Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2016-04-27T11:59:30Z No. of bitstreams: 2 Dissertação - Max Gontijo de Oliveira - 2012.pdf: 3940401 bytes, checksum: 9d69259096bb8d7b7239f7eb20579d8d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2016-04-27T12:01:50Z (GMT) No. of bitstreams: 2 Dissertação - Max Gontijo de Oliveira - 2012.pdf: 3940401 bytes, checksum: 9d69259096bb8d7b7239f7eb20579d8d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2016-04-27T12:01:50Z (GMT). No. of bitstreams: 2 Dissertação - Max Gontijo de Oliveira - 2012.pdf: 3940401 bytes, checksum: 9d69259096bb8d7b7239f7eb20579d8d (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2012-10-08 / Several organizations need to solve the problem of locate and allocate facilities within a geographic area. There are location/allocation problems in various situations, like the distribution of police cars, ambulances, taxi drivers, bus stops among other numerous situations where the location of such facilities is strategic for organization. In location/allocation problems, usually is necessary allocate each demand point to the closest facility. So, each facility will be located in the center of demand points, considering the demand as weight. However, the majority of the real location problems have capacity constraint. Therefore, each facility has a certain capacity based on the type of demand. Facility location problems can be continuous or discrete. In continuous problems (also called Weber problem with multiple sources), any point in the plane is a potential site for the instalation of the facility. There are several approaches for working with continuous models. Furthermore, there are many others works approaches presenting models with capacity constraint. But most of these approaches turns the continous model to a discrete model. The objective of this work thesis is to present an approach to distribution of facilities in instances of the capacitated facility location problem. A case study will be presented with the purpose of evaluating the results. / Diversas organizações precisam lidar com o problema de localizar e alocar facilidades em uma região geográfica. Problemas de localização e alocação podem ser vistos, por exemplo, na distribuição de viaturas policiais, ambulâncias, viaturas de contenção de falhas em redes elétricas, taxistas, pontos de ônibus dentre outras inúmeras situações onde a localização de tais facilidades é um fator estratégico para a organização. Em problemas de localização/alocação de facilidades, geralmente aloca-se cada ponto de demanda à facilidade mais próxima e, localiza-se essa facilidade no centro dos pontos de demanda, considerando o valor da demanda como peso nessa distância. Entretanto, comumente, problemas reais de localização de facilidades possuem restrição de capacidade. Assim, cada facilidade possui uma certa capacidade em função do tipo de demanda. Problemas de localização de facilidades podem ser contínuos ou discretos. Em problemas contínuos (também chamados de problema de Weber com múltiplas fontes), qualquer ponto no plano é um potencial local para se instalar uma facilidade. Existem várias abordagens para trabalhar com modelos contínuos e outras tantas para trabalhar com modelos com restrição de capacidade, mas a maioria dessas abordagens realiza uma discretização do modelo. Assim, o objetivo desse trabalho é apresentar uma abordagem para gerar boas distribuições de facilidades para o problema de localização/alocação contínuo com restrição de capacidade. Um caso de estudo será apresentado com a finalidade de avaliar os resultados obtidos.

abstract: Due to environmental and geopolitical reasons, many countries are embracing electric vehicles (EVs) as an alternative to gasoline powered automobiles. Other alternative-fuel vehicles (AFVs) powered by compressed gas, hydrogen or biodiesel have also been tested for replacing gasoline powered vehicles. However, since the associated refueling infrastructure of AFVs is sparse and is gradually being built, the distance between recharging points (RPs) becomes a crucial prohibitive attribute in attracting drivers to use such vehicles. Optimally locating RPs will both increase demand and help in developing the refueling infrastructure. The major emphasis in this dissertation is the development of theories and associated algorithms for a new set of location problems defined on continuous network space related to siting multiple RPs for range limited vehicles. This dissertation covers three optimization problems: locating multiple RPs on a line network, locating multiple RPs on a comb tree network, and locating multiple RPs on a general tree network. For each of the three problems, finding the minimum number of RPs needed to refuel all Origin-Destination (O-D) flows is considered as the first objective. For this minimum number, the location objective is to locate this number of RPs to minimize weighted sum of the travelling distance for all O-D flows. Different exact algorithms are proposed to solve each of the three algorithms. In the first part of this dissertation, the simplest case of locating RPs on a line network is addressed. Scenarios include single one-way O-D pair, multiple one-way O-D pairs, round trips, etc. A mixed integer program with linear constraints and quartic objective function is formulated. A finite dominating set (FDS) is identified, and based on the existence of FDS, the problem is formulated as a shortest path problem. In the second part, the problem is extended to comb tree networks. Finally, the problem is extended to general tree networks. The extension to a probabilistic version of the location problem is also addressed. / Dissertation/Thesis / Doctoral Dissertation Industrial Engineering 2020

Health care, and especially access to health care, has always been a critical metric for countries. In 2017, South Africa spent 9% of its GDP on health care. Despite the GDP health care allocation being 5% higher than recommended by the World Health Organisation for a country of its socio-economic status, South Africa's health status is poor compared to similar countries. In 1994, South Africa implemented a health care policy to make health care accessible to all South Africans. A primary health care facility within 5km of the place of residence is deemed accessible. There is still a significant gap between the actual and desired accessibility, especially for the lower-income communities. There is a need to improve access to public health care for all South Africans. Cost-effective and sustainable solutions are required to solve this problem. Therefore, an opportunity was identified to investigate the location of low-cost container clinics in lower-income communities. This report uses robust optimisation and goal programming to find robust sites for cost-effective container clinics over multiple years in an uncertain environment using multiple future city development scenarios. The study area of the report includes three metro municipalities (City of Tshwane, City of Johannesburg, and City of Ekurhuleni) in Gauteng, South Africa. Three future development scenarios were created for this study using a synthetic population and urban growth simulation model developed by the CSIR. The model provided the population distribution from 2018 to 2030 for all three of the scenarios. The simulation model provides household attribute tables as an output. Household attributes that have a causal relationship with health care demand were investigated during the literature review. Based on the literature and the available household attributes, four attributes were selected to forecast the health care demand. The four attributes are household income, the number of children in the household, the household size, and the nearest clinic's distance. Using associative forecasting, the primary health care demand was forecasted from 2018 to 2030. These forecasts were used as input into the facility location models. A p-median facility location model was developed and implemented in Python. Since facility location problems are classified as NP-hard problems, heuristics and metaheuristics were investigated to speed up the problem solving. A GA selected as the metaheuristic be used to determine a suitable configuration of facilities for each scenario. The model determined good locations of clinics from a set of candidate locations. A good year to open each clinic is also determined by the model. These decisions are made by minimising three variables: total distances travelled by the households to their nearest clinics, the total distance from the selected distribution centre to the open clinics and the total building cost. An accessibility target of 90% was added to the model to ensure that at least 90% of the households are within 5km of the nearest clinic within the first five years. In these models, operating costs were not included. Therefore all the results are skewed, with most of the clinics being opened in the first year when it is the cheapest since there is no penalty for opening a clinic before it is needed — the exclusion of operating costs is a shortcoming to address in future work. A goal programming model was developed with the variables of the individual scenarios as the goals. The goal programming model was implemented in Python and used to determine a robust configuration of where and in what year to open container clinics. A difference of 25% was set as the upper limit for the difference between the robust configuration variables and the good or acceptable variables for the individual scenarios as the scenarios investigated are very different. This ensured that the robust solution would perform well for any of the three scenarios. The model was able to find locations that provided a relatively good solution to all the scenarios. This came with a cost increase, but that is a trade-off that must be made when dealing with uncertainty. This model is a proof of concept to bridge the gap between urban planning with multiple development scenarios and facility location, more specifically robust facility location. The biggest rendement was achieved by constructing and placing the container clinics in the shortest space of time because the 90% accessibility requirement can be addressed cost-effectively without an operating cost penalty ― this is unfortunately not possible in reality due to budget constraints. An accessibility analysis was conducted to investigate the impact of the accessibility percentage on the variable values and to test the model in a scenario closer resembling the real world by adding a budget constraint. The time limit of the accessibility requirement was removed. In this case, a gradual improvement in the accessibility over the 12 years was observed due to the gradual opening of clinics over the years. Based on the analyses results, it was concluded that the model is sensitive to changes in parameters and that the model can be used for different scenarios. / Dissertation (MEng (Industrial Engineering))--University of Pretoria, 2021. / Industrial and Systems Engineering / MEng (Industrial Engineering) / Unrestricted

This report fulfills the requirements of Geography 4C6. / This research report focuses on explaining the attitudinal and behavioral relationship toward public facilities. This relationship is founded from two bodies of theory; public facility location theory and the theory of attitude formation. Empirical results for testing the relationship were obtained through a survey of the Downsview community's attitudes toward Toronto's proposed dome stadium. It was found that the relationship between attitudes and behaviour can stem from the perceived impacts that a facility may generate on one's neighborhood as well as one's perceived effect or influence creating a change in the planning process. / Thesis / Bachelor of Arts (BA)

In this paper, a two-stage stochastic facility location problem integrated with inven- tory and recourse decisions is studied and solved. This problem is inspired by an industrial supply chain design problem of a large retail chain with slow-moving prod- ucts. Uncertainty is expressed by a discrete and finite set of scenarios. Recourse actions can be taken after the realization of random demands. Location, inventory, transportation, and recourse decisions are integrated into a mixed-integer program with an objective minimizing the expected total cost. A dual heuristic procedure is studied and embedded into the sample average approximation (SAA) method. The computation experiments demonstrate that our combined SAA with dual heuristic algorithm has a similar performance on solution quality and a much shorter compu- tational time. / Thesis / Master of Applied Science (MASc)

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Disaster debris cleanup operations are commonly organized into two phases. During the first phase, the objective is to clear debris from evacuation and other important pathways to ensure access to the disaster-affected area. Practically, Phase 1 activities largely consist of pushing fallen trees, vehicles, and other debris blocking streets and highways to the curb. These activities begin immediately once the disaster has passed, with the goal of completion usually within 24 to 72 hours. In Phase 2 of debris removal, which is the focus of this study, completion can take months or years. Activities in this phase include organizing and managing curbside debris collection, reduction, recycling, and disposal operations (FEMA 2007). This dissertation research investigates methods for improving post-disaster debris cleanup operations—one of the most important and costly aspects of the least researched area of disaster operations management (Altay and Green 2006). The first objective is to identify the unique nature of the disaster debris cleanup problem and the important decisions faced by disaster debris coordinators. The second goal is to present three research projects that develop methods for assisting disaster management coordinators with debris cleanup operations. In the first project, which is the topic of Chapter 3, a facility location model is developed for addressing the problem of opening temporary disposal and storage reduction facilities, which are needed to ensure efficient and effective cleanup operations. In the second project, which is the topic of Chapter 4, a multiple objective mixed-integer linear programming model is developed to address the problem of assigning debris cleanup resources across the disaster-affected area at the onset of debris cleanup operations. The third project and the focus of Chapter 5 addresses the problem of equitably controlling ongoing cleanup operations in real-time. A self-balancing CUSUM statistical process control chart is developed to assist disaster management coordinators with equitably allocating cleanup resources as information becomes available in real-time. All of the models in this dissertation are evaluated using data from debris cleanup operations in Chesapeake, Virginia, completed after Hurricane Isabel in 2003. / Ph. D.

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