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To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Such targeting is potentially contentious, so rigorously quantifying its benefits and downsides is critical for designing effective and equitable pandemic control policies. We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: population group characteristics and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation in a case study focused on the Ile-de-France region of France, based on commonly available hospitalization, community mobility, social contacts and economic data. We find that optimized dual-targeted policies have a simple and explainable structure, imposing less confinement on group-activity pairs that generate a relatively high economic value prorated by activity-specific social contacts. When compared to confinements based on uniform or less granular targeting, dual-targeted policies generate substantial complementarities that lead to Pareto improvements, reducing the number of deaths and the economic losses overall and reducing the time in confinement for each population group. Since dual-targeted policies could lead to increased discrepancies in the confinements faced by distinct groups, we also quantify the impact of requirements that explicitly limit such disparities, and find that satisfactory intermediate trade-offs may be achievable through limited targeting. Significance Statement In the fight against pandemics such as COVID-19, policy makers rely on interventions that target distinct groups of individuals or activities for confinement. Such targeting can however lead to contentious policies that excessively confine certain groups like the elderly, so it is critical to understand its relative merits. We propose and implement a rigorous framework to quantify these merits and demonstrate it in a case study of COVID-19 interventions in Ile-de-France. We find that optimally designed interventions that differentiate based on both population groups and activities achieve significantly better health and economic outcomes overall and also reduce confinement time for each group, compared to less targeted interventions. The implementation, publicly available at http://insead.arnia.ro, is flexible and portable to other geographies.

To respond to pandemics such as COVID-19, policy makers have relied on interventions that target specific population groups or activities. Such targeting is potentially contentious, so rigorously quantifying its benefits and downsides is critical for designing effective and equitable pandemic control policies. We propose a flexible modeling framework and a set of associated algorithms that compute optimally targeted, time-dependent interventions that coordinate across two dimensions of heterogeneity: population group characteristics and the specific activities that individuals engage in during the normal course of a day. We showcase a complete implementation in a case study focused on the Ile-de-France region of France, based on commonly available hospitalization, community mobility, social contacts and economic data. We find that optimized dual-targeted policies have a simple and explainable structure, imposing less confinement on group-activity pairs that generate a relatively high economic value prorated by activity-specific social contacts. When compared to confinements based on uniform or less granular targeting, dual-targeted policies generate substantial complementarities that lead to Pareto improvements, reducing the number of deaths and the economic losses overall and reducing the time in confinement for each population group. Since dual-targeted policies could lead to increased discrepancies in the confinements faced by distinct groups, we also quantify the impact of requirements that explicitly limit such disparities, and find that satisfactory intermediate trade-offs may be achievable through limited targeting.

Problem definition: Loyalty programs (LPs) introduce a new currency—the points—through which customers transact with firms. Such points represent a promise for future service, and their monetary value thus counts as a liability on the issuing firms’ balance sheets. Consequently, adjusting the value of points has a first-order effect on profitability and performance and emerges as a core operating decision. We study the problem of optimally setting the points’ value in view of their associated liabilities. Academic/practical relevance: Firms across numerous industries increasingly utilize LPs. The sheer magnitude of LPs coupled with recent changes in accounting rules have turned the associated liabilities into significant balance-sheet items, amounting to billions of dollars. Managers (from chief financial officers to chief marketing officers) struggle with the problem of adjusting the points’ value in view of these liabilities. Academic work is primarily aimed at understanding LPs as marketing tools, without studying the liability angle. Methodology: We develop a multiperiod model and use dynamic programming techniques and comparative statics analysis. Results: We show that the optimal policies depend on a new financial metric, given by the sum of the firm’s realized cash flows and outstanding deferred revenue, which we refer to as the profit potential. The total value of loyalty points is set to hit a particular target, which increases with the profit potential. We find that loyalty programs can act as buffers against uncertainty, with the value of points increasing (decreasing) under strong (weak) operating performance and increasing with uncertainty. Managerial implications: Setting the point values and adjusting operating decisions in view of LP liabilities should be done by tracking the firm’s profit potential. Loyalty programs can act as hedging tools against uncertainty in future operating performance, which provides a new rationale for their existence, even in the absence of competition.

We study sourcing in a supply chain with three levels: a manufacturer, tier 1 suppliers, and tier 2 suppliers prone to disruption from, e.g., natural disasters such as earthquakes or floods. The manufacturer may not directly dictate which tier 2 suppliers are used but may influence the sourcing decisions of tier 1 suppliers via contract parameters. The manufacturer’s optimal strategy depends critically on the degree of overlap in the supply chain: if tier 1 suppliers share tier 2 suppliers, resulting in a “diamond-shaped” supply chain, the manufacturer relies less on direct mitigation (procuring excess inventory and multisourcing in tier 1) and more on indirect mitigation (inducing tier 1 suppliers to mitigate disruption risk). We also show that while the manufacturer always prefers less overlap, tier 1 suppliers may prefer a more overlapped supply chain and hence may strategically choose to form a diamond-shaped supply chain. This preference conflict worsens as the manufacturer’s profit margin increases, as disruptions become more severe, and as unreliable tier 2 suppliers become more heterogeneous in their probability of disruption; however, penalty contracts—in which the manufacturer penalizes tier 1 suppliers for a failure to deliver ordered units—alleviate this coordination problem. This paper was accepted by Yossi Aviv, operations management.

We study the inefficiencies stemming from a firm’s operating flexibility under debt. We find that flexibility in replenishing or liquidating inventory, by providing risk-shifting incentives, could lead to borrowing costs that erase more than one-third of the firm’s value. In this context, we examine the effectiveness of practical and widely used covenants in restoring firm value by limiting such risk-shifting behavior. We find that simple financial covenants can fully restore value for a firm that possesses a midseason inventory liquidation option. In the presence of added flexibility in replenishing or partially liquidating inventory, financial covenants fail, but simple borrowing base covenants successfully restore firm value. Explicitly characterizing optimal covenant tightness for all these cases, we find that better market conditions, such as lower inventory depreciation rate, higher gross margins, or increased product demand, are typically associated with tighter covenants. Our results suggest that inventory-heavy firms can reap the full benefits of additional operating flexibility, irrespective of their leverage, by entering simple debt contracts of the type commonly employed in practice. For such contracts to be effective, however, firms with enhanced flexibility and/or operating in better markets must also be willing to abide by more and/or tighter covenants. This paper was accepted by Serguei Netessine, operations management.

Many operational settings share the following three features: (i) a centralized planning system allocates tasks to workers or service providers, (ii) the providers generate value by completing the tasks, and (iii) the completion of tasks influences the providers' welfare. In such cases, the planning system's allocations often entail trade-offs between the service providers' welfare and the total value that is generated (or that accrues to the system itself), and concern arises that allocations that are good under one metric may perform poorly under the other. We propose a broad framework for quantifying the magnitude of value losses when allocations are restricted to satisfy certain desirable guarantees to the service providers.} We consider a general class of guarantees that includes many considerations of practical interest arising, e.g., in the design of sustainable two-sided markets, in workforce welfare and compensation, or in sourcing and payments in supply chains, among other application domains. We derive tight bounds on the relative value loss, and show that this loss is limited for any restriction included in our general class. Our analysis shows that when many providers are present, the largest losses are driven by fairness considerations, whereas when few providers are present, they are driven by the heterogeneity in the providers' effectiveness to generate value; when providers are perfectly homogenous, the losses never exceed 50\%. We study additional loss drivers and find that less variability in the value of jobs and a more balanced supply-demand ratio may lead to larger losses. Lastly, we demonstrate numerically using both real-world and synthetic data that the loss can be small in several cases of practical interest.

Currently available medication for treating many chronic diseases is often effective only for a subgroup of patients, and biomarkers accurately assessing whether an individual belongs to this subgroup typically do not exist. In such settings, physicians learn about the effectiveness of a drug primarily through experimentation—i.e., by initiating treatment and monitoring the patient’s response. Precise guidelines for discontinuing treatment are often lacking or left entirely to the physician’s discretion. We introduce a framework for developing adaptive, personalized treatments for such chronic diseases. Our model is based on a continuous-time, multi-armed bandit setting where drug effectiveness is assessed by aggregating information from several channels: by continuously monitoring the state of the patient, but also by (not) observing the occurrence of particular infrequent health events, such as relapses or disease flare-ups. Recognizing that the timing and severity of such events provide critical information for treatment decisions is a key point of departure in our framework compared with typical (bandit) models used in healthcare. We show that the model can be analyzed in closed form for several settings of interest, resulting in optimal policies that are intuitive and may have practical appeal. We illustrate the effectiveness of the methodology by developing a set of efficient treatment policies for multiple sclerosis, which we then use to benchmark several existing treatment guidelines. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2793 . This paper was accepted by Noah Gans, stochastic models and simulation.

We consider a system with an evolving state that can be stopped at any time by a decision maker (DM), yielding a state-dependent reward. The DM does not observe the state except for a limited number of monitoring times, which he must choose, in conjunction with a suitable stopping policy, to maximize his reward. Dealing with these types of stopping problems, which arise in a variety of applications from healthcare to finance, often requires excessive amounts of data for calibration purposes and prohibitive computational resources. To overcome these challenges, we propose a robust optimization approach, whereby adaptive uncertainty sets capture the information acquired through monitoring. We consider two versions of the problem—static and dynamic—depending on how the monitoring times are chosen. We show that, under certain conditions, the same worst-case reward is achievable under either static or dynamic monitoring. This allows recovering the optimal dynamic monitoring policy by resolving static versions of the problem. We discuss cases when the static problem becomes tractable and highlight conditions when monitoring at equidistant times is optimal. Lastly, we showcase our framework in the context of a healthcare problem (monitoring heart-transplant patients for cardiac allograft vasculopathy), where we design optimal monitoring policies that substantially improve over the status quo recommendations. This paper was accepted by Chung Piaw Teo, optimization.

This paper compares two different frameworks recently introduced in the literature for measuring risk in a multi-period setting. The first corresponds to applying a single coherent risk measure to the cumulative future costs, while the second involves applying a composition of one-step coherent risk mappings. We summarize the relative strengths of the two methods, characterize several necessary and sufficient conditions under which one of the measurements always dominates the other, and introduce a metric to quantify how close the two risk measures are. Using this notion, we address the question of how tightly a given coherent measure can be approximated by lower or upper-bounding compositional measures. We exhibit an interesting asymmetry between the two cases: the tightest possible upper-bound can be exactly characterized, and corresponds to a popular construction in the literature, while the tightest-possible lower bound is not readily available. We show that testing domination and computing the approximation factors is generally NP-hard, even when the risk measures in question are comonotonic and law-invariant. However, we characterize conditions and discuss several examples where polynomial-time algorithms are possible. One such case is the well-known Conditional Value-at-Risk measure, which is further explored in our companion paper [Huang, Iancu, Petrik and Subramanian, "Static and Dynamic Conditional Value at Risk" (2012)]. Our theoretical and algorithmic constructions exploit interesting connections between the study of risk measures and the theory of submodularity and combinatorial optimization, which may be of independent interest.

We deal with the problem faced by a portfolio manager in charge of multiple accounts. We argue that because of market impact costs, this setting differs in several subtle ways from the classical (single account) case, with the key distinction being that the performance of each individual account typically depends on the trading strategies of other accounts, as well. We propose a novel, tractable approach for jointly optimizing the trading activities of all accounts and also splitting the associated market impact costs between the accounts. Our approach allows the manager to balance the conflicting objectives of maximizing the aggregate gains from joint optimization and distributing them across the accounts in an equitable way. We perform numerical studies that suggest that our approach outperforms existing methods employed in the industry or discussed in the literature.

We develop a new local search algorithm for binary optimization problems, whose complexity and performance are explicitly controlled by a parameter Q, measuring the depth of the local search neighborhood. We show that the algorithm is pseudo-polynomial for general cost vector c, and achieves a w2/(2w-1) approximation guarantee for set packing problems with exactly w ones in each column of the constraint matrix A, when using Q = w2. Most importantly, we find that the method has practical promise on large, randomly generated instances of both set covering and set packing problems, as it delivers performance that is competitive with leading general-purpose optimization software (CPLEX 11.2).

Firms often finance their inventory through debt and subsequently sell it to generate profits and service the debt. Pricing of products is consequently driven by inventory and debt servicing considerations. We show that limited liability under debt induces sellers to charge higher prices and to discount products at a slower pace. We find that these distortions result in revenue losses that compound over time, leading to some form of performance spiral down. We quantify the extent to which these inefficiencies can be mitigated by practical debt contract terms that emerge as natural remedies from our analysis, and find debt amortization or financial covenants to be the most effective, followed by debt relief and early repayment options. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2862 . This paper was accepted by Serguei Netessine, operations management.

Novel agents have demonstrated enhanced efficacy when combined with other antimyeloma agents especially dexamethasone. The steroid doses employed in myeloma regimens are often poorly tolerated. Therefore, in a phase II clinical trial we investigated the efficacy of a steroid-free combination including bortezomib, pegylated liposomal doxorubicin and thalidomide (VDT regimen). Twenty-three patients with relapsed or refractory myeloma or other plasma cell cancers were treated with the VDT regimen. Patient had a median of five prior therapies and 65.2% were refractory to their last regimen. The overall response rates were 55.5% and 22%, respectively. The median progression free survival was 10.9 months (95% CI: 7.3-15.8) and the median overall survival was 15.7 months (95% CI: 9.1-not reached). Fatigue and sensory neuropathy were the most common side effects noted. We observe that VDT is an effective steroid-free regimen with ability to induce durable remission even in patients with refractory myeloma.

We study the relation between operating flexibility and the borrowing costs incurred by a firm financing inventory investments with debt. We find that flexibility in replenishing or liquidating inventory, by providing risk shifting incentives, could lead to borrowing costs that erase more than a third of the firm’s value. In this context, we examine the effectiveness of practical and widely used covenants in restoring firm value by limiting such risk shifting behavior. We find that simple financial covenants can fully restore value for a firm that possesses a mid-season inventory liquidation option. In the presence of added flexibility in replenishing or partially liquidating inventory, financial covenants fail, but simple borrowing base covenants successfully restore firm value. Explicitly characterizing optimal covenant tightness for all these cases, we find that better market conditions, such as lower inventory depreciation rate, higher gross margins or increased product demand, are typically associated with tighter covenants.Our results suggest that inventory-heavy firms can reap the full benefits of additional operating flexibility, irrespective of their leverage, by entering simple debt contracts of the type commonly employed in practice. For such contracts to be effective, however, firms with enhanced flexibility and/or operating in better markets must also be willing to abide by more and/or tighter covenants.

A loyalty program introduces a new currency -- the points -- through which customers transact with a firm. We study the problem of optimally setting the monetary value of points, i.e., pricing in this new currency, in a multi-period setting. We first show that point pricing is different from cash pricing primarily due to the way points are accounted for, as liabilities on the firm's balance sheet. This introduces subtle channels through which the firm's decisions affect its financial performance, and exacerbates the importance of certain managerial considerations such as taxation or earnings smoothing incentives.We characterize the optimal cash and point pricing policies, and find that they mimic "base-stock, list price'' policies in inventory management. In particular, point prices/values are always set so that the total value of points reaches a "base-stock'' target, and cash prices are charged so as to maximize the firm's cash flows under the optimal loyalty point values. Under a profit-maximizing policy, the total value of loyalty points is set independently of the firm's realized financial performance. In contrast, we find that under the aforementioned managerial considerations, the optimal value of points becomes state-dependent, and is increasing (decreasing) under strong (weak) operating performance. In this sense, our work shows that loyalty points can act as a hedging tool against uncertainty in future performance, providing a new rationale for their existence, even in the absence of competition.

RATIONALE AND OBJECTIVES Pulmonary edema frequently is treated with ventilation but its effects on the distribution of edema, including gravity-dependent gradients as determined by computed tomography (CT) scanning, are unclear. METHODS To study this, 30 to 50 mL 5% albumin in dextran were instilled in both caudal lobes of supine dogs. They were ventilated only on the left side for 1 minute (n = 4), 30 minutes (n = 6), or 60 minutes (n = 6), and the lobes excised, frozen, and imaged in a CT scanner. Regions of interest were outlined on regional CT slices and tissue from corresponding regions taken for measurements of extravascular lung water (quantity of wet lung [Qwl]/dry quantity of lung [dQl] and for histology to grade interstitial and alveolar edema. RESULTS After ventilation for 30 and 60 minutes, the CT density of the left caudal lobes was significantly lower than the right caudal lobes (P < 0.05), with no significant differences in their Qwl/dQl. Although gravity-dependent gradients of Qwl/dQl were demonstrated, they were unaffected by ventilation. Histology showed a trend for more interstitial edema in left caudal lobes ventilated for 60 minutes compared with lobes ventilated for 1 minute (P = 0.054). CONCLUSIONS Ventilation appears to act primarily by maintaining lung aeration and may play a minor role in alveolar fluid clearance.

Understanding the biology of Waldenström macroglobulinemia is hindered by a lack of preclinical models. We report a novel cell line, RPCI-WM1, from a patient treated for WM. The cell line secretes human immunoglobulin M (h-IgM) with κ-light chain restriction identical to the primary tumor. The cell line has a modal chromosomal number of 46 and harbors chromosomal changes such as deletion of 6q21, monoallelic deletion of 9p21 (CDKN2A), 13q14 (RB1) and 18q21 (BCL-2), with a consistent amplification of 14q32 (immunoglobulin heavy chain; IgH) identical to its founding tumor sample. The clonal relationship is confirmed by identical CDR3 length and single nucleotide polymorphisms as well as a matching IgH sequence of the cell line and founding tumor. Both also harbor a heterozygous, non-synonymous mutation at amino acid 265 in the MYD88 gene (L265P). The cell line expresses most of the cell surface markers present on the parent cells. Overall, RPCI-WM1 represents a valuable model to study Waldenström macroglobulinemia.

A multi-period optimal power dispatching problem is considered for a network of energy utilities connected via multiple transmission lines, where the goal is to find the lowest operational-cost dispatching of diverse generation sources to satisfy demand over a time horizon comprised of multiple periods, and consisting of varying power and demand conditions. Our model captures various interactions among the time-varying periods including which generators should be allocated, when they should be brought into use, and the operational costs associated with each. An efficient algorithm is derived that exploits the structure inherent in this multi-period economic dispatch problem. The control options of our optimization model consist of the dispatching order and dispatching amount of available power generators. Our solutions are shown to be globally optimal under conditions that often arise in practice. Numerical experiments based on these solutions and analysis are presented to illustrate our findings.