum expected fill rate, where each agent's fill rate is determined by an irrevocable, one-time allocation. We complement our results with a numerical study motivated by the rationing of COVID-19 medical supplies.

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.

Relational Continuity (RC) in primary care confers many reported benefits, yet it has been in sharp decline. Using multiple econometric techniques on a large consultation-level dataset comprising of 5M patients registered with 300 primary care practices across the UK over the course of 10 years, we find that RC has a significant productivity benefit, with operational and strategic implications for primary care practices and third-party payers.

The rising demand for Emergency Department (ED) care is partially driven by the failure to provide timely and high-quality primary care, resulting in patients being forced to use EDs. It is therefore important to identify primary care practices (PCPs) whose patients place a lower burden on ED departments so that best practices can be identified and disseminated, and practices whose patients place a higher burden in order to provide support. This work develops and validates one such methodology.

The management of ameliorating food inventories with age-differentiated products entails a trade-off between immediate revenues and further maturation. We derive interpretable decision rules for purchasing, fulfilment, and issuance decisions under purchase price and decay uncertainty. We learn the rules from the optimal policy for an aggregated problem. A linear program facilitates scaling back. For a port wine industry case, our derived management strategies yield a substantial profit increase.

We observe that detecting inventory inaccuracies can be viewed as a problem of identifying anomalies in a (low-rank) Poisson matrix. We propose a conceptually simple approach whose cost approaches that of an optimal algorithm at a min-max optimal rate. Using data from a consumer goods retailer, we show that our approach provides up to a 10× cost reduction over incumbent approaches to anomaly detection.

Entrepreneurs often use crowdfunding to solicit feedback from customers to improve their products, and may therefore prefer to launch crowdfunding campaigns for a basic version of their products. Yet, customers may not be persuaded by a campaign if a product appears too basic. Analyzing a game-theoretical model and testing its predictions empirically, we study how a product’s level of enhancement at campaign launch influences product improvements during campaign and campaign success.

To solve the cold start problem on advertising platforms, we build a data-driven optimization model that captures the essential trade-off between short-term revenue and long-term market thickness on the platform, and propose a bandit algorithm to solve the problem. We also demonstrate the effectiveness of our algorithm via a novel two-sided randomized field experiment, and show our algorithm increases the cold start success rate by 62% and boosts the platform’s overall market thickness by 3.1%.

We present a dynamic experimental design for settings where the experimental units are coarse (e.g. to mitigate interference). `Region-split' experiments on online platforms are one such setting. Our design, dubbed Synthetically Controlled Thompson Sampling (SCTS), minimizes the cost (i.e. regret) associated with experimentation at no meaningful loss to inferential ability. We provide theoretical guarantees and experiments highlighting the merits of SCTS relative to fixed and switchback designs.

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.

We examine how employer-driven volatility in workers' schedules impacts their decision to voluntarily leave their job. Using time-stamped work log data of home health nurses, we construct and study an operational measure of schedule volatility. Using an instrumental variables approach, we find that higher levels of schedule volatility substantially increase workers' likelihood of quitting. Using policy simulations, we illustrate how schedule volatility, and employee turnover, could be mitigated.

Using a data set assembled from detailed ED visit-level data and exhaustive billing data in an integrated health system, we show that there is an increasing concave relationship between ED physician workload and post-ED care use. Further, we identify ED physician test ordering behavior as a mechanism of these effects. Together, these findings suggest that when ED physician workload increases, resource utilization increases in the ED and several other channels of care in the healthcare system.

A firm’s cash flow from operations is a function of the contemporaneous and lagged values of its operational variables---sales, operating cost, inventory, payables, etc. Estimating this function is important for forecasting and managing cash flows. However, cash flow forecasting is a challenging problem. In this paper, we propose a generalizable and data-driven model of a firm's operations to disentangle this endogeneity and estimate causal impacts among variables.

We study the data-driven newsvendor problem in which the decision-maker must trade-off underage and overage costs and only observes historical demand. Our metric of interest is the worst-case relative expected regret, compared to an oracle knowing the demand distribution. We provide an exact analysis of Sample Average Approximation across all data sizes. We also derive a minimax optimal algorithm and its performance. Our work reveals that tens of samples are sufficient to perform efficiently.

We consider two ways that blockchain can enhance supply chain transparency: (1) making the manufacturer’s sourcing cost transparent to the buyers (vertical cost transparency), and (2) making the ordering status of buyers transparent to each other (horizontal order transparency). We develop prescriptions to supply chain practitioners with regard to when blockchain should be adopted, who should be the initiator, and how to design the blockchain’s access control layer for the logistics data.

Accounts receivable can be turned into digital assets that program ownership and the flow of cash into transferable tokens that can either be sold on at a discount via factoring or be passed on to the upstream of the supply chain as a payment instrument. This paper investigates how accounts receivable tokenization impacts the multitier supply chain's decisions and profits under different configurations and contractual forms.

Problem Definition: A large segment of cryptoassets consists of tokens that serve as a payment or governance mechanism for a digital platform, usually a peer-to-peer marketplace of various services. An ongoing debate about the viability of the token market is centered on whether tokens are used purely for speculation, or have actual utility on their underlying platforms. The objective of this study is to create and validate a set of granular measures of token utility and speculation.

We use a text mining approach to extract waiting complaints from over 0.72 million online customer reviews of restaurants and conduct difference-in-differences regressions to estimate the impact of Virtual Queue (VQ). We find that VQ reduces customers' pre-process waiting complaints and does not lead to in-process waiting complaints increase. VQ also enhances customers' overall satisfaction. Service providers who face high substitutability or offer low-value service are benefited most from VQ.

When managing service systems, it is common to use priority rules based on some operational criteria. We consider the societal implications of such individual-focused priority policies, when individuals are members of broader population groups. We find that optimal service policies can lead to significant inequity across population groups. We propose policies that generate equitable outcomes across populations with little, or at times, even no additional system cost.

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.

The COVID-19 pandemic has necessitated widespread deferrals of elective surgeries, which non only increase the cost from deterioration of patients' conditions but also decrease the revenue. We develop a robust optimization framework for the dynamic surgical capacity management problem. Our Implementation for more than 15,000 hernia patients in the United States demonstrates sizable improvements over alternative methods by up to 10% with multiple practical insights on optimal expansion strategy.

We propose and implement a rigorous framework and algorithms to quantify the merits of targeted confinement interventions for pandemic response, and demonstrate them in a case study of COVID-19 in Île-de-France. We find that optimized interventions that differentiate based on both population groups and activities are interpretable, and achieve significantly better health and economic outcomes, while also reducing confinement time for each group, compared to less targeted interventions.

This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In this paper, we focus on nonparametric demand model with some mild technical assumptions which are satisfied by most of the commonly used demand functions.

Various operations management problems can be formulated as stochastic optimization under random truncation. Leveraging a convex reformulation, we propose a mirror gradient method that achieves global convergence for the nonconvex objective with optimal complexity. The proposed method only operates in the original space using estimators of the nonconvex objective and consistently outperforms several state-of-the-art control policies in passenger and air-cargo network revenue management.

We consider a joint customization and assortment optimization problem. A firm faces customers of different types, each making a choice according to a different MNL model. The firm picks an assortment of products to carry subject to a constraint. Then, a customer of a certain type arrives into the system and the firm customizes the assortment that it carries by, possibly, dropping products from the assortment. We study the value of customization, the complexity of the problem and design novel algorithms.

We study the role of financing in establishing supply chain (SC) sustainability. We consider an SC where a buyer sources from a financially constrained supplier, who borrows from either a bank or the buyer. We show that both bank financing and buyer financing cannot simultaneously manage SC sustainability and profitability. We thus propose an alternative, in which the supplier borrows from a bank but the buyer offers a reward if the supplier passes the audit, and demonstrates its effectiveness.

We study the contract design and the effectiveness of the retailer initiated inventory-based financing (IBF) scheme. Using a game-theoretical model, we derive the small retailer’s optimal inventory ordering and pledging decisions during the stockpiling phase and characterize the optimal interest rate for the large retailer. We also empirically study the small retailer’s borrowing pattern and estimate the impact of the interest rate on the small retailers’ planning horizon and the loan amount.

Users in online communities can ask questions and other users can answer these questions. Generally, question answerers get rewards while the askers gain knowledge if their questions get answered. We model the community as a stochastic game and find how users decide to participate in such communities. We theoretically validate the empirically observed network structure in such communities. Further, we find that the number of users in the community is non-monotonic in the participation cost.

Motivated by the debate around drivers' welfare in on-demand transportation, we propose a framework to evaluate current practices and possible alternatives. The platform allocates time slots to drivers, who are strategic agents maximizing their utility, which depends on their temporal preference (when to drive), slots they are allocated, and time they spend on-road. We use our framework to evaluate existing policies and propose improvements with respect to platform profit and drivers' wages.

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.

In many healthcare settings, patients receive a series of services during a single visit, e.g., infusion care and orthopedic visit. A key commonality is the tandem structure where each stage involves a non-trivial random service time. We develop the first analytic model to provide each patient an individualized visit itinerary in a tandem health service system. A case study populated by data from a large infusion center shows that our approach makes a remarkable 27% cost reduction over practice.

Delays in admission to rehabilitation care can be both capacity-driven and/or due to processing delays. Standard data however only includes a single measure of delay, and the bed allocation decisions in practice do not follow a systematic policy. We propose a hidden Markov model to estimate the processing times and the status-quo bed allocation policy. We validate our structural model and conduct counterfactual experiments to evaluate various operational interventions aimed at reducing delays.

In this paper, we explore dynamic ad allocation with limited slots upon each customer arrival for e-commerce platforms when customers follow a choice model to click the ads. Motivated by the recent advocacy for the algorithmic fairness, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. We propose a two-stage stochastic program and design a debt-weighted offer-set algorithm to solve the online problem.

Motivated by the middle-mile delivery operations of an e-retailer, we consider the problem of designing a sparse graph that supports a large matching after random node deletion. We study three families of sparse graph designs (namely, Clusters, Rings, and Erdos Renyi graphs) and show that their performances are close to the complete graph. We test our theory using real data and conclude that adding a little flexibility to the routing network can significantly reduce transportation costs.

We provide the first improvements to the celebrated correlated rounding procedure of Jasin and Sinha (2015), which has become a fundamental problem in multi-item e-commerce order fulfillment.

We derive rounding schemes with guarantees of $1+\ln(n)$ and $d$, where $d$ is the maximum number of fulfillment centers containing an item.

The first of these improves their guarantee of ~n/4 by an entire order of magnitude in terms of the dependence on $n$.

We also show our guarantees to be tight.

Service platforms connecting consumers to independent service providers are now ubiquitous in industries such as ride-hailing, food delivery, and accommodation. We study how a platform may expand capacity through either financing new providers' assets (e.g., cars), or investing in assets directly and employing the providers. We use a game-theoretic model to show when the platform prefers to expand by either scheme compared to conventional bank financing, and how this affects provider profits.

Recent development in the U.S. tariff policies has forced companies to rethink their global operational strategies, particularly whether to add a domestic production location that is immune to tariffs. This paper formulates a three-stage model to analyze the global firm's reshoring capacity, output quantity, and production decisions. We examine how reshoring capacity investment is affected by domestic competition and by tariff uncertainty at both the raw-material and finished-goods level.

We use 5-year data from a hospital with 16 inpatient units to empirically examine whether and how much congestion propagates through the network of inpatient units. We find that the magnitude of the congestion spillover is substantial in our study hospital. We then use counterfactual analyses to empirically identify the bottleneck unit---the unit that has the biggest impact on system performance when an intervention is applied to increase its capacity.

Via an app we developed, we identify a barrier to digital information access by the poor – data shortages. In a Mumbai slum, we randomly assigned respondents to a data plan with daily replenishment cycles – or a standard plan. Our data reveal that absent caps, respondents binge on YouTube and social media, resulting in subsequent data shortages. The capped plan increases late-plan access of WhatsApp invites to health camps, increases attendance at these camps, and reduces social media checking.

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.

Hospitals are rife with alarms, many of which are false. This leads to alarm fatigue, in which clinicians become desensitized and may inadvertently ignore real threats. We develop a partially observable Markov decision process model for recommending dynamic, patient-specific alarms. We find that compared to current approaches of setting patients’ alarms, our dynamic patient-centred model significantly reduces the risk of patient harm.

Split liver transplantation (SLT) can save two lives using one liver. To facilitate increased SLT usage, we formulate a multi-queue fluid model, incorporating size matching specifics, dynamic health conditions, transplant type, and fairness. We find the optimal organ allocation policy, and evaluate its performance versus other common allocations.

Newly emerging smart replenishment systems at the point-of-consumption track product usage via smart, connected devices and use this data to automate order processes. Based on a large industry dataset from the professional coffee industry, we develop models for demand forecasting, inventory control, and replenishment under inventory inaccuracies.

Project networks involve several participants; clients, contractors, and subcontractors; each working on multiple projects concurrently. By tracking a network of 2.6 million public projects over a five-year span, we show that when a project suffers a localized disruption, other projects in the network get delayed because participants reallocate resources to the disrupted project. This creates a domino-effect externality that ripples through the network, causing delays across unrelated projects.

We consider the problem of a 3D printer supplying a critical part installed in multiple machines embedded with sensors and interconnected via IoT. We show that it is optimal to print-to-stock predictively in advance of demand, triggered by a system-lifetime-status dependent threshold. We further quantify the impact of IoT on system cost and inventory by separately assessing the impact of advance demand information from embedded sensors and that of IoT's real-time information fusion.