A tree search approach for the solution of set problems using alternative relaxations

A number of alternative relaxations for the family of set problems (FSP) in general and set covering problems (SCP) in particular are introduced and discussed. These are (i) Network flow relaxation, (ii) Assignment relaxation, (iii) Shortest route relaxation, (iv) Minimum spanning tree relaxation. A unified tree search method is developed which makes use of these relaxations. Computational experience of processing a collection of test problems is reported

Set covering and set partitioning: a collection of test problems

It is now well established that set covering and set partitioning models play a central role in many scheduling applications. There are many algorithms which solve these problems. In order to test and validate such implementations we have collected a range of test problems taken from different contexts. A brief description of these models, their applications and summary model data, are supplied in this paper

A simulation study of the winter bed crisis

The winter bed crisis is a cyclical phenomenon which appears in British hospitals every year, two or three weeks after Christmas. The crisis is usually attributed to factors such as the bad weather, influenza, older people, geriatricians, lack of cash or nurse shortages. However, a possible alternative explanation could be that beds within the hospital are blocked because of lack of social services for discharge of hospital patients during the Christmas period. Adopting this explanation of why the bed crisis occurs, the problem was considered as a queuing system and discrete event simulation was employed to evaluate the model numerically. The model shows that stopping discharges of rehabilitating patients for 21 days accompanied by a cessation of planned patients for 14 days precipitate a bed crisis when the planned admissions recommence. The extensive 'what-if' capabilities of such models could be proved to be crucial to the designing and implementation of possible solutions to the problem

A data warehouse environment for storing and analyzing simulation output data

Discrete event simulation modelling has been extensively used in modelling complex systems. Although it offers great conceptual-modelling flexibility, it is both computationally expensive and data intensive. There are several examples of simulation models that generate millions of observations to achieve satisfactory point and confidence interval estimations for the model variables. In these cases, it is exceptionally cumbersome to conduct the required output and sensitivity analysis in a spreadsheet or statistical package. In this paper, we highlight the advantages of employing data warehousing techniques for storing and analyzing simulation output data. The proposed data warehouse environment is capable of providing the means for automating the necessary algorithms and procedures for estimating different parameters of the simulation. These include initial transient in steady-state simulations and point and confidence interval estimations. Previously developed models for evaluating patient flow through hospital epartments are used to demonstrate the problem and the proposed solutions

An OLAP-enabled software environment for modelling patient flow

On-Line Analytical Processing (OLAP) tools use multidimensional views to provide quick access to information. They have become the de facto standard in the business world for analytical databases. In health care, care givers and managers could benefit from being able to perform interactive data exploration, ad-hoc analysis and possibly discover hidden trends and patterns in health data. However, health data have unique characteristics that distinguish them from common business examples, an aspect that makes the direct adaptation of the already established business oriented solutions difficult. In this paper we report the development of an OLAP system for analyzing hospital discharge data and for modeling hospital length of stay

Extending the gaia methodology for the design and development of agent-based software systems

Over the past decade, agent-based computing has emerged as a new and popular paradigm for design, implementation and analysis of distributed information systems. In this paper, the participant researchers in Health Care Computing Group at University of Westminster concentrate on the agent-oriented methodology for the analysis and design of agentbased systems and identify how methodology can support both the levels of "agent structure" and of "agent society" in the agent-oriented software design and development process. The research reported here takes one leading agent-oriented methodology-Gaia, and then extended it by the creation of innovative design tools which aimed at better supporting application to real-world domains. In discussion section, agent-oriented methodology and AUML approaches are compared and evaluated in great detail; the strengths and weaknesses of the current agent-oriented methodology are explored and discussed; the importance of effectively using methodology to improve agents and their productivity potential also is emphasized. Finally, we draw conclusions from the work presented and the experience gained in this research and look into the future possible improvements on agent-oriented software engineering in the agent technology research field

Analysis of stopping criteria for the EM algorithm in the context of patient grouping according to length of stay

The expectation maximisation (EM) algorithm is an iterative maximum likelihood procedure often used for estimating the parameters of a mixture model. Theoretically, increases in the likelihood function are guaranteed as the algorithm iteratively improves upon previously derived parameter estimates. The algorithm is considered to converge when all parameter estimates become stable and no further improvements can be made to the likelihood value. However, to reduce computational time, it is often common practice for the algorithm to be stopped before complete convergence using heuristic approaches. In this paper, we consider various stopping criteria and evaluate their effect on fitting Gaussian mixture models (GMMs) to patient length of stay (LOS) data. Although the GMM can be successfully fitted to positively skewed data such as LOS, the fitting procedure often requires many iterations of the EM algorithm. To our knowledge, no previous study has evaluated the effect of different stopping criteria on fitting GMMs to skewed distributions. Hence, the aim of this paper is to evaluate the effect of various stopping criteria in order to select and justify their use within a patient spell classification methodology. Results illustrate that criteria based on the difference in the likelihood value and on the GMM parameters may not always be a good indicator for stopping the algorithm. In fact we show that the values of the difference in the variance parameters should be used instead, as these parameters are the last to stabilise. In addition, we also specify threshold values for the other stopping criteria

Agent-based models for community care systems analysis and design

In recent years, the providers of public and private sector health care services have been faced with some radical changes in the society they serve, and more importantly, development in the way that traditional health care is delivered to Information Technology (I.T) based communities. It is widely believed by health care professionals that the better health care results really come from the improved healthcare systems and more effective health care services' management. This paper focuses on using an agnet-based software engineering approach and design models to the development of an appropriate agent-based healthcare software system is described in which software researchers collaborate with environment builders to enhance the levels of cooperation and support provided within an integrated agent-based community healthcare system

A breast cancer diagnosis system: a combined approach using rough sets and probabilistic neural networks

In this paper, we present a medical decision support system based on a hybrid approach utilising rough sets and a probabilistic neural network. We utilised the ability of rough sets to perform dimensionality reduction to eliminate redundant attributes from a biomedical dataset. We then utilised a probabilistic neural network to perform supervised classification. Our results indicate that rough sets was able to reduce the number of attributes in the dataset by 67% without sacrificing classification accuracy. Our classification accuracy results yielded results on the order of 93%