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<p> This dissertation examines the facility location problems in the presence of barrier regions and consists basically of four essays exploring new problems. Despite the fact that the facility location problems considering barriers to travel are more realistic than their unrestricted counterparts, research in the area is relatively limited. This is due to the computational complexity associated with them. </p> <p> The first essay analyzes the problem of locating a facility in a region in the presence of a probabilistic line barrier. The objective is to locate the facility such that the sum of the volume times distances between the facility and demand points is minimized. Some convexity results are presented and a solution algorithm is proposed. </p> <p> Another interrelated problem is locating a facility in a region where a fixed line barrier such as a borderline divides the region into two. The regions communicate with each other through a number of passage points located on the line barrier. A version of this problem with minisum objective has been studied in the literature where the locations of the passage points are known. The second essay considers a number of extensions to this problem and proposes an efficient solution methodology based on the Outer Approximation algorithm. </p> <p> The third essay discusses the problem of locating a rectangular barrier facility m an area where interactions among existing facilities are present. The problem has two objectives. The first objective is to minimize the interference of the barrier facility to the interactions among the existing facilities. The second objective is to find a center (minimax) location for the barrier facility. The problem is formulated as a bi-objective problem and a mixed integer program is proposed as a solution methodology. A Simulated Annealing algorithm is presented for an extension of the problem where expropriation of existing facilities is also possible. </p> <p> Finally, the last essay suggests a practical analog approach for facility location problems in the presence of barriers. The use of the analog for certain problems is justified through some analytical results and a number of problems that appeared in the literature are solved efficiently. </p> / Thesis / Doctor of Philosophy (PhD)

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<p> This paper is concerned with developing a methodology for identifying and measuring the dimensions of community opposition to externality-generating public facilities. It critically reviews the traditional modelling approaches to public facility location. The methodology, by which the dimensions of facility impact might be established for incorporation into political decision-making models of facility location, is then proposed. The results of a pilot empirical test of this methodology, using techniques of non-metric Multidimensional Scaling for the analysis of individuals' perception's, indicate the types of dimensions which might be derived from the application of the methodology to questions concerning public facility location. </p> / Thesis / Doctor of Philosophy (PhD)

[pt] Ao longo dos últimos anos o desenvolvimento da Pesquisa Operacional foi fundamental para o crescimento da indústria aérea. No Brasil, o órgão responsável pela fiscalização da aviação civil é a Agência Nacional de Aviação Civil (ANAC). O objetivo da dissertação é desenvolver um modelo de otimização para localização-alocação de pessoal (servidores) e aplicá-lo à um estudo de caso da ANAC, no contexto de Safety Oversight. Uma revisão sistematizada de literatura foi conduzida para identificar os gaps e soluções recentes na literatura de problemas de facility location. O objetivo descrito foi alcançado e o modelo matemático foi validado pelo Estudo de Caso proposto. O modelo alocou 31 porcento dos servidores da ANAC na Região Sudeste do Brasil, 25 porcento na Região Nordeste, 17 porcento na Região Norte, 17 porcento na Região Sul e 10 porcento na Região Centro-Oeste; reduzindo em 66 porcento a quantidade total de inspetores. Obteve-se ainda uma matriz de distribuição de capacitações por agência da ANAC, de forma que o tomador de decisão possa analisar o perfil ótimo de habilitações dos funcionários de cada agência. Uma análise de sensibilidade foi conduzida para avaliar a flexibilidade do modelo, que se mostrou eficiente para aplicações em problemas reais. / [en] Over the last years, Research Operations development has become fundamental for Aviation Industry. In Brazil, the agency responsible for Civil Aviation inspection is the National Agency of Civil Aviation (ANAC). This work aims the development of an optimal personnel location-allocation model and application in a case study at ANAC in Safety Oversight context. One Literature Review has been done for gaps identification and to find the most recent solution techniques for facility location problems. The research objective has been achieved, and the proposed case study has validated the model. The model located 31 percent of ANAC personnel in Brazilian Southeast Region, 25 percent in Northeast Region, 17 percent in North Region, 17 percent in South Region and 10 percent in Central-West Region; decreasing in 66 percent the total quantity of allocated inspectors. A capacities matrix has been constructed with model results; decision-makers can analyze the optimal distribution of personnel capacities in each facility. Finally, a sensitivity analysis has been done to test the model flexibility, which prove the model is efficient for real problems application.

A new branch of machine and deep learning models has evolved in constrained optimization, specifically in mixed integer programming problems (MIP). These models draw inspiration from earlier solver methods, primarily the heuristic, branch and bound. While utilizing the branch and bound framework, machine and deep learning models enhance either the computational efficiency or performance of the model. This thesis examines how imitating different variable selection strategies of classical MIP solvers behave on a state-of-the-art deep learning model. A recently developed deep learning algorithm is used in this thesis, which represents the branch and bound state as a bipartite graph. This graph serves as the input to a graph network model, which determines the variable in the MIP on which branching occurs. This thesis compares how imitating different classical branching strategies behaves on different algorithm outputs and, most importantly, time span. More specifically, this thesis conducts an empirical study on a MIP known as the facility location problem (FLP) and compares the different methods for imitation. This thesis shows that the deep learning algorithm can outperform the classical methods in terms of time span. More specifically, imitating the branching strategies resulting in small branch and bound trees give rise to a more rapid performance in finding the global optimum. Lastly, it is shown that a smaller embedding size in the network model is preferred for these instances when looking at the trade-off between variable selection and time cost. / En ny typ av maskin och djupinlärningsmodeller har utvecklats inom villkors optimering, specifikt för så kallade blandade heltalsproblem (MIP). Dessa modeller hämtar inspiration från tidigare lösningsmetoder, främst en heuristisk som kallas “branch and bound”. Genom att använda “branch and bound” ramverket förbättrar maskin och djupinlärningsmodeller antingen beräkningshastigheten eller prestandan hos modellen. Denna uppsats undersöker hur imitation av olika strategier för val av variabler från klassiska MIP-algoritmer beter sig på en modern djupinlärningsmodell. I denna uppsats används en nyligen utvecklad djupinlärningsalgoritm som representerar “branch and bound” tillståndet som en bipartit graf. Denna graf används som indata till en “graph network” modell som avgör vilken variabel i MIP-problemet som tas hänsyn till. Uppsatsen jämför hur imitation av olika klassiska “branching” strategier påverkar olika algoritmutgångar, framför allt, tidslängd. Mer specifikt utför denna uppsats en empirisk studie på ett MIP-problem som kallas för “facility location problem” (FLP) och jämför imitationen av de olika metoderna. I denna uppsats visas det att denna djupinlärningsalgoritm kan överträffa de klassiska metoderna när det gäller tidslängd. Mer specifikt ger imitation av “branching” strategier som resulterar i små “branch and bound” träd upphov till en snabbare prestation vid sökning av den globala optimala lösningen. Slutligen visas det att en mindre inbäddningsstorlek i nätverksmodellen föredras i dessa fall när man ser på avvägningen mellan val av variabler och tidskostnad.

Master of Science / Department of Industrial & Manufacturing Systems Engineering / Jessica L. Heier Stamm / Coordination between humanitarian organizations is critical during the response effort to a disaster, as coordinating aid improves efficiency, reduces duplication of efforts, and ultimately leads to better outcomes for beneficiaries. One particular challenge arises when temporary facilities must be established post-disaster due to the destruction of buildings. For example, the 2015 Nepal earthquakes created a need for the placement of over 4,000 temporary learning facilities after several school buildings were damaged or destroyed. It is important that humanitarians coordinate well to fill these needs efficiently and effectively, while maintaining equity among beneficiaries in the affected areas. This means ensuring that enough facilities are provided in a timely manner, and are distributed fairly to all in need. The goals of this thesis are to study coordination strategies focusing primarily on the placement of temporary educational facilities for children following a disaster. This research also aims to gather useful data by surveying active humanitarians in order to better understand their decisions made in the field. This work uses the results of this survey, along with publicly available data published after the 2015 Nepal earthquakes to create an agent-based simulation model, and uses the Nepal case study to demonstrate the efficacy of the model framework. This research finds that organizations' initial location of operation can greatly impact the number of facilities they are collectively able to establish, the geographic disparity across the region, and the organizations' utilization. Specifically, while focusing efforts on the districts with the most need is most efficient and effective, a more uniform approach yields a more equitable response. This work also finds that there can be a trade-off between overall effectiveness and the number of partnerships established in the field. These findings show a need for further study into the intricacies of coordination between humanitarian workers. This author advocates for the use of information sharing mechanisms among practitioners, as well as further utilization of agent-based modeling as a means of studying the complex nature of disaster response. Specifically there is a need to further study educational needs as a logistical problem, and strategies for solving the post-disaster facility location problem.

Some oil and gas companies are drilling and developing fields in the Arctic Ocean, which has an environment with sea ice called ice floes. These companies must protect their platforms from ice floe collisions. One proposal is to use a system that consists of autonomous underwater vehicles (AUVs) and docking stations. The AUVs measure the under-water topography of the ice floes, while the docking stations launch the AUVs and recharge their batteries. Given resource constraints, we optimize quantities and locations for the docking stations and the AUVs, as well as the AUV scheduling policies, in order to provide the maximum protection level for the platform. We first use an queueing approach to model the problem as a queueing system with abandonments, with the objective to minimize the abandonment probability. Both M/M/k+M and M/G/k+G queueing approximations are applied and we also develop a detailed simulation model based on the queueing approximation. In a complementary approach, we model the system using a multi-stage stochastic facility location problem in order to optimize the docking station locations, the AUV allocations, and the scheduling policies of the AUVs. A two-stage stochastic facility location problem and several efficient online scheduling heuristics are developed to provide lower bounds and upper bounds for the multi-stage model, and also to solve large-scale instances of the optimization model. Even though the model is motivated by an oil industry project, most of the modeling and optimization methods apply more broadly to any radial detection problems with queueing dynamics. / text

Hazardous Materials (HazMat) are transported throughout Canada in a great number of road shipments. The transportation of HazMat poses special risks for neighboring population and environment. While HazMat accidents are rare events, they could be catastrophic in nature and could result in substantial damage to nearby communities. Effective emergency response plays an important role in the safe transportation of HazMat. Transportation of HazMat involves different parties, including shippers, regulators, and surrounding communities. While the shipping party is responsible for safe delivery of HazMat shipments, it is the responsibility of local emergency service agencies to respond to accidents occurring within their jurisdictions. In this research, the emergency response to HazMat transport accidents is assumed to be delegated exclusively to specially trained and equipped HazMat teams. This research proposes a new comprehensive systematic approach to determine the best location of HazMat teams on regional bases utilizing HazMat transport risk as a location criterion. The proposed model is the first to consider emergency response roles in HazMat transport risk analysis, and was intended as an optimization tool to be used by practitioners for HazMat emergency response planning. Additionally, the proposed model can be used to assess risk implications in regards to current locations of HazMat teams in a region, and to develop effective strategies for locating HazMat teams, such as closing and/or relocating teams in the region. The model investigates how HazMat team locations can be tailored to recognize the risk of transporting HazMat and would provide a more objective set of input alternatives into the multi-criteria decision making process of regionally locating HazMat teams. The proposed model was applied to the region of southwestern Ontario in effort to illustrate its features and capabilities in the HazMat emergency response planning and decision making process. Accordingly, the model provided very useful insights while reviewing several HazMat team location strategies for the southwestern Ontario region and investigating tradeoff among different factors. This research contributes to a better understanding of emergency response roles by reducing HazMat transport risks, and will greatly benefit both researchers and practitioners in the field of HazMat transport and emergency response.