Core-selecting combinatorial auctions have been introduced as an alternative to the Vickrey–Clarke–Groves (VCG) mechanism because VCG can result in payments that are not in the core with respect to bids, leading to unfair payments, unacceptably low revenues, and unstable outcomes. This raises an auction selection problem for an auctioneer deciding whether to employ a core-selecting auction or VCG mechanism in practice. The downside of a core-selecting auction is that it is not incentive compatible, as bidders have an incentive to reduce (shade) their bids below their true values. It has been argued that such bid shading in core-selecting auctions may lead to lower efficiency, lower revenue, and outcomes that are, on average, farther from the core with respect to true values, than the VCG mechanism. Using a much-studied auction environment, we address the auction selection problem faced by an auctioneer and obtain Bayes–Nash equilibrium bidding strategies when bidders are loss averse. We also bound the equilibrium strategies when bidders are risk-averse. This analysis demonstrates that when bidders are risk-averse or loss-averse, core-selecting auctions outperform the VCG mechanism in terms of revenue and stability, while yielding efficient allocations with high probability.

Healthcare organizations face a challenging operational environment characterized by uncertain demand, the need to deliver highly complex and specialized services, and increasing pressure to provide better quality care to more patients at lower total cost. Healthcare organizations invest substantial financial resources into the human, technological, and structural assets needed to provide a wide range of healthcare services. Determining how much capacity is made available, how that capacity is allocated to patients, and how various specialized units in the organization coordinate their activities are important drivers of performance. In particular, healthcare organizations continuously evaluate the flow of patients through the organization as different healthcare services are provided. If patients do not flow smoothly through the healthcare delivery process, either due to inadequate capacity or the inefficient use of capacity, then patient satisfaction and quality of care can suffer. This chapter provides an overview of the concept of patient flow as one measure of the quality and effectiveness of healthcare delivery, examines some of the most significant challenges to improving patient flow, provides an overview of prior operations research related to patient flow, and discusses current factors that are driving future research opportunities.

With the growing volume of collected and stored data from customer interactions that have recently shifted towards online channels, an important challenge faced by today's businesses is appropriately dealing with data quality problems. A key step in the data cleaning process is the matching and merging of customer records to assess the identity of individuals. The practical importance of this research is exemplified by a large client firm that deals with private label credit cards. They needed to know whether there existed histories of new customers within the company, in order to decide on the appropriate parameters of possible card offerings. The company incurs substantial costs if they incorrectly ''match'' an incoming application with an existing customer (Type I error), and also if they falsely assume that there is no match (Type II error). While there is a good deal of generic identity matching software available, that will provide a ''strength'' score for each potential match, the question of how to use the scores for new applications is of great interest and is addressed in this work. The academic significance lies in the analysis of the score thresholds that are typically used in decision making. That is, upper and lower thresholds are set, where matches are accepted above the former, rejected below the latter, and more information is gathered between the two. We show, for the first time, that the optimal thresholds can be considered to be parameters of a matching distribution, and a number of estimators of these parameters are developed and analyzed. Then extensive computations show the effects of various factors on the convergence rates of the estimates.

We consider the problem of balancing two competing objectives in the pursuit of efficient management of operating rooms in a hospital: providing surgeons with predictable, reliable access to the operating room and maintaining high utilization of capacity. The common solution to the first problem (in practice) is to grant exclusive "block time," in which a portion of the week in an operating room is designated to a particular surgeon, barring other surgeons from using this room/time. As a major improvement over this existing approach, we model the possibility of "shared" block time, which need only satisfy capacity constraints in expectation. We reduce the computational difficulty of the resulting NP-hard block-scheduling problem by implementing a column-generation approach and demonstrate the efficacy of this technique using simulation, calibrated to a real hospital's historical data and objectives. Our simulations illustrate substantial benefits to hospitals under a variety of circumstances and demonstrate the advantages of our new approach relative to a benchmark method taken from the recent literature.

Digital microfluidic biochips (DMFBs) are rectangular arrays of electrodes, or cells, that enable precise manipulation of nanoliter-sized droplets of biological fluids and chemical reagents. Because of the safety-critical nature of their applications, biochips must be tested frequently, both off-line (e.g., postmanufacturing) and concurrent with assay execution. Under both scenarios, testing is accomplished by routing one or more test droplets across the chip and recording their arrival at the destination. In this paper, we formalize the DMFB-testing problem under the common objective of completion time minimization, including previously ignored constraints of droplet noninterference. Our contributions include a proof that the general version of the problem is NP-hard, tight lower bounds for both off-line and concurrent testing, optimal and approximation algorithms for off-line testing of commonly used rectangular shaped biochips, as well as a concurrent testing heuristic producing solutions within 23%-34% of the lower bound in experiments conducted on data sets simulating varying percentages of biochip cells occupied by concurrently running assays.

Although significant technical advances have been made in the commercial deployment of grid computing, the pricing and allocation of distributed computing resources remains understudied. We develop a customized clock auction that is able to allocate grid resources and discover separate prices for the different computing resources under the condition that buyers do not know with certainty how much of these resources they will need. The proposed clock auction facilitates the discovery of unit prices for the resources in each time period in a finite-horizon market. Our mechanism exploits the lopsided nature of the grid market where a small number of large-scale jobs are expected to be completed by a large number of heterogeneous, distributed machines. The traditional stopping rule used for clock auctions is not effective in our setting, and therefore we design several adaptations that can be implemented in real time, geared toward ending the auction process quickly while producing a close-to-efficient allocation. Our extensive computations show that our clock-and-offer auction outperforms the traditional clock auction in terms of computational tractability, social welfare, and expected bidder's utility. For large problems of practical interest, we develop a transportation-based heuristic for the NP-complete bid feasibility problem and demonstrate theoretically and computationally that it quickly produces high-quality solutions to the overall problem.

A cardiac diagnostic testing center (CDTC) makes real-time scheduling decisions that impact the use of its resources and the availability of telemetry-equipped beds within a hospital. Both inpatients and outpatients are frequent users of CDTC resources, and physicians prescribe one of several single-phase or multiple-phase test protocols. This complex online decision-making environment is modeled as a finite-horizon, discrete-time Markov decision process (MDP), but the growth of the state space motivates the introduction of a fast heuristic for real-time decision support. We therefore introduce a dynamic network scheduling tool which is both more flexible and more robust, making it applicable to the various configurations that may be found in practically any CDTC. We evaluate this new method computationally using simulation, comparing it to both an MDP model for small instances, and to the existing operational practice at our partner hospital for more realistic sized problems.

Online retailers are increasingly using information technologies to provide value-added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms, both of which serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is how recommender systems affect sales. We take into consideration the interaction among recommendations, sales, and price. We then develop a robust empirical model that incorporates the indirect effect of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive effect on sales. Moreover, this effect is moderated by the recency effect, where more recently released recommended items positively affect the cross-selling efforts of sellers. We also show that recommender systems help to reinforce the long-tail phenomenon of electronic commerce, and obscure recommendations positively affect cross-selling. We also found a positive effect of recommendations on prices. These results suggest that recommendations not only improve sales but they also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher effect on sales than does consumer feedback. Our empirical results show that providing value-added services, such as digital word of mouth and recommendations, allows retailers to charge higher prices while at the same time increasing demand by providing more information regarding the quality and match of products.

The accelerating growth of the Internet, along with the current stress on privacy that limits the nature of data that organizations can collect and use, has rendered it increasingly difficult to control data quality. To that end many organizations use identity matching software to help determine whether an incoming record pertains to the same subject as that of an existing record in their system. Commonly used software essentially yields a ‘match strength score’ for each potential match. It is virtually always the case that at most one resident record will have very high strength, and the decision problem for the user is whether or not to consider the two to be a ‘true match’. That decision is based on a threshold score, above which the answer will be ‘match’. Clearly the optimal threshold is a context dependent parameter, which is a function of the costs of erroneous matching or non-matching, as well as of the underlying probabilities that true matches are associated with given score values. In many practical settings, these probabilities are unknown and difficult to calculate, leading to a need to estimate the threshold effectively via sampling. However it is extremely costly to acquire the sampling information needed for estimation, and also not clear what properties the estimators will possess. Here, a number of possible estimators are developed and compared. A comparative analysis of estimators reveals that a maximum likelihood based estimator displays the best large sample properties. Also the option of iteratively purchasing additional information, to aid in the matching decision, is investigated. This analysis provides us the opportunity to develop important characteristics of the market for such information provision.

Many hospitals face the problem of insufficient capacity to meet demand for inpatient beds, especially during demand surges. This results in quality degradation of patient care due to large delays from admission time to the hospital until arrival at a floor. In addition, there is loss of revenue because of the inability to provide service to potential patients. A solution to the problem is to proactively transfer patients between floors in anticipation of a demand surge. Optimal reallocation poses an extraordinarily complex problem that can be modeled as a finite-horizon Markov decision process. Based on the optimization model, a decision-support system has been developed and implemented at Windham Hospital in Willimantic, Connecticut. Projections from an initial trial period indicate very significant financial gains of about 1% of their total revenue, with no negative impact on any standard quality of care or staffing effectiveness indicators. In addition, the hospital showed a marked improvement in quality of care because of a resulting decrease of almost 50% in the average time that an admitted patient has to wait from admission until being transferred to a floor.

The massive amount of sensitive survey data about individuals that agencies collect and share through the Internet is causing a great deal of privacy concerns. These concerns may discourage individuals from revealing their sensitive information. Existing data collection techniques have serious downsides in terms of both efficiency and the levels of protection they offer against various realizations of threats. Moreover, they do not provide any flexibility to the users to be able to specify acceptable levels of privacy protection before deciding whether to participate in the surveys. In this paper, we propose a two-pronged privacy protection model corresponding to these two privacy concerns: these are a new efficient anonymity preserving data collection technique and a method to incorporate heterogeneous privacy constraints. Together, they help preserve the privacy of respondents both during and after data collection.

This chapter outlines some of the major themes of information systems security in statistical databases (SDB). Information security in databases, including confidentiality in SDBs and privacy-preserving data mining, is discussed broadly while the chapter primary focuses on the protection of SDBs against the very specific threat of statistical inference. Several protection mechanisms that researchers have developed to protect against this threat are introduced including data restriction, perturbation, and data-hiding techniques. One particular data-hiding model, Confidentiality via Camouflage (CVC), is introduced and demonstrated in detail. CVC provides a functional and robust technique to protect online and dynamic SDBs from the inference threat. Additionally, the chapter demonstrates how CVC can be linked to all economic model for an intermediated electronic market for private information.

The current generation of shopbots reduce consumer search costs associated with determining the best purchase price and place to buy a product predetermined by the shopper. In order to provide better service to shoppers, the service horizon of these shopbots can be extended in several dimensions. In this paper, we suggest that shopbots can integrate retail promotions and incorporate recommender systems in order to provide greater values to their users. Although the majority of online retailers already provide recommender systems, we show that profit maximizing retailers may not always provide transparent recommendations and argue that shopbots are in the better position to offer such recommendations. We develop integer programming models for shopbots to integrate sales promotions and product recommendations. We validate our model by using product recommendation data from two popular online retailers, Amazon.com and Buy.com, to show that our model provides recommendations that offer better value to the price sensitive shopbot customers.

Confidentiality via Camoufiage (CVC) provides a practical method for giving unlimited, correct, numerical responses to ad-hoc queries to an on-line database, while not compromising confidential numerical data. CVC-POL guarantees confidentiality by hiding protected data points in a polytope and generating query answers over the polytope. In this paper we introduce CVC-STAR, which protects confidential data using a set defined by a union of line segments resembling a star. Solutions for determining CVC-STAR interval answers are given for several common query types. Computational experience shows that the answers for CVC-STAR are superior to those of CVC-POL while providing more robust confidentiality protection.

The ability to collect and disseminate individually identifiable microdata is becoming increasingly important in a number of arenas. This is especially true in health care and national security, where this data is considered vital for a number of public health and safety initiatives. In some cases legislation has been used to establish some standards for limiting the collection of and access to such data. However, all such legislative efforts contain many provisions that allow for access to individually identifiable microdata without the consent of the data subject. Furthermore, although legislation is useful in that penalties are levied for violating the law, these penalties occur after an individual’s privacy has been compromised. Such deterrent measures can only serve as disincentives and offer no true protection. This paper considers security issues involved in releasing microdata, including individual identifiers. The threats to the confidentiality of the data subjects come from the users possessing statistical information that relates the revealed microdata to suppressed confidential information. The general strategy is to recode the initial data, in which some subjects are “safe” and some are at risk, into a data set in which no subjects are at risk. We develop a technique that enables the release of individually identifiable microdata in a manner that maximizes the utility of the released data while providing preventive protection of confidential data. Extensive computational results show that the proposed method is practical and viable and that useful data can be released even when the level of risk in the data is high.

Data perturbation and query restriction are two methods developed to protect confidential data in statistical databases. In the former, the data is systematically changed to yield answers to queries that are statistically similar to those that would have resulted from the original data. The latter provides exact answers to queries as long as the risk of exact disclosure of confidential data does not become too great. We present a new methodology to combine these techniques so that the advantages of both are captured. The model is appropriate and computationally viable for large databases whether the queries are linear or nonlinear. The query restriction phase consists of finding an optimal subset of queries to answer exactly without compromising the database. This is an NP-hard problem with a matroid intersection structure that lends itself to an efficient greedy heuristic. Then, given the queries that are answered exactly, we implement a data Perturbation phase that provides stochastic protection and consistency. We present computational results on a large database with both linear and nonlinear queries. The results indicate that many queries can be answered exactly and the proposed perturbation approach provides more accurate answers than the standard perturbation method.

Online retailers are increasingly using information technologies to provide value added services to customers. Prominent examples of these services are online recommender systems and consumer feedback mechanisms that serve to reduce consumer search costs and uncertainty associated with the purchase of unfamiliar products. The central question we address is the business value of online recommender systems to online retailers. We develop a robust empirical method that incorporates indirect impact of recommendations on sales through retailer pricing, potential simultaneity between sales and recommendations, and a comprehensive measure of the strength of recommendations. Applying the model to a panel data set collected from two online retailers, we found that the strength of recommendations has a positive impact on sales. We also found empirical evidence for the reinforcing effect of sales on recommendations and for the positive impact of recommendations on prices. These results suggest that recommendations not only improve sales but also provide added flexibility to retailers to adjust their prices. A comparative analysis reveals that recommendations have a higher impact on sales than consumer feedback. Our study demonstrates the value provided by information technology to an online retailer and provides guidelines for integrating recommender systems into their overall marketing strategy.

Online retailers are increasingly utilizing recommender systems to offer product recommendations to consumers. Such recommendations are typically based on previous purchases made by a network of customers with related purchase patterns. Although there has been extensive research devoted to enhancing the quality of recommendations, little research has been done in integrating recommendations with economic factors that drive the purchase behavior. Moreover, much of the work to date has utilized data collected from user satisfaction surveys or simulated experiments to assess the impact of recommender systems. This dissertation adds to the growing literature in recommender systems by providing models to integrate other economic factors along with recommendations and by empirically investigating the performance of online recommendations. The first essay of this dissertation presents integer programming based models to integrate recommendations with economic factors related to consumer purchase behavior. The underlying contention is that even if recommendations are accurate and useful, customers may not be interested in purchasing if such recommendations are not properly aligned with their economic interests. Integer programming models developed in this essay deal with integrating various economic incentives such as online retail promotions and price discounts with recommendations. These models are complementary to current recommender system algorithms and can be implemented by online retailers, or, by other independent intermediaries such as shopbots. The empirical analysis for these models suggests that our alternative set of recommendations offer significantly higher economic benefits to customers. The second essay empirically analyzes the relationship between sales and recommendations. The analysis is based on a panel data of books collected from publicly available information from online retailers. A weighted measure for recommendations is developed based on the number and the impact of recommenders. Subsequently, pooled OLS and panel data based models are used to analyze the effect of recommendations on sales. Further, sales, price and recommendations are jointly determined using a simultaneous equations model to address the potential endogeneity arising from simultaneity amongst these variables. We estimate the marginal change in sales due to recommendations, and contrast it with the impacts of other types of customer feedback.

Thek-Centrum Shortest Path Problem (kCSP[s, t]) is to minimize the sum of thek longest arcs in any (simple)s−t path containing at leastk arcs, wherek is a positive integer.kCSP is introduced and is shown to be NP-Hard, although it is polynomially solvable ifk is constrained to be no greater than the number of arcs in ans−t path with fewest arcs. Some properties of the problem are studied and a new weakly dual problem is also introduced.