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We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with a finite, fixed number of supply units that circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the "origin" to the "destination" of the customer. The key challenge is to manage the distribution of supply in the network. We consider general controls including customer entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a rich family of mirror backpressure (MBP) control policies. The MBP policies are simple and practical and crucially do not need any statistical knowledge of the demand (customer) arrival rates (these rates are permitted to vary in time). Under mild conditions, we propose MBP policies that are provably near optimal. Specifically, our policies lose at most O(KT+1K+ηK) payoff per customer relative to the optimal policy that knows the demand arrival rates, where K is the number of supply units, T is the total number of customers over the time horizon, and η is the demand process' average rate of change per customer arrival. An adaptation of MBP is found to perform well in numerical experiments based on data from NYC Cab. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: Y. Kanoria was supported by the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI-1653477]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4934. [ABSTRACT FROM AUTHOR]

Newborn screening (NBS) is a state-level initiative that detects life-threatening genetic disorders for which early treatment can substantially improve health outcomes. Cystic fibrosis (CF) is among the most prevalent disorders in NBS. CF can be caused by a large number of mutation variants to the CFTR gene. Most states use a multitest CF screening process that includes a genetic test (DNA). However, due to cost concerns, DNA is used only on a small subset of newborns (based on a low-cost biomarker test with low classification accuracy), and only for a small subset of CF-causing variants. To overcome the cost barriers of expanded genetic testing, we explore a novel approach, of multipanel pooled DNA testing. This approach leads not only to a novel optimization problem (variant selection for screening, variant partition into multipanels, and pool size determination for each panel), but also to novel CF NBS processes. We establish key structural properties of optimal multipanel pooled DNA designs; develop a methodology that generates a family of optimal designs at different costs; and characterize the conditions under which a 1-panel versus a multipanel design is optimal. This methodology can assist decision-makers to design a screening process, considering the cost versus accuracy trade-off. Our case study, based on published CF NBS data from the state of New York, indicates that the multipanel and pooling aspects of genetic testing work synergistically, and the proposed NBS processes have the potential to substantially improve both the efficiency and accuracy of current practices. This paper was accepted by Stefan Scholtes, healthcare management. Funding: This work was supported by National Science Foundation [Grant 1761842]. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2021.4289. [ABSTRACT FROM AUTHOR]

Vertical equity or fairness is an important aspect in many settings, yet has received relatively little attention in the literature. Recent developments underline the practical relevance (e.g., COVID-19 vaccination policies). It plays an important role in the performance evaluation of many (nongovernmental) organizations. For example, donors might require a family-planning organization to allocate a minimum fraction of the total utility (client volume) to a particular player (the "high-impact" subgroup of the population, e.g., young and poor clients). However, the price (decrease in client volume) of such requirements is not well-understood. Consequently, this price is not accounted for in decision making. We provide an analytical upper bound on the price (i.e., loss of overall utility) of vertical equity considerations in resource allocation. We assume that these concerns are expressed via outcome constraints, specifying a minimum percentage of the total utility for each player. Our set-up considers a decision maker maximizing total utility over a general convex set, subject to outcome constraints. The set-up is general and applicable to many practical problems. Our results depend only on high-level parameters and are therefore well-suited for strategic decision making. We conclude with two applications. First, we apply our results to practical instances in health delivery. We confirm that outcome constraints can entail a substantial price and analyze the factors driving this price close to the worst-case bound. Second, we analyze how our results can help bound the impact of prioritization in vaccine allocation. This paper was accepted by Jeannette Song, operations management. Supplemental Material: Data files are available at https://doi.org/10.1287/mnsc.2021.4287. [ABSTRACT FROM AUTHOR]

Digitization has given creators direct access to consumers as well as a plethora of new data for suppliers of new products to draw on. We study how this affects market efficiency in the context of book publishing. Using data on about 50,000 license deals over more than 10 years, we identify the effects of digitization from quasi-experimental variation across book types. Consistent with digitization generating additional information for predicting product appeal, we show that the size of license payments more accurately reflects a product's ex post success, and more so for publishers that invest more in data analytics. These effects cannot be fully explained by changes in bargaining power or in demand. We estimate that efficiency gains are worth between 10% and 18% of publishers' total investments in book deals. Thus, digitization can have large impacts on the allocation of resources across products of varying qualities in markets in which product appeal has traditionally been difficult to predict ex ante. This paper was accepted by Joshua Gans, business strategy. [ABSTRACT FROM AUTHOR]

Florida, an important state in presidential elections in the United States, has received considerable media coverage in recent years for long lines to vote. Do some segments of the population receive a disproportionate share of the resources to serve the voting process, which could encourage some or dissuade others from voting? We conduct the first empirical panel data study to examine whether minority and Democrat voters in Florida experience lower poll worker staffing, which could lengthen the time to vote. We do not find evidence of a disparity directly due to race. Instead, we observe a political party effect—all else equal, a 1% increase in the percentage of voters registered as Democrat in a county increases the number of registered voters per poll worker by 3.5%. This effect appears to be meaningful—using a voting queue simulation, a 5% increase in voters registered as Democrat in a county could increase the average wait time to vote from 40 minutes (the approximate average wait time to vote in Florida in 2012 and the highest average wait time across all states in that election per the Cooperative Congressional Election Study) to about 115 minutes. This paper was accepted by Vishal Gaur, operations management. [ABSTRACT FROM AUTHOR]

Many innovative products are designed to satisfy the demand of specific target consumers; thus, the innovators will inevitably compete with each other in the product market. We investigate how a profit-maximizing principal should properly allocate her limited resources to support the innovations of multiple potentially competing innovators. We find that, as the available resources increase, the optimal diversification of investment may first increase and then decrease. This interesting nonmonotone pattern is driven by a trade-off between the risk of innovation failure and rent dissipation because of competition. Using this framework, we also analyze a nonprofit principal seeking to maximize the total number of successful innovations, the probability of at least one innovator succeeding, consumer surplus, and total social welfare. A nonprofit principal tends to invest more diversely compared with a for-profit counterpart. This paper was accepted by Sridhar Tayur, entrepreneurship and innovation. [ABSTRACT FROM AUTHOR]

This paper optimizes dynamic pricing and real-time resource allocation policies for a platform facing nontransferable capacity, stochastic demand-capacity imbalances, and strategic customers with heterogenous price and time sensitivities. We characterize the optimal mechanism, which specifies a dynamic menu of prices and allocations. Service timing and pricing are used strategically to: (i) dynamically manage demand-capacity imbalances, and (ii) provide discriminated service levels. The balance between these two objectives depends on customer heterogeneity and customers' time sensitivities. The optimal policy may feature strategic idlenexss (deliberately rejecting incoming requests for discrimination), late service prioritization (clearing the queue of delayed customers), and deliberate late-service rejection (focusing on incoming demand by rationing capacity for delayed customers). Surprisingly, the price charged to time-sensitive customers is not increasing with demand—high demand may trigger lower prices. By dynamically adjusting a menu of prices and service levels, the optimal mechanism increases profits significantly, as compared with dynamic pricing and static screening benchmarks. We also suggest a less information-intensive mechanism that is history-independent but fine-tunes the menu with incoming demand; this easier-to-implement mechanism yields close-to-optimal outcomes. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. [ABSTRACT FROM AUTHOR]

The literature on resource allocation under adverse selection has focused on models in which the resource being allocated is such that the privately informed agent always prefers more of it to less. We analyze a firm's optimal resource allocation mechanism when this assumption does not hold and show that the resulting mechanism has a number of novel characteristics. For example, first best may be achievable even with nontrivial information asymmetry; when first best cannot be achieved, it is always optimal to overinvest relative to first best, and the most efficient agent may not earn rents, even when a less efficient agent does. This paper was accepted by Suraj Srinivasan, accounting. [ABSTRACT FROM AUTHOR]

Platform businesses are typically resource-intensive and must scale up their business quickly in the early stage to compete successfully against fast-emerging rivals. We study a critical question faced by such firms in the novel context of multicategory two-sided platforms: how to optimally make investment decisions across two sides, multiple categories, and different time periods to achieve fast and sustainable growth. We first develop a two-category two-period theoretical model and propose optimal resource allocation strategies that account for heterogeneous within-category direct and indirect network effects and cross-category interdependence. We find that the proposed strategy shares the spirit of the allocation rules for multiproduct nonplatform firms and single-product platform firms, yet it does not amount to a simple combination of the existing rules. Interestingly, the business model that platforms adopt crucially determines the optimal strategy. Platforms that charge by user should adopt a "reinforcing" rule for both within- and cross-category allocations by allocating more resources toward the stronger growth driver. Platforms that charge by transaction should also adopt the reinforcing rule for within-category allocation, but follow a "compensatory" rule for cross-category and intertemporal allocations by allocating more resources toward the weaker growth driver. We use data from the daily deals industry to empirically identify the network effects, propose alternative allocation strategies stemming from our theoretical findings, and use simulations to show the benefits of these strategies. For instance, we show that reallocating 10% of the average observed investment from Fitness to Beauty can increase profits by up to 15.5% for some cities. This paper was accepted by Matthew Shum, marketing. [ABSTRACT FROM AUTHOR]

We study online demand fulfillment in a class of networks with limited flexibility and arbitrary numbers of resources and request types. We show analytically that such a network is both necessary and sufficient to guarantee a performance gap independent of the market size compared with networks with full flexibility, extending the previous literature from the long chains to more general sparse networks. Inspired by the performance bound, we develop simple inventory allocation rules and guidelines for designing such network structures. Numerical experiments including one using some real data from Amazon China are conducted to confirm our findings as well as some of the flexibility principles conjectured in the literature. This paper was accepted by Chung Piaw Teo, optimization. [ABSTRACT FROM AUTHOR]

We consider a canonical quantity-based network revenue management problem where a firm accepts or rejects incoming customer requests irrevocably in order to maximize expected revenue given limited resources. Because of the curse of dimensionality, the exact solution to this problem by dynamic programming is intractable when the number of resources is large. We study a family of re-solving heuristics that periodically re-optimize an approximation to the original problem known as the deterministic linear program (DLP), where random customer arrivals are replaced by their expectations. We find that, in general, frequently re-solving the DLP produces the same order of revenue loss as one would get without re-solving, which scales as the square root of the time horizon length and resource capacities. By re-solving the DLP at a few selected points in time and applying thresholds to the customer acceptance probabilities, we design a new re-solving heuristic with revenue loss that is uniformly bounded by a constant that is independent of the time horizon and resource capacities. This paper was accepted by Kalyan Talluri, revenue management and market analytics. [ABSTRACT FROM AUTHOR]

We study the problem of sharing the revenues from broadcasting sport league events among participating teams. We provide direct, axiomatic, and game-theoretical foundations for two focal rules: the equal-split rule and concede-and-divide. The former allocates the revenues generated from broadcasting each game equally among the participating teams in the game. The latter concedes each team the revenues from its fan base and divides equally the residual. We also provide an application studying the case of sharing the revenue from broadcasting games in La Liga, the Spanish Football League. We show that hybrid schemes, combining our rules with lower bounds and performance measures, yield close outcomes to the current allocation being implemented by the Spanish National Professional Football League Association. This paper was accepted by Manel Baucells, decision analysis. [ABSTRACT FROM AUTHOR]

We consider a class of online resource-allocation problems in which there are n types of resources with limited initial inventory and n demand classes. The resources are flexible in that each type of resource can serve more than one demand class. In this paper, we focus on a special class of structures with limited flexibility, the long-chain design, which was proposed by Jordan and Graves [Jordan WC, Graves SC (1995) Principles on the benefits of manufacturing process flexibility. Management Sci. 41(4):577–594.] and has been an important concept in the design of sparse flexible processes. We study the long-chain design in an online stochastic environment in which the requests are drawn repeatedly and independently from a nonstationary probability distribution over the different demand classes. Also, the decision on how to address each request must be made immediately upon its arrival. We show the effectiveness of the long-chain design in mitigating supply–demand mismatch under a simple myopic online allocation policy. In particular, we provide an upper bound on the expected total number of lost sales that is irrespective of how large the market size is. This paper was accepted by Yinyu Ye, optimization. [ABSTRACT FROM AUTHOR]

We study a model in which a monopoly firm designs the quality profile of its inventory and then dynamically updates its pricing menu for a finite selling horizon to maximize revenue. In a counterfactual scenario, a social planner goes through the same process to maximize total welfare. We show that in both scenarios the problem of dynamically pricing heterogeneous-quality (vertically differentiated) inventories is equivalent to that of dynamically pricing homogeneous-quality inventories, in the sense that a solution to one implies a solution to the other. Moreover, we prove a strong scarcity result, which suggests that the sale of a product drives up the prices on all remaining products, whether of higher or lower quality. We then consider product line design under a production technology that utilizes costly and potentially limited resources. We show that with unlimited (but costly) resources, the revenue maximizer undersupplies quality to all products compared with the social planner. With limited resources, we show that the revenue maximizer exhibits elitism: he overallocates (underallocates) resources on the production of high-quality (low-quality) products. However, as the volume of expected consumer arrivals increases to infinity, both the revenue maximizer and the welfare maximizer allocate resources equally across products. [ABSTRACT FROM AUTHOR]

This paper considers a traditional problem of resource allocation: scheduling jobs on machines. One such recent application is cloud computing; jobs arrive in an online fashion with capacity requirements and need to be immediately scheduled on physical machines in data centers. It is often observed that the requested capacities are not fully utilized, hence offering an opportunity to employ an overcommitment policy, that is, selling resources beyond capacity. Setting the right overcommitment level can yield a significant cost reduction for the cloud provider while only inducing a very low risk of violating capacity constraints. We introduce and study a model that quantifies the value of overcommitment by modeling the problem as bin packing with chance constraints. We then propose an alternative formulation that transforms each chance constraint to a submodular function. We show that our model captures the risk pooling effect and can guide scheduling and overcommitment decisions. We also develop a family of online algorithms that are intuitive, easy to implement, and provide a constant factor guarantee from optimal. Finally, we calibrate our model using realistic workload data and test our approach in a practical setting. Our analysis and experiments illustrate the benefit of overcommitment in cloud services and suggest a cost reduction of 1.5% to 17%, depending on the provider's risk tolerance. The online appendices are available at https://doi.org/10.1287/mnsc.2018.3091. [ABSTRACT FROM AUTHOR]

This paper shows that operational flexibility interacting with informational uncertainty may lead to truthful information exchange in equilibrium even when the communication is nonbinding and unverifiable, i.e., "cheap talk." We consider a model consisting of a manufacturer releasing a new product with uncertain release date and demand, and a retailer who must determine the allocation of limited capacity between a preexisting third-party product and the manufacturer's new product that may or may not be released on time. The manufacturer has a private forecast about the likelihood of the product release and/or about the demand, which he shares (either truthfully or not) with the retailer. We show that under the "traditional" supply chain structure (one-time opportunity to order) no truthful equilibrium can emerge. However, if (1) the supply chain structure allows for postponement, i.e., the ability to delay orders at a certain cost by the retailer, and (2) the manufacturer has informational uncertainty about the retailer's capacity, then truthful information exchange may emerge in equilibrium, where the manufacturer transmits his true forecast and the retailer treats the transmission as truthful. The genesis of this effect is preference reversal, where the manufacturer is not sure which way to distort the forecast to best motivate the retailer to wait for the new product. Thus, we show that a truth-revealing mechanism can emerge from a relatively rich setup featuring two-sided information asymmetry interacting with postponement. This paper was accepted by Gad Allon, operations management. [ABSTRACT FROM AUTHOR]

Motivated by diverse application areas such as healthcare, call centers, and crowdsourcing, we consider the design and operation of service systems that process tasks with types that are ex ante unknown, and employ servers with different skill sets. Our benchmark model involves two types of tasks, Easy and Hard, and servers that are either Junior or Senior in their abilities. The service provider determines a resource allocation policy, i.e., how to assign tasks to servers over time, with the goal of maximizing the system's long-term throughput. Information about a task's type can only be obtained while serving it. In particular, the more time a Junior server spends on a task without service completion, the higher her belief that the task is Hard and thus needs to be rerouted to a Senior server. This interplay between service time and task-type uncertainty implies that the system's resource allocation policy and staffing levels implicitly determine how the provider prioritizes between learning and actually serving. We show that the performance loss due to the uncertainty in task types can be significant and, interestingly, that the system's stability region is largely dependent on the rate at which information about tasks' types is generated. Furthermore, we consider endogenizing the servers' capabilities: assuming that training is costly, we explore the problem of jointly optimizing over the training levels of the system's server pools, the staffing levels, and the resource allocation policy. We find that among optimal designs there always exists one with a "hierarchical" structure, where all tasks are initially routed to the least skilled servers and then progressively move to more skilled ones, if necessary. Comparative statics indicate that uncertainty in task types leads to significantly higher staffing cost and less specialized server pools. This paper was accepted by Serguei Netessine, operations management. [ABSTRACT FROM AUTHOR]

We study a new class of scheduling problems that capture common settings in service environments, in which one has to serve a collection of jobs that have a priori uncertain attributes (e.g., processing times and priorities) and the service provider has to decide how to dynamically allocate resources (e.g., people, equipment, and time) between testing (diagnosing) jobs to learn more about their respective uncertain attributes and processing jobs. The former could inform future decisions, but could delay the service time for other jobs, while the latter directly advances the processing of the jobs but requires making decisions under uncertainty. Through novel analysis we obtain surprising structural results of optimal policies that provide operational managerial insights, efficient optimal and near-optimal algorithms, and quantification of the value of testing. We believe that our approach will lead to further research to explore this important practical trade-off. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2973. This paper was accepted by Yinyu Ye, optimization. [ABSTRACT FROM AUTHOR]

In this paper, we study systems that allocate different types of scarce resources to heterogeneous allocatees based on predetermined priority rules—the U.S. deceased-donor kidney allocation system or the public housing program. We tackle the problem of estimating the wait time of an allocatee who possesses incomplete system information with regard, for example, to his relative priority, other allocatees' preferences, and resource availability. We model such systems as multiclass, multiserver queuing systems that are potentially unstable or in transient regime. We propose a novel robust optimization solution methodology that builds on the assignment problem. For first-come, first-served systems, our approach yields a mixed-integer programming formulation. For the important case where there is a hierarchy in the resource types, we strengthen our formulation through a drastic variable reduction and also propose a highly scalable heuristic, involving only the solution of a convex optimization problem (usually a second-order cone problem). We back the heuristic with an approximation guarantee that becomes tighter for larger problem sizes. We illustrate the generalizability of our approach by studying systems that operate under different priority rules, such as class priority. Numerical studies demonstrate that our approach outperforms simulation. We showcase how our methodology can be applied to assist patients in the U.S. deceased-donor kidney waitlist. We calibrate our model using historical data to estimate patients' wait times based on their kidney quality preferences, blood type, location, and rank in the waitlist. This paper was accepted by Yinyu Ye, optimization. [ABSTRACT FROM AUTHOR]

Motivated by the trend toward more collaboration in workflows, we study networks where some tasks require the simultaneous processing by multiple types of multitasking human or indivisible resources. The capacity of such networks is generally smaller than the bottleneck capacity. In Gurvich and Van Mieghem [Gurvich I, Van Mieghem JA (2015) Collaboration and multitasking in networks: Architectures, bottlenecks, and capacity. Manufacturing Service Oper. Management 17(1):16-33], we proved that both capacities are equal in networks with a hierarchical collaboration architecture, which define a collaboration level for each task depending on how many types of resources it requires. This paper studies how task prioritization impacts the achievable capacity of such hierarchical networks using a conceptual queueing framework that formalizes coordination and switching idleness. To maximize the capacity of a collaborative network, highest priority must be given to the tasks that require the most collaboration. Otherwise, a mismatch between priority levels and collaboration levels inevitably inflicts a capacity loss. We demonstrate this fundamental tension between flexibility in task prioritization (ability to adjust quality of service) and capacity (productivity) in a basic collaborative network and in parallel networks. To manage this trade-off, we present a hierarchical threshold priority policy that balances switching and coordination idleness. [ABSTRACT FROM AUTHOR]

Resource pooling strategies have been widely used in industry to match supply with demand. However, effective implementation of these strategies can be challenging. Firms need to integrate the heterogeneous service level requirements of different customers into the pooling model and allocate the resources (inventory or capacity) appropriately in the most effective manner. The traditional analysis of inventory pooling, for instance, considers the performance metric in a centralized system and does not address the associated issue of inventory allocation. Using Blackwell's Approachability Theorem, we derive a set of necessary and sufficient conditions to relate the fill rate requirement of each customer to the resources needed in the system. This provides a new approach to studying the value of resource pooling in a system with differentiated service requirements. Furthermore, we show that with "allocation flexibility," the amount of safety stock needed in a system with independent and identically distributed demands does not grow with the number of customers but instead diminishes to zero and eventually becomes negative as the number of customers grows sufficiently large. This surprising result holds for all demand distributions with bounded first and second moments. [ABSTRACT FROM AUTHOR]

Recent work suggests that an excessive focus on "managing the numbers"--delivering quarterly earnings at the expense of longer-term performance--makes it difficult for firms to make the investments necessary to build competitive advantage. "Short termism" has been blamed for everything from the decline of the U.S. automobile industry to the low penetration of techniques such as total quality management and continuous improvement. Yet a significant body of research suggests that firms that sacrifice long-term investment to manage earnings are often rewarded for doing so. This paper presents a model to help reconcile the tension between these apparently contradictory perspectives. We show that if the source of long-term advantage is modeled as a stock of capability that accumulates over time, the intensity of the firm's effort to manage short-term earnings at the expense of long-term investment can have very different consequences depending on whether the firm's capability is close to a critical "tipping threshold." When the firm operates above this threshold, aggressively managing earnings smooths revenue and cash flow with few long-term consequences. Below it, managing earnings can tip the firm into a vicious cycle of accelerating decline. Our results have important implications for understanding managerial incentives and the internal processes that create sustained advantage. [ABSTRACT FROM AUTHOR]

We study the effect of multilateral private information on the efficiency of markets where capacity-constrained upstream agents supply a resource to downstream entities facing uncertain end-demands. We analyze two models: a 'pooling system,' in which a single downstream principal pools a resource from multiple upstream agents; and a 'distribution system,' in which one upstream principal allocates a resource across multiple downstream agents. We show that the presence of multilateral private information does not hinder efficiency in the pooling system. In contrast, in the distribution system, the quantities allocated to downstream agents can exceed, as well as fall short of, their first-best levels. These results shed light on the recently improved performance of U.S. agricultural produce market, and the observed episodes of shortages/oversupplies in flu vaccine and other seasonal markets. This paper was accepted by Manel Baucells, decision analysis. [ABSTRACT FROM AUTHOR]

Uncertain outcomes are an inevitable feature of policy choices and their public support often depends on their perceived justice. We theoretically and experimentally explore just allocations when recipients are exposed to certainty and uncertainty. In the experiment, uninvolved participants unequivocally choose to allocate resources equally between recipients, when there is certainty. In stark contrast, with uncertainty just allocations are widely dispersed and recipients exposed to higher degrees of uncertainty are allocated less. The observed allocations can be well organized by four different theoretical views of justice, indicating that uninvolved participants differ fundamentally in their views on justice under uncertainty. Data, as supplemental material, are available at . This paper was accepted by Uri Gneezy, behavioral economics. [ABSTRACT FROM AUTHOR]

Firms require demand forecasts at different levels of aggregation to support a variety of resource allocation decisions. For example, a retailer needs store-level forecasts to manage inventory at the store, but also requires a regionally aggregated forecast for managing inventory at a distribution center. In generating an aggregate forecast, a firm can choose to make the forecast directly based on the aggregated data or indirectly by summing lower-level forecasts (i.e., bottom up). Our study investigates the relative performance of such hierarchical forecasting processes through a behavioral lens. We identify two judgment biases that affect the relative performance of direct and indirect forecasting approaches: a propensity for random judgment errors and a failure to benefit from the informational value that is embedded in the correlation structure between lower-level demands. Based on these biases, we characterize demand environments where one hierarchical process results in more accurate forecasts than the other. This paper was accepted by Martin Lariviere, operations management. [ABSTRACT FROM AUTHOR]

Resource assignment problems occur in a vast variety of applications, from scheduling problems over image recognition to communication networks. Often these problems can be modeled by a maximum weight matching problem in (bipartite) graphs or generalizations thereof, and efficient and practical algorithms are known for these problems. Although in some of the applications an assignment of the resources may be needed only once, in many of these applications, the assignment has to be computed more often for different scenarios. In that case it is often essential that the assignments can be computed very fast. Moreover, implementing different assignments in different scenarios may come with a certain cost for the reconfiguration of the system. In this paper, we consider the problem of determining optimal assignments sequentially over a given time horizon, where consecutive assignments are coupled by constraints that control the cost of reconfiguration. We develop fast approximation and online algorithms for this problem with provable approximation guarantees and competitive ratios. Moreover, we present an extensive computational study about the applicability of our model and our algorithms in the context of orthogonal frequency division multiple access (OFDMA) wireless networks, finding a significant performance improvement for the total bandwidth of the system using our algorithms. For this application (the downlink of an OFDMA wireless cell) , the run time of matching algorithms is extremely important, having an acceptable range of a few milliseconds only. For the considered realistic instances, our algorithms perform extremely well: the solution quality is, on average, within a factor of 0.8-0.9 of optimal off-line solutions, and the running times are at most 5 ms per phase even in the worst case. Thus, our algorithms are well suited to be applied in the context of OFDMA systems. Data, as supplemental material, are available at . This paper was accepted by Teck-Hua Ho, optimization. [ABSTRACT FROM AUTHOR]

Research and development (R&D) projects face significant organizational challenges, especially when the different units who run these projects compete among each other for resources. In such cases, information sharing among the different units is critical, but it cannot be taken for granted. Instead, individual units need to be incentivized to not only exert effort in evaluating their projects, but also to truthfully reveal their findings. The former requires an emphasis on individual performance, whereas the latter relies on the existence of a common goal across the organization. Motivated by this commonly observed tension, we address the following question: How should a firm balance individual and shared incentives, so that vital information is both acquired, and equally importantly, disseminated to the entire organization? Our model captures two key characteristics of R&D experimentation: information is imperfect and it is also costly. Our analysis yields several important implications for the design of such incentive schemes and the management of R&D portfolios. [ABSTRACT FROM AUTHOR]

We examine the role of target firms' accounting quality in the merger and acquisition process. We predict that target firm accounting quality will be positively associated with (1) the likelihood that the deal will be structured as a negotiation rather than as an auction, (2) the speed with which the deal reaches final resolution, and (3) the likelihood that the proposed deal is ultimately completed. Our empirical evidence is consistent with these predictions. These results complement and extend existing findings on target firm accounting quality and provide new evidence that financial accounting quality relates positively to the efficient allocation of the economy's capital resources. [ABSTRACT FROM AUTHOR]

When senior managers make the critical decision of whether to assign resources to a strategic initiative, they have less precise initiative-specific information than project managers who execute such initiatives. Senior management chooses between a decision process that dictates the resource level (top-down) and one that delegates the resource decision and gives up control in favor of more precise information (bottom-up). We investigate this choice and vary the amount of information asymmetry between stakeholders, the "penalty for failure" imposed upon project managers, and how challenging the initiative is for the firm. We find that no single decision process is the "best." Bottom-up processes are beneficial for more challenging initiatives. Increased organizational penalties may prompt the firm to choose a narrower scope and deter the approval of profitable initiatives. Such penalties, however, enable an effective decision process known as "strategic buckets" that holds the potential to achieve first-best resource allocation levels. [ABSTRACT FROM AUTHOR]

We address the mechanism design problem for a market with multiple buyers and sellers. Each buyer demands some bundle(s) of various commodities, and each seller supplies multiple units of one commodity. To design truthful double-auction mechanisms, we propose a novel 'padding' method that intentionally creates imbalances between the supply availability and demand requirement by introducing a phantom buyer with unlimited budget. To the best of our knowledge, this 'padding' method leads to a class of mechanisms that are the first strategy-proof, individually rational, budget-balanced, and asymptotically efficient mechanisms for the specified exchange environment. Furthermore, these mechanisms dominate known truthful bundle/single-unit mechanisms with higher efficiency, lower buying prices, and higher selling prices. Key words: strategy proofness; auction design; bidding with synergies; resource allocation. History: Received August 21, 2008; accepted February 25, 2009, by Ramayya Krishnan, information systems. Published online in Articles in Advance May 7, 2009., 1. Introduction Recent years have seen a growing number of transactions in Internet marketplaces, and this has generated great interest in online auctions in the academic research communities (Pinker et [...]

In this paper, we describe both applied and analytical work in collaboration with a large multistate gas utility. The project addressed a major operational resource allocation challenge that is typical to the industry. We study the resource allocation problem in which some of the tasks are scheduled and known in advance, and some are unpredictable and have to be addressed as they appear. The utility has maintenance crews that perform both standard jobs (each must be done before a specified deadline) as well as respond to emergency gas leaks (that occur randomly throughout the day and could disrupt the schedule and lead to significant overtime). The goal is to perform all the standard jobs by their respective deadlines, to address all emergency jobs in a timely manner, and to minimize maintenance crew overtime. We employ a novel decomposition approach that solves the problem in two phases. The first is a job scheduling phase, where standard jobs are scheduled over a time horizon. The second is a crew assignment phase, which solves a stochastic mixed integer program to assign jobs to maintenance crews under a stochastic number of future emergencies. For the first phase, we propose a heuristic based on the rounding of a linear programming relaxation formulation and prove an analytical worst-case performance guarantee. For the second phase, we propose an algorithm for assigning crews that is motivated by the structure of an optimal solution. We used our models and heuristics to develop a decision support tool that is being piloted in one of the utility’s sites. Using the utility’s data, we project that the tool will result in a 55% reduction in overtime hours. [ABSTRACT FROM AUTHOR]

Capital budgeting frequently involves multiple stages at which firms can continue or abandon ongoing projects. In this paper, we study a project requiring two stages of investment. Failure to fund Stage 1 of the investment precludes investment in Stage 2, whereas failure to fund Stage 2 results in early termination. In contrast to the existing literature, we assume that the firm can limit the manager's informational rents with the early termination of the project. In this setting, we find that the firm optimally commits to a capital allocation scheme whereby it forgoes positive net present value (NPV) projects at Stage 1 (capital rationing), whereas at Stage 2, depending on the manager's previous report, it sometimes implements projects with a negative continuation NPV but in other situations forgoes implementing projects with positive continuation NPVs. [ABSTRACT FROM AUTHOR]

We study an order promising problem in a multiclass, available-to-promise (ATP) assembly system in the presence of pseudo orders. A pseudo order refers to a tentative customer order whose attributes, such as the likelihood of an actual order, order quantity, and confirmation timing, can change dynamically over time. A unit demand from any class is assembled from one manufactured unit and one inventory unit, where the manufactured unit takes one unit of capacity and needs a single period to produce. An accepted order must be filled before a positive delivery lead time. The underlying order acceptance decisions involve trade-offs between committing resources (production capacity and component inventory) to low-reward firm orders and reserving resources for high-reward orders. We develop a Markov chain model that captures the key characteristics of pseudo orders, including demand lumpiness, nonstationarity, and volatility. We then formulate a stochastic dynamic program for the ATP assembly system that embeds the Markov chain model as a short-term forecast for pseudo orders. We show that the optimal order acceptance policy is characterized by class prioritization, resource-imbalance- based rationing, and capacity-inventory-demand matching. In particular, the rationing level for each class is determined by a critical value that depends on the resource imbalance level, defined as the net difference between the production capacity and component inventory levels. Extensive numerical tests underscore the importance of the key properties of the optimal policy and provide operational and managerial insights on the value of the short-term demand forecast and the robustness of the optimal policy. [ABSTRACT FROM AUTHOR]

Cadaveric liver transplantation is the only viable therapy for end-stage liver disease patients without a living donor. However, this type of transplantation is hindered in the United States by donor scarcity and rapid viability decay. Given these difficulties, the current U.S. liver allocation policy balances allocation likelihood and geographic proximity by allocating cadaveric livers hierarchically. We consider the problem of maximizing the efficiency of intraregional transplants through the redesign of liver allocation regions. We formulate the problem as a set partitioning problem that clusters organ procurement organizations into regions. We develop an estimate of viability-adjusted intraregional transplants to capture the trade-off between large and small regions. We utilize branch and price because the set partitioning formulation includes too many potential regions to handle explicitly. We formulate the pricing problem as a mixed-integer program and design a geographic decomposition heuristic to generate promising columns quickly. Because the optimal solution depends on the design of geographic decomposition, we develop an iterative procedure that integrates branch and price with local search to alleviate this dependency. Finally, we present computational studies that show the benefit of region redesign and the efficacy of our solution approach. Our carefully calibrated test instances can be solved within a reasonable amount of time, and the resulting region designs yield a noticeable improvement over the current configuration.