Increasingly, Consumer Packaged Goods (CPG) companies make use of reusable packaging and manage them via a deposit system. We study a CPG company that offers a product in reusable and disposable packaging, and manages reusable packaging via a deposit system. We formulate a decision model in which the CPG company sets product price and deposit fee under price and deposit sensitive demand while considering packaging durability. We provide analytical conditions for optimality and procedure to solve

Power systems require new approaches to system operations to respond to the increased volatility of solar or wind energy. In this paper, we suggest a novel online demand response program with variable prices. Our program is able to incorporate new information on changing wholesale market conditions while notifying load operators early enough to enable response. We furthermore propose Deep Reinforcement Learning as a tool to identify effective prices.

In this research project, we aim to understand when private households with existing or planned

rooftop solar installations should invest in electricity storage and how these investment decisions

affect their electricity usage patterns as well as the market structure overall. We use a novel household panel datasets to structurally estimate households consumption utility functions and valuations for self-produced solar energy.

I study farmer poverty and productivity with unique data from 2,000 Philippine coconut farms and field work. I find strong evidence that micro-level farming practices account for large productivity differences and that best fertilization practices vary with environment. Supporting organizations should develop customized farming advice and assist farmers with fine details of implementation, an approach not currently preferred, but increasingly available through emerging information technologies.

Firms increasingly recognize the importance of their upstream suppliers’ social responsibility. However, they may fail to heed the unintended negative consequences of their own common practices on the suppliers’ social responsibility decision. Our study shows that cost auditing may undermine the supplier's social responsibility choice, which sheds light on the reluctance of many suppliers to commit to social responsibility programs.

We consider contracting between a supplier of specialty material forgings and a buyer that manufactures airplane components by extensively machining them down. Due to high material removal costs, the buyer prefers forgings to be as similar in geometry and size to the component as possible. We assess the implications of two innovative approaches for improving supply chain performance: (i) Input material reduction incentives via contracting, and (ii) scrap material recycling.

We study if discounts for products at their expiry dates can reduce waste and increase profit. In a Markovian inventory model we obtain combinatorial expressions for the transition rates, but with no informative stationary distribution. In a regime where customer arrivals and order-up-to-level grow large, we obtain via Donsker's theorem expressions for waste and profit. In an MNL setting we prove that optimizing regular prices and discounts always reduces waste compared to not giving discounts.

Consumers often exhibit showrooming behaviour in which they visit a brick-and-mortar (BM) store to gather product information but complete the product purchase in the online channel. Many BM retailers carry exclusive store brand products. We examine how consumer showrooming interacts with a BM retailer's exclusive store brand strategy. Contrary to common notion, our findings reveal that the BM retailer can benefit from consumer showrooming when it carries an exclusive store brand.

We investigate whether horizontal competition drives the increase of the number of product portfolio varieties of self-interested firms that compete for demand through their product portfolio sets. We characterize the equilibrium, in both, the complete information game and the incomplete information game and prove that neither firms have the incentive to go beyond its monopolistic choice. Moreover, we show that proliferation may fail as an entry barrier when the game is played a la Stackelberg.

Many manufacturers frequently introduce new products and retire low-performing SKUs. These portfolio adjustments cause a demand shock for existing products. We study the impact of these demand shocks on the bullwhip effect for existing products. We prove that retiring products always increase the bullwhip effect for existing SKUs while introducing new products does not necessarily lead to this increase. We also study the behavior of the bullwhip effect as function of time remaining to the shock.

We propose a three-step demand forecasting framework that combines the expert's knowledge of the market with the machine learning algorithm's ability to leverage historical information to forecast seasonal demand for rapid innovation products. Using data from Canyon Bicycles, we find a 29% reduction in forecast error (measured by WMAPE) over a purely judgmental forecast.

We present our work with a publicly listed food manufacturer in the UK and a private equity firm that invests in the heavy equipment industry to improve their procurement practice. We used natural language processing and machine learning to organize their vast unstructured procurement data and to classify the suppliers and products into hierarchical categories. With our accompanying decision support tool, we identify the procurement inefficiencies and provides request-for-quote (RFQ) targets.

We predict that societal culture will have a significant impact on inventory management by (1) causing behavioral biases on individual decision makers and (2) affecting the organizational culture. In this paper, we look at the national inventory levels with a dataset that spans the globe. We find that all national characteristics have a statistically significant impact on managing inventory, some in counter-intuitive ways. We also discuss the impact of development status of nations.

Retailers in e-commerce are facing muti-sources of supply chain complexity, making accurate inventory records increasingly important while greatly challenged. This study systematically explores how supply chain complexity affects IRI using a hierarchical segmentation of the complexity sources in e-commerce. Our research contributes a hierarchical framework for supply chain complexity and complements existing literature regarding IRI by systematically analyzing its causes.

We asymptotically characterize and compare risk pooling approaches. We show that physical pooling dominates information pooling in settings with no additional per-location costs. In the presence of such costs, however, information pooling becomes a viable alternative to physical pooling. Through asymptotic analysis, we also address the grouping problem. The convergence of the expected total costs and the base stock levels are demonstrated through a simple numerical illustration.

We develop an optimization framework for infrastructure development to mitigate the risk of flooding caused by sea level rise and storm surge in a coastal area. Expected flood costs are included using a range of possible sea level rise scenarios, and investment costs are modeled for overall infrastructure development assuming budgetary limitations. Using the City of Boston as a case for this study, our methodology resulted in more than 90% cost reduction compared to a “do nothing” strategy.

We analyze the performance of simple (static) pricing policies for the joint pricing and inventory control problem. Compared to dynamic pricing policies, static pricing policies are more tractable, easier to implement and strategy-proof. We consider a continuous review system with Poisson arrivals of unit demand. We construct simple pricing policies that only increase inventory costs by a constant factor while actually increasing revenue, in comparison with the optimal dynamic pricing policy.

This paper investigates the impact of bilateral relationships on contracting incentives in a supply chain from a behavioral perspective. Our experimental data suggests systematic deviations from the theoretical benchmark and reveal behavioral regularities on contracting behavior. We develop a new behavioral theory where a firm's unfavorable bargaining position inflicts distress to a firm. We show that our behavioral theory explains and predicts supply contract bargaining incentives well.

We study the impact of assortment size on consumer choice decisions in an online recommender system context. Via a field experiment on Alibaba's online retail platforms, we causally show that the both consumers' search and purchase likelihoods first increase and then decrease as the number of options increases. We develop a two-stage consumer choice model and demonstrate that our empirical results are consistent with the predictions of a model that incorporates consumers' anticipated regret.

The timeline of product recalls provides corporate insiders with an opportunity to sell stocks before the market reacts to a recall. In this paper, we examine whether such insider trading occurs during the product recall process. Our results show that insider trading is present and that directors but not officers seem to engage in it in the days following defect awareness. Thus, we identify the product recall process as a novel source of information that insiders exploit for personal gains.

How should manufacturers sell network goods through retailers? We study this new supply chain contracting problem, where the retailer can privately observe and control the evolving market conditions. The optimal contract resembles the second-best in the short run, but converges to the first-best in the long run. We guide practice why manufacturers should over supply, mitigate network effects, favor incumbent retailers, and improve retailer information capability, despite information asymmetry.

Insurers have started incentivising customers for buying smart home security products along with home insurance to achieve a reduction in hazard likelihood. In this paper, we study the discounting decision of the insurer and pricing and quality decisions of the smart product manufacturer for offering "smart home insurance" to customers under no-contract, a Wholesale price contract and a Cost-sharing contract, considering (1) equal market power and (2) having a dominant SPM separately.

This work presents a computational study of two-stage stochastic programming and budget-uncertainty robust optimization for capacitated lot-sizing under uncertain demand. To solve the stochastic models, a Benders decomposition approach is tailored to the problem. The tradeoff between computational time and performance on out-of-sample scenarios is investigated. Managerial insights are provided by analyzing the structure of the obtained production plans and the impact of flexibility in planning.

This paper proposes an integrated methodology to optimize tactical production planning and strategic buffer placement in complex capacity constrained high-tech manufacturing supply chains. We introduce a novel multi-stage stochastic programming model that simultaneously tackles demand and lead time uncertainty. For extended planning horizons, we establish a data-driven rolling horizon-based decision framework to derive efficient buffer replenishment policies for varying service levels.

We study frictions in foreign exchange market. We consider two companies with opposite needs of currencies. They can negotiate an exchange between themselves. Forward contract is where negotiation happens prior to randomness resolution, while ad-hoc contract after. The forward contract has a larger potential in increasing quantity decisions due to prior commitment. Ad-hoc contract leads to either unique or continuum of equilibrium(a). Payoff dominance uniquely selects an equilibrium.

The Operating Rooms (ORs) are some of the most expensive and resource intensive areas of healthcare delivery. We developed and implemented a unique method to overbook surgeon blocks, i.e. assigning more OR block time than we have OR capacity to improve OR access. Using Mixed Integer Quadratic Programming, we spread the risk of overbooking equitably and enable block overbooking through an automated process for the front-line to manage and sustain easily.

To efficiently schedule operating rooms (ORs) in complex inpatient surgical departments, we consider separating the more predictable elective surgeries from the less predictable elective and non-elective surgeries. We solve this problem by heuristics and simulation. Using the data from a university hospital, we find that the partitioning can considerably reduce the cancellation rate and can fairly reduce the elective access times without much damaging the non-elective access times.

Admission and discharge decisions play important roles in hospital intensive care unit (ICU) bed capacity management. In this model, we formulate the readmission of patients as an endogenous process that relies on previous discharge decisions. We develop a model to jointly consider early discharge decisions and admission control, including emergency diversion and elective scheduling. By applying the riskiness index, we can reformulate the problem and solve it efficiently.

This work examines how health insurance companies (HICs) can exert effort and offer cash rewards to nudge patients towards cost-effective providers. We build a stylized model to analyze the HIC’s optimal effort and reward, individually and jointly, under different cost-share structures. We find that neither a reward-only nor an effort-only approach uniformly outperforms the other, and HIC strictly benefits most from the joint approach when the price difference is modest.

Patients with cystic fibrosis disease experience frequent pulmonary exacerbation and require antibacterial treatments. Intravenous aminoglycosides are the primary choice but they cause hearing loss. To detect possible hearing loss, there are several hearing assessment methods available. The overarching aim of this research is to design cost-effective strategies to monitor pediatric patients with CF disease to detect potential hearing loss and improve their quality of life using a hearing aid.

We study incarceration diversion decisions with community corrections as an alternative to jailing to alleviate the prominent issue of jail overcrowding. We formulate an MDP model to optimize incarceration diversions for individuals of different risks. To tackle the curse-of-dimensionality caused by non-memoryless, we develop a novel two-timescale approximation embedded in an actor-critic policy gradient algorithm. We provide structured insights for diversion decisions and service capacities.

Personalized intervention management in healthcare has received a rapidly growing interest. A key challenge for personalization is data scarcity. In this research, we develop data-pooling technique in the reinforcement learning (RL) context to address the small sample issue. We develop a novel data-pooling estimator and establish theoretical performance guarantee. We demonstrate its empirical success on a real hospital dataset with an application to reduce 30-day hospital readmission rate.

We rigorously investigate whether and to what extent variations in the (i) the physical context where training is administered (i.e., onsite vs. offsite training); (ii) the familiarity of the trainer who administers the training (i.e., familiar vs. unfamiliar trainer); and (iii) the timing of the week when training is administered (i.e., early-week vs. mid-week vs. late-week training) impact the learning outcomes of health workers and subsequently the likelihood of health commodity stock-outs.

We investigate how the Degree of Standardization across service chain-belonging units impacts performance outcomes. We find that nursing homes that customize service delivery and standardize customer mix tend to experience improved financial performance; those that standardize customer mix tend to experience improved clinical outcomes, and those that customize service delivery tend to experience enhanced resident welfare.

We develop a queueing model to represent a clinic with two types of strategic patients who choose between making an appointment, incurring type-dependent indirect wait cost, and walking in, bearing an inconvenience cost and a risk of being rejected. We focus on the clinics' decisions to allocate slots to walk-ins and appointments to maximize their revenues. The system is analyzed under observable and unobservable settings. The model assumptions and results are examined via a simulation platform.

This study improves our understanding of how workload affects service speed by analyzing patient flow through the ED at a high resolution. We exploit a novel dataset and a nonparametric ML method to track multiple dimensions of workload in realtime. We find the service rate resembles the hazard of a log-normal distribution, nurse load has a greater impact on service speed than physician load, which in turn has a greater impact than system load, and a clearer picture of the system workload.

A key challenge to deploying reinforcement learning in practice is safe exploration. We propose a reinforcement learning algorithm that provably satisfies a safety constraint where it uniformly improves performance at each iteration while achieving sublinear regret. We experimentally validate our results on a sepsis treatment task and an HIV treatment task, demonstrating that our algorithm can learn while ensuring good performance compared to the baseline policy for every patient.

This paper proposes a multiple-choice rank list model to extend the classic discrete choice model by allowing customers to choose multiple distinct alternatives. In addition, we propose a framework to extract customers' preferences from the clickstream data. The corresponding assortment optimization and personalization problems can be solved by mixed-integer linear programs. Numerical experiments based on Cainiao Network showcase the predictive power of the proposed model.

We propose and analyze a sequential design of price experimentation that balances the learning and earning trade-off in revenue management. Assuming the demand function belongs to a parametric family with an unknown parameter value, we derive a closed-form stopping rule based on the observed Fisher information. The decision maker adaptively stops learning and optimizes a price based on the cumulative information and there is no need to find an optimal “fixed” sample size a priori.

A broker matches suppliers with a single buyer in an industrial market by submitting bids to procure goods to the suppliers. After bids are evaluated, the broker learns the quantities procured and ships the goods to the buyer to meet its demand. Modeling the broker’s problem as a new type of newsvendor network problem, we study the effect of problem parameters and uncertainty on the optimal bids as well as conditions under which it is optimal for the broker to bid at multiple supply locations.

Ridesharing platforms have traditionally implemented dynamic walking, which asks passengers to walk a little towards the car in order to achieve more efficient matches. Using novel models and extremely detailed Lyft data, we propose the new paradigm of static walking, which communicates a predetermined pickup location to the rider.

Discovery program is an affirmative action policy used by NYC Department of Education to increase the number of disadvantaged students at specialized high schools. We show that the discovery program suffer many drawbacks both in practice and in theory, and explore possible replacements. We propose a minimal yet powerful modification of the current implementation via the joint seat allocation mechanism, which we show would improve the welfare of disadvantaged students maximally.

In this paper, we study how schools work around insufficient government funding with supplemental resources from nonprofits. We ask: (i) How do resource-supplementing workarounds differ across schools with different socioeconomic advantage? (ii) What policies can ensure workarounds do not exacerbate educational inequities? We answer thee questions by applying Little's Law with validation from 62 interviews from six strategically sampled schools with different levels of socioeconomic advantage.

We perform a game-theoretic analysis of two approaches to finding adoptive parents for children in foster care. We develop a new search-and-matching model and provide analytical results that suggest several advantages of having children's caseworkers drive the search process rather than prospective parents. Numerical case studies show that caseworker-driven search can result in both reduced search efforts and better matches for children.

The food delivery market migrates to platforms that allow optimizing courier routing by sharing couriers among many restaurants. We address the question: how courier sharing contribute to the reduction of delivery costs? We consider a spatial queuing model in which couriers are servers. We show that in several scenarios dedicated courier system achieves higher profit than a shared courier system. This result can be attributed to the imbalance in the courtier allocation that sharing creates.

Many platforms let guests reserve assets ahead of the rental start time. We find that this feature also allows hosts to decide when to create a listing of their asset, especially given most platforms use a commission contract along with dynamic prices. We use a game-theoretic framework to model such platforms. We theoretically find conditions where the hosts withhold asset availability information and find empirical support for the same. We find the optimal contract for such platforms.

We propose, analyze and solve three decision optimization models for online retailers to make personalized upgraded substitution and co-purchase recommendations. Specifically, we explore: (i) pure expected revenue maximization, (ii) maximization of a weighted average of the expected revenue and the expected consumer surplus, and (iii) maximization of the expected revenue with a constraint on the minimum expected size of the shopping basket.