Desenvolvimento e aplicação de uma meta-heurística ao Home Health Care Problem

Nos últimos anos tem-se assistido a um crescimento na percentagem de população idosa, o que provocou um aumento na procura de cuidados ao domicílio. Como tal, é necessário haver um planeamento eficiente, através da otimização do uso dos recursos, humanos e materiais, bem como das rotas. O objetivo desta dissertação é desenvolver um algoritmo para melhorar o planeamento das rotas dos cuidados ao domicílio, auxiliando os prestadores de serviço (caregivers). A otimização foca na minimização do tempo de viagem. O modelo é uma extensão do Vehicle Routing Problem com janelas temporais, sincronização (quando 2 equipas de um caregiver se encontram ao mesmo tempo para realizar um serviço que necessita de dois caregivers), caregivers sem skills, para um horizonte temporal de um dia e com um tempo de trabalho diário máximo de 480 minutos por equipa. De modo a atingir o objetivo, foi aplicado o Biased Random Key Genetic Algorithm (BRKGA), o qual é uma extensão do Genetic Algorithm (GA). Para a obtenção dos resultados foi utilizado uma instância de teste com 75 clientes. Para realizar os serviços estão à disposição 15 caregivers, entre os quais poderão ser for- madas até oito combinações de equipas. As equipas são formadas por equipas de dois caregivers e equipas de um caregiver. Um objetivo adicional é o de encontrar a melhor combinação de equipas para o problema.In the recent years we have seen a growth of the elderly population percentage, which has caused an increase in demand for home care support. As such, there is a need for efficient planning, by optimizing the use of human and material resources, as well as routes. The goal of this dissertation is to develop an algorithm to improve the planning of home care routes by the caregivers. The optimization focuses on minimizing travel time. The model is an extension of the Vehicle Routing Problem with time windows, synchronization (when two teams of one caregiver meet at the same time do perform a service that needs two caregivers), caregivers without skills, for a time horizon of one day and with a maximum daily working time of 480 minutes per team. In order to achieve the goal, the Biased Random Key Genetic Algorithm (BRKGA) was applied, which is an extension of the Genetic Algorithm (GA). A test instance with 75 clients was used to obtain the results. To perform the services 15 caregivers are available, from which can be formed up to eight team combinations. The teams are made up of teams of two caregivers and teams of one caregiver. An additional goal is to find the best combination of teams for the problem

Optimizing Vaccine Supply Chains with Drones in Less-Developed Regions: Multimodal Vaccine Distribution in Vanuatu

In recent years, many less-developed countries (LDCs) have been exploring new opportunities provided by drones, such as the capability to deliver items with minimal infrastructure, fast speed, and relatively low cost, especially for high value-added products such as lifesaving medical products and vaccines. This dissertation optimizes the delivery network and operations for routine childhood vaccines in LDCs. It analyzes two important problems using mathematical programming, with an application in the South Pacific nation of Vanuatu. The first problem is to optimize the nation-wide multi-modal vaccine supply chain with drones to deliver vaccines from the national depot to all health zones in an LDC. The second problem is to optimize vaccine delivery using drones within a single health zone while considering the synchronization of drone deliveries with health worker outreach trips to remote clinics. Both problems consider a cold chain time limit to ensure vaccine viability. The two research problems together provide a holistic solution at the strategic and operational levels for the vaccine supply chain network in LDCs. Results from the first problem show that drones can reduce cost and delivery time simultaneously by replacing expensive and/or slow modes. The use of large drones is shown to save up to 60% of the delivery cost and the use of small drones is shown to save up to 43% of the delivery cost. The research highlights the tradeoff between delivery cost and service, with tighter cold chain limits providing faster delivery to health zones at the expense of added cost. Results from the second problem show that adding drones to delivery plans can save up to 40% of the delivery cost and improve the service time simultaneously by resupplying vaccines when the cold chain and payload limit of health workers are reached. This research contributes to both literature and practice. It develops innovative methodologies to model drone paths with relay stations and to optimize synchronized multi-stop drone trips with health worker trips. The models are tested with real-world data for an island nation (Vanuatu), which provides data for a geographic setting new to the literature on drone delivery and vaccine distribution

Modelling activities at a neurological rehabilitation unit

A queuing model is developed for the neurological rehabilitation unit at Rookwood Hospital in Cardiff. Arrivals at the queuing system are represented by patient referrals and service is represented by patient length of stay (typically five months). Since there are often delays to discharge, length of stay is partitioned into two parts: admission until date ready for discharge (modelled by Coxian phase-type distribution) and date ready for discharge until ultimate discharge (modelled by exponential distribution). The attributes of patients (such as age, gender, diagnosis etc) are taken into account since they affect these distributions. A computer program has been developed to solve this multi-server (21 bed) queuing system to produce steady-state probabilities and various performance measures. However, early on in the project it became apparent that the intensity of treatment received by patients has an effect on the time, from admission, until they are ready for discharge. That is, the service rates of the Coxian distribution are dependent on the amount of therapy received over time. This directly relates to the amount of treatment allocated in the weekly timetables. For the physiotherapy department, these take about eight hours to produce each week by hand. In order to ask the valuable what-if questions that relate to treatment intensity, it is therefore necessary to produce an automated scheduling program that replicates the manual assignment of therapy. The quality of timetables produced using this program was, in fact, considerably better than its alternative and so replaced the by-hand approach. Other benefits are more clinical time (since less employee input is required)and a convenient output of data and performance measures that are required for audit purposes. Once the model is constructed a number of relevant hypothetical scenarios are considered. Such as, what if delays to discharge are reduced by 50%? Also, through the scheduling program, the effect of changes to the composition of staff or therapy sessions can be evaluated, for example, what if the number of therapists is increased by one third? The effects of such measures are analysed by studying performance measures (such as throughput and occupancy) and the associated costs

GVSU Undergraduate and Graduate Catalog, 2011-2012

Grand Valley State University 2011-2012 undergraduate and/or graduate course catalog published annually to provide students with information and guidance for enrollment.https://scholarworks.gvsu.edu/course_catalogs/1086/thumbnail.jp