Manuel A. Nunez

OR Practice---Efficient Short-Term Allocation and Reallocation of Patients to Floors of a Hospital During Demand Surges

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.

Managing Data Quality Risk in Accounting Information Systems

The quality of data contained in accounting information systems has a significant impact on both internal business decision making and external regulatory compliance. Although a considerable body of literature exists on the issue of data quality, there has been little research done at the task level of a business process to develop effective control strategies to mitigate data quality risks. In this paper, we present a methodology for managing the risks associated with the quality of data in accounting information systems. This methodology first models the error evolution process in transactional data flow as a dynamical process; it then finds optimal control policies at the task level to mitigate the data quality-related risks using a Markov decision process model with risk constraints. The proposed Markov decision methodology facilitates the modeling of multiple dimensions of error dependence, captures the correlated impact among control procedures, and identifies an optimal control policy. A revenue realization process of an international production company is used to illustrate this methodology.

Strategic alignment and value maximization for IT project portfolios

Managing project portfolios has been a challenge to many IT organizations due to the size and complexity of their initiatives that are often cross-functional, fast changing, and transformational in nature. A governance process on project solicitation, evaluation, and monitoring is thus essential to ensure the resulting portfolio creates tangible values, balances across priorities, and supports business objectives. An optimization model to streamline the decision processes for IT portfolios and programs is proposed. We consider project characteristics such as the extent of strategic alignment, expected benefit, development cost, and cross-project synergy to maximize the portfolio value. We also consider team proficiency and resource availability to determine a project portfolio that could be implemented within the overall development time. The multi-objective model identifies the optimal mix among project types and the solution procedure efficiently produces recommendations that are superior to those found with current empirical techniques. We also describe an evolutionary algorithm to find approximate solutions to the optimization model. Possible extensions on how the optimization procedure can go beyond projects to also streamline decisions such as the renewal or replacement of in-flight applications is discussed.

A characterization of ill-posed data instances for convex programming

Abstract.Given a data instance of a convex program, we provide a collection of conic linear systems such that the data instance is ill-posed if and only if at least one of those systems is satisfied. This collection of conic linear systems is derived from a characterization of the boundary of the set of primal and dual feasible data instances associated with the given convex program.

Condition-Measure Bounds on the Behavior of the Central Trajectory of a Semidefinite Program

We present bounds on various quantities of interest regarding the central trajectory of a semidefinite program, where the bounds are functions of Renegars condition number

Activity structures in a project-based environment: a coordination theory perspective

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On the Prevention of Fraud and Privacy Exposure in Process Information Flow

and other naturally occurring quantities such as the dimensions n and m. The condition number

Business Process Integration of Multiple Customer Order Review Systems

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A decision methodology for managing operational efficiency and information disclosure risk in healthcare processes

is defined in terms of the data instance d=(A,b,C) for a semidefinite program; it is the inverse of a relative measure of the distance of the data instance to the set of ill-posed data instances, that is, data instances for which arbitrary perturbations would make the corresponding semidefinite program either feasible or infeasible. We provide upper and lower bounds on the solutions along the central trajectory, and upper bounds on changes in solutions and objective function values along the central trajectory when the data instance is perturbed and/or when the path parameter defining the central trajectory is changed. Based on these bounds, we prove that the solutions along the central trajectory grow at most linearly and at a rate proportional to the inverse of the distance to ill-posedness, and grow at least linearly and at a rate proportional to the inverse of

Stochastic Protection of Confidential Information in Databases: A Hybrid of Data Perturbation and Query Restriction

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