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Pickup and Delivery Problems (PDPs) constitute a class of Vehicle Routing Problems (VRPs) consisting of finding the optimal routes for a fleet of vehicles to deliver requests from a set of origin locations to a corresponding set of destinations. PDPs are NP-hard and have a wide variety of variants and potential constraints. This thesis evaluates methods for solving a dynamic single- vehicle PDP restricted by multiple time-related constraints. The problem is dynamic in the sense that new requests arrive as time is simulated and inserted into the vehicle’s pickup and delivery plan as it is being executed. The time- related constraints include limited time windows during which the requests may be picked up or delivered, as well as maximum ride times that items may spend in the vehicle before being delivered. To solve the problem, we adapt insertion heuristics based on Large Neighborhood Search (LNS) and Heuristic Destroy and Repair (HDR) to the problem and evaluate them in a comparative study. Solution methods for the PDP are also applied on the problem of dynamically assigning incoming orders to vehicles in a delivery service with a short planning horizon. A PDP-based order assignment strategy is compared with assignment strategies based on proximity and workload. Due to the short planning horizon of the target application, the study is focused on finding well-performing methods for quickly solving small PDPs containing 10-15 requests. Our results indicate that LNS outperforms HDR for small problem instances. However, the quick convergence of HDR allows it to outperform LNS for larger problem instances. We also show that applying a PDP- based assignment strategy in the order assignment problem allows the service to accommodate more requests than the alternative assignment strategies while simultaneously providing a significant reduction in operational costs. Future work may improve the order assignment strategy by incorporating more anticipatory functionality and streamlining the PDP methods with more efficient tests for the feasibility of solutions. / Pickup and Delivery Problems (PDP:er) utgör en grupp av Vehicle Routing Problems (VRP:er) som består av att hitta de optimala rutterna för en fordonsflotta för att leverera beställningar från en uppsättning av upplockningsplatser till motsvarande uppsättning av avlämningsplatser. PDP:er är NP-svåra och har en stor mängd olika varianter och potentiella begränsningar. Denna avhandling utvärderar metoder för att lösa ett dynamiskt enkel-fordon PDP med flera tidsrelaterade begränsningar. Problemet är dynamiskt i den mening att nya beställnigar anländer i samband med att tiden simuleras och sätts in i fordonets leveransplan samtidigt som den utförs. De tidsrelaterade begränsningarna innefattar begränsade tidsfönstren under vilka beställningar kan plockas upp eller lämnas av, samt maximala tider som hämtade föremål får tillbringa i fordonet innan de lämnas av. För att lösa problemet anpassar vi insättningsheuristiker baserade på Large Neighborhood Search (LNS) och Heuristic Destroy and Repair (HDR) till problemet och utvärderar dem i en jämförande studie. Lösningsmetoder för PDP tillämpas också på problemet att dynamiskt tilldela inkommande beställningar till fordon i en leveransservice med en kort planeringshorisont. En PDP-baserad tilldelningsstrategi jämförs med strategier baserade på närhet och arbetsbelastning. På grund av målapplikationens korta planeringshorisont så fokuserar studien på att hitta väl presterande metoder för att snabbt lösa små PDP:er som innehåller 10-15 förfrågningar. Våra resultat indikerar att LNS överträffar HDR för små probleminstanser. Däremot leder den snabba konvergensen av HDR till att den överträffar LNS för större probleminstanser. Vi visar också att tillämpningen av en PDP-baserad tilldelningsstrategi i tilldelningsproblemet gör att tjänsten kan tillgodose fler beställningar än de alternativa tilldelningsstrategierna, samtidigt som det ger en betydlig minskning av driftskostnaderna. Framtida arbete kan förbättra tilldelningsstrategin genom att integrera mer förutseende funktionalitet och effektivisera PDP-metoderna med ett mer effektivt test av genomförbarhet för lösningar.

[pt] Em meio ao avanço expressivo da tecnologia e às evoluções contínuas observadas no ramo de inteligência artificial, esta última se mostrou ter potencial para ser aplicada a diferentes setores da sociedade. No contexto de extrema competitividade e relevância crescente nos esportes mais famosos ao redor do mundo, o basquete se apresenta como um esporte interessante para a aplicação de mecanismos de apoio à decisão capazes de aumentar a eficácia e consistência de vitórias dos times nos campeonatos. Diante desse contexto, este estudo propõe o desenvolvimento de sistemas de apoio à decisão baseados em modelos de redes neurais e k-Nearest Neighbors (kNNs). O objetivo é avaliar, para cada substituição durante um jogo de basquete, qual grupo de jogadores em quadra, conhecido por quinteto, apresenta mais chances de ter uma maior vantagem sobre o adversário. Para tal, foram treinados modelos para classificar, ao final de uma sequência de posses de bola, a equipe que conseguiria vantagem, e prever a magnitude dessa vantagem. A base de dados foi obtida de partidas do Novo Basquete Brasil (NBB), envolvendo estatísticas de jogadores, detalhes de jogo e contextos diversos. O modelo apresentou uma acurácia de 76,99 por cento das posses de bola nas projeções de vantagem entre duas equipes em quadra, demonstrando o potencial da utilização de métodos de inteligência computacional na tomada de decisões em esportes profissionais. Por fim, o trabalho ressalta a importância do uso de tais ferramentas em complemento à experiência humana, instigando pesquisas futuras para o desenvolvimento de modelos ainda mais sofisticados e eficazes na tomada de decisões no âmbito esportivo. / [en] In light of the recent significant growth in technological capabilities andthe observed advancements in the field of computational intelligence, the latterhas demonstrated potential for application in various sectors of society. Inthe context of extreme competitiveness and increasing relevance in the mostfamous sports around the world, basketball presents itself as an interestingsport for the application of decision-support mechanisms capable of enhancingthe efficacy and consistency of team victories in championships. In this context,this study proposes the development of decision-support systems, such asneural networks and k-Nearest Neighbors (kNNs). The goal is to evaluate, foreach substitution during a match, which group of players in the field, knownas lineup, presents the most probability to be superior to their opponent. Forthis, models were trained to predict, during a sequence of possessions, theteam that would have advantage and the magnitude of this advantage. Thedatabase was obtained from Novo Basquete Brasil (NBB) matches, involvingplayers statistics, match details and different contexts.. The model achieved anaccuracy of 76,99 percent in projections of superiority between the playing lineups,demonstrating the potential of using computational intelligence methods indecision-making applied to professional sports. Finally, the study highlightsthe importance of using such tools in conjunction with human experience,encouraging future research for the development of even more sophisticatedand effective models for decision-making in the sports field.

This paper presents a survey of georeferenced point clouds. Concentration is, on the one hand, put on features, which originate in the measurement process themselves, and features derived by processing the point cloud. On the other hand, approaches for the processing of georeferenced point clouds are reviewed. This includes the data structures, but also spatial processing concepts. We suggest a categorization of features into levels that reflect the amount of processing. Point clouds are found across many disciplines, which is reflected in the versatility of the literature suggesting specific features. (authors' abstract)

Interstellar extinction and reddening are inescapable sources of uncertainty in the study of astronomical objects. Many creative ways to measure its effects have been developed, two of them being evaluated in this study. I apply two recently improved methods to estimate extinction toward individual stars, one based on interstellar absorption of sodium and the other based on opacity maps of the interstellar medium. The methods are applied to 14 stars in the local neighborhood, all within a distance of 250 pc from the Sun, and their results are compared. I find that both methods have severe limitations and large uncertainties, but can still be useful under the right conditions. I also provide suggestions on how the methods can be improved. / Interstellär extinktion och rödfärgning är oundvikliga osäkerhetskällor i studiet av astronomiska objekt. Många kreativa sätt att mäta dess effekter har utvecklats, av vilka två utvärderas i denna studie. Jag tillämpar två nyligen förbättrade metoder för att uppskatta utsläckning mot enskilda stjärnor, en baserad på interstellär absorption av natrium och den andra baserad på opacitetskartor över det interstellära mediet. Metoderna tillämpas på 14 stjärnor i solens närområde, alla inom ett avstånd av 250 pc, och deras resultat jämförs. Jag finner att båda metoderna har allvarliga begränsningar och stora osäkerheter, men ändå kan vara användbara under rätt förutsättningar. Jag ger också förslag på hur metoderna kan förbättras.

Simulated annealing is a useful heuristic for finding good solutions for difficult combinatorial optimization problems. In some engineering applications the quality of a solution is based upon how tolerant the solution is to changes in the environment. The concept of simulated annealing is based upon the metallurgical process of annealing where a material is tempered by heating and cooling. Genetic algorithms have been used to evolve solutions to complex problems by imitating the biological process of evolution using crossover and mutation to modify the candidate solutions. In coevolution a candidate solution is composed of multiple species each of which provides a portion of the candidate solution. Those individuals of a species that, in collaboration with the individuals from the other species, are evaluated as providing the most fit solution are the preferred individuals of a species. This work investigated whether robustness, defined as the ability of a solution to tolerate changes to the problem environment, could be improved by defining a neighborhood of fitness functions that are centered in the neighborhood of the nominal objective function. Simulated annealing was used to manage the subsequent narrowing of the neighborhood of fitness functions. Two robustness measures were developed that used samples from the neighborhood of objective functions; one employed the minimum fitness value, and the other employed the average fitness value. Coevolutionary genetic algorithms were used to generate candidate solutions employing the robustness measures. This study used three benchmark functions to evaluate the effects of the robustness measures. The results indicated that the robustness measures could produce solutions that were robust and, often, globally optimal for benchmark functions employed in the testing. Future work includes applying this framework to a broader class of optimization problems, investigating new neighborhood strategies, and devising new robustness measures.

Starbucks' success following Howard Schultz's purchase of the company in 1987 was largely the product of a particular historical moment, one rooted in the social and economic changes that manifested themselves in the built environment of the American metropolis from the 1970's to the present. Most contemporary observers saw Starbucks as a symbol of these changes – particularly those that fell under the complicated heading of gentrification – rather than recognizing it as an agent of change. This thesis reveals the development of Starbucks' character and expansion model from its humble beginnings in 1971. It offers an overview of the various theories of gentrification and neighborhood change, relating them to Seattle, and placing Starbucks within this narrative. Chapter three examines Starbucks as a commodity, a place, and a neighbor. As a commodity, the history and preparation of specialty coffee made it a de facto consumption choice for the rich, famous, and educated. Starbucks appropriated, packaged and marketed the drink's sophisticated characteristics toward its own ends. Meanwhile, Starbucks' claims of community centered on its perception and presentation as a "third place" – the public place of a new age. Finally, as a neighbor, Starbucks has been courted and rejected by communities, developers, and city governments seeking or spurning the changes – increased foot traffic, wealthier clientele, etc. – that often accompany the coffee giant's arrival to a neighborhood. Lastly, this thesis focuses on metropolitan areas, perhaps the most tangible places to think about capitalism and capitalist enterprises, with an emphasis on Seattle, Starbucks' native city.

My dissertation investigates the reasons behind the persistence of income among individuals and US counties. I look at the role of initial conditions in explaining current level of income. In my first essay, I look at how childhood neighborhood conditions affect income of a person. To study persistence, I model income as an autoregressive process where the coefficient on the lagged dependent variable heterogeneous across individuals. In my second essay, I derive a new way to measure chronic poverty, or long term poverty. Current measures of chronic poverty cannot be used to compare improvements of poverty rates over time. Using my measure, one can compare to see if chronic poverty rates changed over time. My third essay looks at the historical reasons behind differences in income between rich and poor counties in the US. There are about 250 counties in the US where poverty rates have been above 20 percent for the last 40 years. I look at whether current and past factors, or differences in technologies is the main reason behind persistence of high rates of poverty in these counties. Overall, I find that childhood neighborhood conditions have a big effect in determining the coefficient on the lagged dependent variable, that is, childhood neighborhood conditions affect persistence of income. I find that improving neighborhood poverty rates by one percentage point and father’s education by one year bring the greatest improvement of social welfare. In my second essay, I show the importance of measuring chronic poverty separately from total poverty; for example, between 2000 and 2005, total poverty declined, but chronic poverty rates actually increased, which shows that the long-term poor got worse off during that time period. In my last essay, I find that some US counties remained poor mainly because of differences in factor endowment, and past and present levels of human capital explain most of the differences in current level of income between poor and non-poor counties. Differences in factor endowments explained 80 percent of income between poor and non-poor counties, while technology accounted for only 20 percent of the difference.

The aim of this thesis is to explore the evolving relationship between neighborhood change and public housing in the historically black neighborhood of Rosewood in Austin, Texas. In October 2010, the Housing Authority of the City of Austin was awarded a grant to begin the process of redeveloping one of the nation’s oldest federally funded public housing facilities – Rosewood Courts. As the once segregated public housing complex is slated for redevelopment, community members representing an assortment of interests have engaged in a series of heated exchanges and elevated discourse surrounding the legacy of public housing in Austin, Texas. At the same time, the Rosewood Neighborhood has witnessed a dramatic transformation in recent decades, losing much of its long-standing black community to an ever emergent gentrifying population. This research evaluates the relationship between neighborhood change and public housing (re)development, highlighting the position of Rosewood Courts within larger processes of policy and political economy transformation. / text

This report studies the specific case of Highland Redevelopment as if would pursue a LEED ND certification. It highlights the major issues observed to fulfill compliance of LEED ND minimum requirements as it is proposed, as well as the importance of achieving this certification as a means to define a performance level of the development. / text