Reengineering health care materials management

Health care executives across the country, faced with intense competition, are being forced to consider drastic cost cutting measures as a matter of survival. The entire health care industry is under siege from boards of directors, management and others who encourage health care systems to take actions ranging from strategic acquisitions and mergers to simple "downsizing" or "rightsizing," to improve their perceived competitive positions in terms of costs, revenues and market share. In some cases, management is poorly prepared to work within this new competitive paradigm and turns to consultants who promise that following their methodologies can result in competitive advantage. One favored methodology is reengineering. Frequently, cost cutting attention is focused on the materials management budget because it is relatively large and is viewed as being comprised mostly of controllable expenses. Also, materials management is seldom considered a core competency for the health care system and the organization performing these activities does not occupy a strongly defensible position. This paper focuses on the application of a reengineering methodology to healthcare materials management.     

Optimum detection of multiple vapor materials with frequency-agile lidar

Differential absorption lidar (DIAL) is a well-established technology for estimating the concentration and its path integral CL of vapor materials using two closely spaced wavelengths. The recent development of frequency-agile lasers (FAL's) with as many as 60 wavelengths that can be rapidly scanned motivates the need for detection and estimation algorithms that are optimal for lidar employing these new sources. I derive detection and multimaterial CL estimation algorithms for FAL applications using the likelihood ratio test methodology of multivariate statistical inference theory. Three model sets of assumptions are considered with regard to the spectral properties of the backscatter from either topographic or aerosol targets. The calculations are illustrated through both simulated and actual lidar data.     

St. Francis Hospice: Medicare and health care reform

The St. Francis Hospice Program is symbolic of more than 100 years of Franciscan dedication to the people of Hawaii. Since Mother Marianne's arrival in November of 1883, the Sisters of the Third Franciscan Order Syracuse, New York have responded to the calling; "the charity of Christ impels us." It is through this calling that care and comfort for the terminally ill is a part of the mission of St. Francis Healthcare System. The magnificent spirit through which Hospice services have been made possible, is a reflection of God's great generosity to us throughout the years.     

A review of stochastic algorithms with continuous value function approximation and some new approximate policy iteration algorithms for multidimensional continuous applications

We review the literature on approximate dynamic programming, with the goal of better understanding the theory behind practical algorithms for solving dynamic programs with continuous and vector-valued states and actions and complex information processes. We build on the literature that has addressed the well-known problem of multidimensional (and possibly continuous) states, and the extensive literature on model-free dynamic programming, which also assumes that the expectation in Bellman’s equation cannot be computed. However, we point out complications that arise when the actions/controls are vector-valued and possibly continuous. We then describe some recent research by the authors on approximate policy iteration algorithms that offer convergence guarantees (with technical assumptions) for both parametric and nonparametric architectures for the value function.     

C-PSA: Constrained Pareto simulated annealing for constrained multi-objective optimization

In recent years, evolutionary algorithms (EAs) have been extensively developed and utilized to solve multi-objective optimization problems. However, some previous studies have shown that for certain problems, an approach which allows for non-greedy or uphill moves (unlike EAs), can be more beneficial. One such approach is simulated annealing (SA). SA is a proven heuristic for solving numerical optimization problems. But owing to its point-to-point nature of search, limited efforts has been made to explore its potential for solving multi-objective problems. The focus of the presented work is to develop a simulated annealing algorithm for constrained multi-objective problems. The performance of the proposed algorithm is reported on a number of difficult constrained benchmark problems. A comparison with other established multi-objective optimization algorithms, such as infeasibility driven evolutionary algorithm (IDEA), Non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective Scatter search II (MOSS-II) has been included to highlight the benefits of the proposed approach.     

Free-living inferential modeling of blood glucose level using only noninvasive inputs

The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the subject’s personal glucose meter AAE of 15.3 mg/dL for replicated measurements.     

Minimizing total tardiness in a stochastic single machine scheduling problem using approximate dynamic programming

This paper addresses the non-preemptive single machine scheduling problem to minimize total tardiness. We are interested in the online version of this problem, where orders arrive at the system at random times. Jobs have to be scheduled without knowledge of what jobs will come afterwards. The processing times and the due dates become known when the order is placed. The order release date occurs only at the beginning of periodic intervals. A customized approximate dynamic programming method is introduced for this problem. The authors also present numerical experiments that assess the reliability of the new approach and show that it performs better than a myopic policy.     

Two hybrid differential evolution algorithms for engineering design optimization

This paper presents two hybrid differential evolution algorithms for optimizing engineering design problems. One hybrid algorithm enhances a basic differential evolution algorithm with a local search operator, i.e., random walk with direction exploitation, to strengthen the exploitation ability, while the other adding a second metaheuristic, i.e., harmony search, to cooperate with the differential evolution algorithm so as to produce the desirable synergetic effect. For comparison, the differential evolution algorithm that the two hybrids are based on is also implemented. All algorithms incorporate a generalized method to handle discrete variables and Deb's parameterless penalty method for handling constraints. Fourteen engineering design problems selected from different engineering fields are used for testing. The test results show that: (i) both hybrid algorithms overall outperform the differential evolution algorithms; (ii) among the two hybrid algorithms, the cooperative hybrid overall outperforms the other hybrid with local search; and (iii) the performance of proposed hybrid algorithms can be further improved with some effort of tuning the relevant parameters.