1000 resultados para Source location


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We offer a formulation that locates hubs on a network in a competitiveenvironment; that is, customer capture is sought, which happenswhenever the location of a new hub results in a reduction of thecurrent cost (time, distance) needed by the traffic that goes from thespecified origin to the specified destination.The formulation presented here reduces the number of variables andconstraints as compared to existing covering models. This model issuited for both air passenger and cargo transportation.In this model, each origin-destination flow can go through either oneor two hubs, and each demand point can be assigned to more than a hub,depending on the different destinations of its traffic. Links(``spokes'' have no capacity limit. Computational experience is provided.

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Previous covering models for emergency service consider all the calls to be of the sameimportance and impose the same waiting time constraints independently of the service's priority.This type of constraint is clearly inappropriate in many contexts. For example, in urban medicalemergency services, calls that involve danger to human life deserve higher priority over calls formore routine incidents. A realistic model in such a context should allow prioritizing the calls forservice.In this paper a covering model which considers different priority levels is formulated andsolved. The model heritages its formulation from previous research on Maximum CoverageModels and incorporates results from Queuing Theory, in particular Priority Queuing. Theadditional complexity incorporated in the model justifies the use of a heuristic procedure.

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When dealing with the design of service networks, such as healthand EMS services, banking or distributed ticket selling services, thelocation of service centers has a strong influence on the congestion ateach of them, and consequently, on the quality of service. In this paper,several models are presented to consider service congestion. The firstmodel addresses the issue of the location of the least number of single--servercenters such that all the population is served within a standard distance,and nobody stands in line for a time longer than a given time--limit, or withmore than a predetermined number of other clients. We then formulateseveral maximal coverage models, with one or more servers per service center.A new heuristic is developed to solve the models and tested in a 30--nodesnetwork.

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Apresentam‑se os principais resultados obtidos nas duas missões arqueológicas patrocinadas pelo Centro Português de Actividades Subaquáticas (CPAS) à ilha de São Vicente (República de Cabo Verde), em 1998 e em 2005. Em 1998, confirmou‑se o efectivo interesse arqueológico do sítio, localizado sobre o mar, em local abrigado da vasta baía de Salamansa, situada na parte setentrional da ilha, tendo‑se registado a respectiva extensão e estratigrafia e procedido à colheita de amostras para datação. Embora os resultados dessa campanha tivessem sido publicados, indicando estação de carácter habitacional, revelada pela notável acumulação de conchas, acompanhada de abundantes fragmentos de cerâmicas manuais, de produção africana, mantinha‑se indefinida a sua verdadeira natureza. Impunha‑se, assim, proceder à escavação integral da área que ainda subsistia da estação — sujeita de forma contínua a forte erosão marinha — bem como à colheita de novos materiais para datação, de forma a confirmar as conclusões preliminares anteriormente obtidas, objectivos que se concretizaram em 2005. Deste modo, foi possível concluir que, contrariando a hipótese, de início considerada, de poder corresponder a um testemunho da ocupação da ilha em época anterior à chegada dos Portugueses — hipótese que já as primeiras datas de radiocarbono contradiziam — se trata de um sítio onde uma unidade habitacional construída por muros de pedra seca, de planta ortogonal, revela inspiração europeia, aliás sublinhada pelos materiais exumados, onde estão representados produtos com tal origem, como cachimbos de caulino, vidros, faianças portuguesas, e projécteis de armas de fogo, a par de objectos oriundos do Extremo Oriente, num quadro dominado pelas produções cerâmicas africanas. Esta situação evidencia um estabelecimento cuja ocupação se centrou no século XVII, conforme indicam os materiais recolhidos e os resultados das datações obtidas, francamente aberto aos contactos de longa distância, apesar do isolamento do local escolhido. Os restos faunísticos recolhidos, com a presença deburro e de boi, sugerem um estacionamento sedentário, sendo a alimentação assegurada essencialmente pela captura de tartarugas, pela pesca e pela recolecção de moluscos marinhos (especialmente grandes lapas) e complementada pelo consumo de cabra, que poderia ser doméstica ou caçada, dado o estado selvagem a que retornou ali esta espécie. na última parte do trabalho, discutem‑se as diversas hipóteses susceptíveis de explicar esta estação — desde um entreposto comercial relacionado com a exploração agro‑pecuária da ilha de Santo Antão, passando por pequeno estabelecimento especializado de apoio à navegação, com a produção de carne salgada de tartaruga, até ter constituído refúgio relacionado com a intensa pirataria vigente à época no arquipélago, tendo presente os elementos históricos conhecidos, que, aliás, indicam que o início da ocupação permanente de São Vicente só se produziu a partir da segunda década do século XIX. Seja como for, a forte componente cultural africana revelada pelo espólio destes primeiros ocupantes da ilha expressa‑se também pelos rituais que terão envolvido o abandono do estabelecimento, com o enterramento de dois vasos emborcados sob o chão da habitação explorada, e a deposição de uma pequena taça, nas mesmas circunstâncias, junto à parede da mesma, do lado externo.

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The past four decades have witnessed an explosive growth in the field of networkbased facilitylocation modeling. This is not at all surprising since location policy is one of the mostprofitable areas of applied systems analysis in regional science and ample theoretical andapplied challenges are offered. Location-allocation models seek the location of facilitiesand/or services (e.g., schools, hospitals, and warehouses) so as to optimize one or severalobjectives generally related to the efficiency of the system or to the allocation of resources.This paper concerns the location of facilities or services in discrete space or networks, thatare related to the public sector, such as emergency services (ambulances, fire stations, andpolice units), school systems and postal facilities. The paper is structured as follows: first,we will focus on public facility location models that use some type of coverage criterion,with special emphasis in emergency services. The second section will examine models based onthe P-Median problem and some of the issues faced by planners when implementing thisformulation in real world locational decisions. Finally, the last section will examine newtrends in public sector facility location modeling.

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The paper presents a new model based on the basic Maximum Capture model,MAXCAP. The New Chance Constrained Maximum Capture modelintroduces astochastic threshold constraint, which recognises the fact that a facilitycan be open only if a minimum level of demand is captured. A metaheuristicbased on MAX MIN ANT system and TABU search procedure is presented tosolve the model. This is the first time that the MAX MIN ANT system isadapted to solve a location problem. Computational experience and anapplication to 55 node network are also presented.

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A new direction of research in Competitive Location theory incorporatestheories of Consumer Choice Behavior in its models. Following thisdirection, this paper studies the importance of consumer behavior withrespect to distance or transportation costs in the optimality oflocations obtained by traditional Competitive Location models. To dothis, it considers different ways of defining a key parameter in thebasic Maximum Capture model (MAXCAP). This parameter will reflectvarious ways of taking into account distance based on several ConsumerChoice Behavior theories. The optimal locations and the deviation indemand captured when the optimal locations of the other models are usedinstead of the true ones, are computed for each model. A metaheuristicbased on GRASP and Tabu search procedure is presented to solve all themodels. Computational experience and an application to 55-node networkare also presented.

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The Department’s 2007 Greenhouse Gas Inventory is a refinement of previous statewide inventories. It is a bottom-up inventory of two sectors – fossil fuel combustion at federally-recognized major sources of air pollution and fossil fuel combustion and ethanol fermentation at dry mill ethanol plants. This is the first bottomup greenhouse gas inventory conducted for Iowa and the first bottom-up greenhouse gas inventory of ethanol plants in the nation that the Department is aware of. In a bottom-up inventory, facility-specific activity data is used to calculate emissions. In a top-down inventory, aggregate activity data is used to calculate emissions. For example, this bottom-up inventory calculates greenhouse gas emissions from the fossil fuel combustion at each individual facility instead of using the total amount of fossil fuel combusted state-wide, which would be a top-down inventory method. The advantage to a bottom-up inventory is that the calculations are more accurate than a top-down inventory. However, because the two methods differ, the results from a bottom-up inventory are not directly comparable to a top-down inventory.

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Sarcosaprophagous macroinvertebrates (earthworms, termites and a number of Diptera larvae) enhance changes in the physical and chemical properties of organic matter during degradation and stabilization processes in composting, causing a decrease in the molecular weights of compounds. This activity makes these organisms excellent recyclers of organic matter. This article evaluates the succession of insects associated with the decomposition of solid urban waste separated at the source. The study was carried out in the city of Medellin, Colombia. A total of 11,732 individuals were determined, belonging to the classes Insecta and Arachnida. Species of three orders of Insecta were identified, Diptera, Coleoptera and Hymenoptera. Diptera corresponding to 98.5% of the total, was the most abundant and diverse group, with 16 families (Calliphoridae, Drosophilidae, Psychodidae, Fanniidae, Muscidae, Milichiidae, Ulidiidae, Scatopsidae, Sepsidae, Sphaeroceridae, Heleomyzidae, Stratiomyidae, Syrphidae, Phoridae, Tephritidae and Curtonotidae) followed by Coleoptera with five families (Carabidae, Staphylinidae, Ptiliidae, Hydrophilidae and Phalacaridae). Three stages were observed during the composting process, allowing species associated with each stage to be identified. Other species were also present throughout the whole process. In terms of number of species, Diptera was the most important group observed, particularly Ornidia obesa, considered a highly invasive species, and Hermetia illuscens, both reported as beneficial for decomposition of organic matter.

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Cardiospheres (CSs) are self-assembling multicellular clusters from the cellular outgrowth from cardiac explants cultured in nonadhesive substrates. They contain a core of primitive, proliferating cells, and an outer layer of mesenchymal/stromal cells and differentiating cells that express cardiomyocyte proteins and connexin 43. Because CSs contain both primitive cells and committed progenitors for the three major cell types present in the heart, that is, cardiomyocytes, endothelial cells, and smooth muscle cells, and because they are derived from percutaneous endomyocardial biopsies, they represent an attractive cell source for cardiac regeneration. In preclinical studies, CS-derived cells (CDCs) delivered to infarcted hearts resulted in improved cardiac function. CDCs have been tested safely in an initial phase-1 clinical trial in patients after myocardial infarction. Whether or not CDCs are superior to purified populations, for example, c-kit(+) cardiac stem cells, or to gene therapy approaches for cardiac regeneration remains to be evaluated.

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In this paper we propose a metaheuristic to solve a new version of the Maximum CaptureProblem. In the original MCP, market capture is obtained by lower traveling distances or lowertraveling time, in this new version not only the traveling time but also the waiting time willaffect the market share. This problem is hard to solve using standard optimization techniques.Metaheuristics are shown to offer accurate results within acceptable computing times.

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New location models are presented here for exploring the reduction of facilities in aregion. The first of these models considers firms ceding market share to competitorsunder situations of financial exigency. The goal of this model is to cede the leastmarket share, i.e., retain as much of the customer base as possible while sheddingcostly outlets. The second model considers a firm essentially without competition thatmust shrink it services for economic reasons. This firm is assumed to close outlets sothat the degradation of service is limited. An example is offered within a competitiveenvironment to demonstrate the usefulness of this modeling approach.

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Models are presented for the optimal location of hubs in airline networks, that take into consideration the congestion effects. Hubs, which are the most congested airports, are modeled as M/D/c queuing systems, that is, Poisson arrivals, deterministic service time, and {\em c} servers. A formula is derived for the probability of a number of customers in the system, which is later used to propose a probabilistic constraint. This constraint limits the probability of {\em b} airplanes in queue, to be lesser than a value $\alpha$. Due to the computational complexity of the formulation. The model is solved using a meta-heuristic based on tabu search. Computational experience is presented.

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In this paper we address the issue of locating hierarchical facilities in the presence of congestion. Two hierarchical models are presented, where lower level servers attend requests first, and then, some of the served customers are referred to higher level servers. In the first model, the objective is to find the minimum number of servers and theirlocations that will cover a given region with a distance or time standard. The second model is cast as a Maximal Covering Location formulation. A heuristic procedure is then presented together with computational experience. Finally, some extensions of these models that address other types of spatial configurations are offered.