This report presents a series of models that can be used to find weekly schedules for therapists who provide ongoing treatment to patients scattered around a geographical region. In all cases, the patients’ appointment times and visit days are known prior to the beginning of the planning horizon. Variations in the model include single vs. multiple home bases, homogeneous vs. heterogeneous therapists, lunch break requirements, and a nonlinear cost structure for mileage reimbursement and overtime. The single home base and homogeneous therapist cases proved to be easy to solve and so were not investigated. This left two cases of interest: the first includes only lunch breaks while the second adds overtime and mileage reimbursement. In all, 40 randomly generated data sets were solved that consisted of either 15 or 20 therapists and between roughly 300 and 540 visits over five days. For each instance, we were able to obtain the minimum cost of providing home healthcare services for both models using CPLEX 12.2. The results showed that CPU time increases more rapidly than total cost as the total number of visits grows. In general, data sets with therapists who have different starting and ending locations are more difficult to solve than those whose therapists have the same home base. / text

abstract: Driven by stringent power and thermal constraints, heterogeneous multi-core processors, such as the ARM big-LITTLE architecture, are becoming increasingly popular. In this thesis, the use of low-power heterogeneous multi-cores as Microservers using web search as a motivational application is addressed. In particular, I propose a new family of scheduling policies for heterogeneous microservers that assign incoming search queries to available cores so as to optimize for performance metrics such as mean response time and service level agreements, while guaranteeing thermally-safe operation. Thorough experimental evaluations on a big-LITTLE platform demonstrate, on an heterogeneous eight-core Samsung Exynos 5422 MpSoC, with four big and little cores each, that naive performance oriented scheduling policies quickly result in thermal instability, while the proposed policies not only reduce peak temperature but also achieve 4.8x reduction in processing time and 5.6x increase in energy efficiency compared to baseline scheduling policies. / Dissertation/Thesis / Masters Thesis Electrical Engineering 2015