One of the impacts of the rising demand for community health services, primarily used by seniors, is that hospitals are often faced with the challenge of having patients finish the acute phase of their treatment and yet are unable to discharge them due to the lack of a bed in a more appropriate community care setting. The frequency of this challenge has led to the designation of “alternative level of care” (ALC) being ascribed to patients who remain in the hospitals due to insufficient capacity downstream. The thesis focuses on a model that seeks to address patient flow through the community care network (CCN) and finding capacity allocation policies for the different facilities that resolves the ALC challenge using scenario analysis. A queuing network model with general routings and nodes’ blocking has been developed and a heuristic approximation method has been employed for solving the model. Blocking probabilities and the number of blocked patients are derived as performance metrics of the CCN. We test the accuracy of the queuing model through a simulation model and the behaviours of the system in different scenarios are investigated in the simulation model and our policy insights and conclusions are provided.

One of the impacts of the rising demand for community health services, primarily used by seniors, is that hospitals are often faced with the challenge of having patients finish the acute phase of their treatment and yet are unable to discharge them due to the lack of a bed in a more appropriate community care setting. The frequency of this challenge has led to the designation of “alternative level of care” (ALC) being ascribed to patients who remain in the hospitals due to insufficient capacity downstream. The thesis focuses on a model that seeks to address patient flow through the community care network (CCN) and finding capacity allocation policies for the different facilities that resolves the ALC challenge using scenario analysis. A queuing network model with general routings and nodes’ blocking has been developed and a heuristic approximation method has been employed for solving the model. Blocking probabilities and the number of blocked patients are derived as performance metrics of the CCN. We test the accuracy of the queuing model through a simulation model and the behaviours of the system in different scenarios are investigated in the simulation model and our policy insights and conclusions are provided.

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Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.