It has been suggested that projects in the construction industry are subject to risks more than other industries. However, there is often little parity in allocation of risks in the construction industry. Usually, project participants allocate risks by aversion where owners tend to shift risks to the primary contractor, who in turn transfers them to the subcontractors. As a result of this, risks are not necessarily allocated/ re-allocated to the party that is best able to manage them efficiently and effectively. Risk allocation can significantly influence the behavior of the project participants and hence affects project schedule, cost and performance. Inappropriate risk allocation has led to adversarial relationships between contracting participants and has consequently increased project cost. The objective of this dissertation is to shed light on the current practices of risk allocation in the construction industry. The dissertation consists of three sections. The first section investigates and evaluates the problems of the current practice of risks allocation and their impacts on project performance. The second section investigates, identifies, and classifies barriers to optimal risk allocation. The third section looks into allocating construction risk from a more cooperative and rational perspective. The goal is to provide the construction industry with a rational decision-making mechanism that will provide an alternative to the current practice of typically allocating risks by aversion. To meet the objectives, structured survey questionnaires for Sections One and Two were used. The first survey found that the current practice of risk allocation has four major problems. These problems include: 1. Dispute, claims and tension leads to adversarial relationships. 2. Competitive relationship leads to aggressive relationships. 3. Subjective pricing of risk leading to higher contingency. 4. Allocation by aversion that leads to misallocation of risks. The second survey found thirteen barriers to optimal risk allocation, which were classified into three main categories: behavioral, technical, and organizational barriers. Lack of an efficient risk allocation mechanism ranks at the top of the identified barriers. These findings were linked, in causal-effects relationships, to formulate an analytic model for the current practice of risk allocation. This dissertation uses the research findings and the rational decision-making process to develop a practical mechanism for optimizing risk allocation. The developed mechanism was then fine-tuned and validated by a Delphi expert panel technique. The developed mechanism should aid construction industry professionals and construction project participants in making rational and economical risk allocation decisions to alleviate the identified above-mentioned problems, overcome the identified barriers, and improve project efficiency by minimizing the negative impacts of the current practice of risk allocation on project cost, schedule and overall project performance. / Graduation date: 2013 / Access restricted to the OSU Community at author's request from Jan. 4, 2013 - July 4, 2013

Orientador: Luiz Carlos Kretly / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-08T18:48:25Z (GMT). No. of bitstreams: 1 Portelinha_FranciscoMartins_D.pdf: 24786076 bytes, checksum: 9371c9850eefc768ce24e777299b0788 (MD5) Previous issue date: 2007 / Resumo: Em freqüênciasabaixode 3 GHz, ocorreuma demandade bandas no espectrode freqüência, devido à expansãodas redes de comunicaçõessem fio, principalmentepara aplicações outdoor. Estudosmostram que há grandes lacunas no espectrode freqüência até a faixa de 3 GHz. O modelo de alocação do espetrode freqüência,já ultrapassado, precisa ser reformuladopara uso destas lacunas.A utilização do espectro deve sair do modelo estático,para o modelodinâmico.Rádioscognitivose redes de rádios cognitivos surgem como opção tecnológica para uso deste novo modelo. Apresentamos uma arquitetura inovadorapara a implementaçãode rádios cognitivos,baseadosnos modelos computacionais de: rádios definidos por software,agentes e frameworks.Um estudo de caso, para rádio cognitivo nível 2, é apresentado para uso não licenciado no espectro nas faixas licenciadas para TV. Um algoritmo inovador, para detecção da disponibilidade de canais, é desenvolvido utilizando redes neurais / Abstract: In frequencies lower than 3 GHz, a demand occurs out of the frequency spectrum due to the expansion of the wireless communication network, mainly for outdoor applications. Studies show that, there are great gaps of the frequency spectrum in the bands up to 3 GHz. The allocation model of the frequency spectrum needs reformulatiQn for the use of these gaps. This utilization must come from, the change of a static model to a dynamic one. Cognitive radio and cognitive radio networks rise as a technological option for the use of the new model. We introduce and an innovatory architecture for the implementation of cognitive radios, based on the computational models of: software defined radio, agents and frameworks. A case study, for cognitive radio leveI 2, is introduced for use in licensed TV bands. A new algorithm is developed to detect available channel / Doutorado / Telecomunicações e Telemática / Doutor em Engenharia Elétrica

No description available.

Waste Load Allocation (WLA) in rivers refers to the determination of required pollutant fractional removal levels at a set of point sources of pollution to ensure that water quality standards are maintained throughout the system. Optimal waste load allocation implies that the selected pollution treatment vector not only maintains the water quality standards, but also results in the best value for the objective function defined for the management problem. Waste load allocation problems are characterized by uncertainties due to the randomness and imprecision. Uncertainty due to randomness arises mainly due to the random nature of the variables influencing the water quality. Uncertainty due to imprecision or fuzziness is associated with setting up the water quality standards and goals of the Pollution Control Agencies (PCA), and the dischargers (e.g., industries and municipal dischargers). Many decision problems in water resources applications are dominated by natural, extreme, rarely occurring, uncertain events. However usually such events will be absent or be rarely present in the historical records. Due to the scarcity of information of these uncertain events, a realistic decision-making becomes difficult. Furthermore, water resources planners often deal with imprecision, mostly due to imperfect knowledge and insufficient or inadequate data. Therefore missing data is very common in most water resources decision problems. Missing data introduces inaccuracy in analysis and evaluation. For instance, the sample mean of the available data can be an inaccurate estimate of the mean of the complete data. Use of sample statistics estimated from inadequate samples in WLA models would lead to incorrect decisions. Therefore there is a necessity to incorporate the uncertainty due to missing data also in WLA models in addition to the uncertainties due to randomness and imprecision. The uncertainty in the input parameters due to missing or inadequate data renders the input parameters (such as mean and variance) as interval grey parameters in water quality decision-making. In a Fuzzy Waste Load Allocation Model (FWLAM), randomness and imprecision both can be addressed simultaneously by using the concept of fuzzy risk of low water quality (Mujumdar and Sasikumar, 2002). In the present work, an attempt is made to also address uncertainty due to partial ignorance due to missing data or inadequate data in the samples of input variables in FWLAM, considering the fuzzy risk approach proposed by Mujumdar and Sasikumar (2002). To address the uncertainty due to missing data or inadequate data, the input parameters (such as mean and variance) are considered as interval grey numbers. The resulting output water quality indicator (such as DO) will also, consequently, be an interval grey number. The fuzzy risk will also be interval grey number when output water quality indicator is an interval grey number. A methodology is developed for the computation of grey fuzzy risk of low water quality, when the input variables are characterized by uncertainty due to partial ignorance resulting from missing or inadequate data in the samples of input variables. To achieve this, an Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is developed for water quality management of a river system to address uncertainties due to randomness, fuzziness and also due to missing data or inadequate data. Monte Carlo Simulation (MCS) incorporating a water quality simulation model is performed two times for each set of randomly generated input variables: once for obtaining the upper bound of DO and once for the lower bound of DO, by using appropriate upper or lower bounds of interval grey input variables. These two bounds of DO are used in the estimation of grey fuzzy risk by substituting the upper and lower values of fuzzy membership functions of low water quality. A backward finite difference scheme (Chapra, 1997) is used to solve the water quality simulation model. The goal of PCA is to minimize the bounds of grey fuzzy risk, whereas the goal of dischargers is to minimize the fractional removal levels. The two sets of goals are conflicting with each other. Fuzzy multiobjective optimization technique is used to formulate the multiobjective model to provide best compromise solutions. Probabilistic Global Search Lausanne (PGSL) method is used to solve the optimization problem. Finally the results of the model are compared with the results of risk minimization model (Ghosh and Mujumdar, 2006), when the methodology is applied to the case study of the Tunga-Bhadra river system in South India. The model is capable of determining a grey fuzzy risk with the corresponding bounds of DO, at each check point, rather than specifying a single value of fuzzy risk as done in a Fuzzy Waste Load Allocation Model (FWLAM). The IFWLAM developed is based on fuzzy multiobjective optimization problem with ‘max-min’ as the operator, which usually may not result in a unique solution and there exists a possibility of obtaining multiple solutions (Karmakar and Mujumdar, 2006b). Karmakar and Mujumdar (2006b) developed a two-phase Grey Fuzzy Waste Load Allocation Model (two-phase GFWLAM), to determine the widest range of interval-valued optimal decision variables, resulting in the same value of interval-valued optimal goal fulfillment level as obtained from GFWLAM (Karmakar and Mujumdar 2006a). Following Karmakar and Mujumdar (2006b), two optimization models are developed in this study to capture all the decision alternatives or multiple solutions: one to maximize and the other to minimize the summation of membership functions of the dischargers by keeping the maximum goal fulfillment level same as that obtained in IFWLAM to obtain a lower limit and an upper limit of fractional removal levels respectively. The aim of the two optimization models is to obtain a range of fractional removal levels for the dischargers such that the resultant grey fuzzy risk will be within acceptable limits. Specification of a range for fractional removal levels enhances flexibility in decision-making. The models are applied to the case study of Tunga-Bhadra river system. A range of upper and lower limits of fractional removal levels is obtained for each discharger; within this range, the discharger can select the fractional removal level so that the resulting grey fuzzy risk will also be within specified bounds. In IFWLAM, the membership functions are subjective, and lower and upper bounds are arbitrarily fixed. Karmakar and Mujumdar (2006a) developed a Grey Fuzzy Waste Load Allocation Model (GFWLAM), in which uncertainty in the values of membership parameters is quantified by treating them as interval grey numbers. Imprecise membership functions are assigned for the goals of PCA and dischargers. Following Karmakar and Mujumdar (2006a), a Grey Optimization Model with Grey Fuzzy Risk is developed in the present study to address the uncertainty in the memebership functions of IFWLAM. The goals of PCA and dischargers are considered as grey fuzzy goals with imprecise membership functions. Imprecise membership functions are assigned to the fuzzy set of low water quality and fuzzy set of low risk. The grey fuzzy risk approach is included to account for the uncertainty due to missing data or inadequate data in the samples of input variables as done in IFWLAM. Randomness and imprecision associated with various water quality influencing variables and parameters of the river system are considered through a Monte-Carlo simulation when input parameters (such as mean and variance) are interval grey numbers. The model application is demonstrated with the case study of Tunga-Bhadra river system in South India. Finally the results of the model are compared with the results of GFWLAM (Karmakar and Mujumdar, 2006a). For the case study of Tunga Bhadra River system, it is observed that the fractional removal levels are higher for Grey Optimization Model with Grey Fuzzy Risk compared to GFWLAM (Karmakar and Mujumdar, 2006a) and therefore the resulting risk values at each check point are reduced to a significant extent. The models give a set of flexible policies (range of fractional removal levels). Corresponding optimal values of goal fulfillment level and the grey fuzzy risk are all in terms of interval grey numbers. The IFWLAM and Grey Fuzzy Optimization Model with Grey Fuzzy Risk, developed in the study do not limit their application to any particular pollutant or water quality indicator in the river system. Given appropriate transfer functions for spatial distribution of the pollutants in water body, the models can be used for water quality management of any general river system.

Made available in DSpace on 2016-06-02T19:53:34Z (GMT). No. of bitstreams: 1 STARK_Felipe_2015.pdf: 2619946 bytes, checksum: 296213b74067cc59aea0beae2cf8f165 (MD5) Previous issue date: 2015-03-20 / Universidade Federal de Minas Gerais / The increase in the municipal and industrial waste generation has caused enviromental and public health problems and as a consequence laws exist to address the issue. In Brazil, with the Federal Law No. 12,350 / 10 about the Política Nacional de Resíduos Sólidos reverse logistics (RL) of some products has become mandatory, including waste tires. However, RL of waste tires has been structured since environmental government agency resolutions propose treatment for tires disposal incorrectly in the environment and new generation of waste tires. Currently, the reverse network is managed by the tire manufacturers and importers associations, and destinations are commonly used are co-processing in cement kilns, and the recovery of rubber and steel as secondary products, by processes such as scrapping or lamination. Increasing transportation and operation costs in the logistics network make the network design critical for the full compliance with the legal goal. This network design is aligned with a planning that considers the financial issues like the minimization of costs or the maximization of profit, while meets the requirements of environmental government agencies. So it involves key strategic decisions, as the location of facilities and material flows, taking into account many parameters simultaneously. The objective of this study is to propose a model for waste tires reverse logistics considering: (i) flows from the output of the collection points (called ecopontos ) to the destination companies; (ii) the possibility of processes as the sorting of used tires in usable condition or not; (iii) the grinding process as an intermediate phase and for which type of destination the tire would be sent. Still are considered fixed costs for the installation of storage centers, for sorting used tires, and intermediary companies, for grinding and separation of components, in addition to the variable operating costs, transportation and potential revenues generated from the substitution of raw materials or fuel in destination companies. The model is a mixed integer linear problem (MILP) with multiple time periods. Experiments are done with a single and multiple periods, finally were present some sensitivity analysis. Other financial constraints as the annual budget and an approach that includes the carbon footprint (CO2) in the transport and processing are explored. The results showed configurations that meet the goal and have a small profit, indicating that storage centers are preferred in places far from destination points and low demand, while the pre-processing companies have opposite behavior. When using the carbon footprint, it was found that the sorting of used tires gain more importance in the view of reducing emissions, because there is less emission in the reuse or refurbishment compared to the manufacturing of new tires. / O aumento na geração de resíduos urbanos e industriais tem ocasionado problemas de ordem ambiental e de saúde pública, e, como consequência, legislações específicas surgiram para tratar do assunto. No Brasil, a Lei Federal nº 12.350/10, da Política Nacional de Resíduos Sólidos, tornou obrigatória a logística reversa (LR) de alguns produtos, entre os quais o pneu usado sem condição de rodagem (inservível). Entratanto, a LR de pneus inservíveis já se apresentava em fase de estruturação, uma vez que resoluções ambientais propunham o tratamento do passivo deixado por anos de descarte incorreto dos pneus no meio ambiente. Atualmente, a rede reversa de pneus é administrada por associações de fabricantes e importadores, e as destinações comumente utilizadas são o coprocessamento, em fornos de cimenteira, e a recuperação da borracha e do aço como subprodutos, por meio de processos como a granulação ou a laminação. Os crescentes custos do transporte terrestre e as proposições de melhorias na rede logística tornam a configuração da rede de logística reversa de pneus como uma proposta para o total cumprimento da meta, alinhados ao planejamento que considere as questões financeiras como a minimização de custos logísticos ou maximização do lucro, enquanto cumprem as exigências dos órgãos ambientais. Esta configuração envolve decisões estratégicas essenciais, como a localização de instalações e determinação dos fluxos de materiais, sendo que muitos parâmetros estão presentes simultaneamente. O objetivo deste estudo é propor um modelo de configuração de rede logística reversa de pneus inservíveis considerando: (i) fluxos a partir da saída dos pontos de coleta (ecopontos) até as empresas destinadoras; (ii) processos como a possibilidade da triagem dos pneus usados em servíveis e inservíveis; (iii) a trituração como fase intermediária e para qual tipo de destinação enviar. São considerados ainda custos fixos para instalação de centros de armazenamento, para triagem dos pneus usados, e empresas intermediárias, para trituração e separação de componentes, além dos custos variáveis de operação, transporte e as possíveis rendas geradas com a substituição da matéria-prima ou combustível nas empresas destinadoras. O modelo apresenta formulação linear inteira mista (MILP) com múltiplos períodos. São feitos experimentos com único período, e com a variação de parâmetros. Posteriormente são estudadas restrições financeiras como orçamento anual e uma abordagem que inclui a pegada de carbono (CO2) no transporte e processamento. Os resultados encontrados apresentaram configurações que cumprem a meta e apresentam um pequeno lucro, indicando que os centros de armazenamento são preferíveis em locais afastados das destinadoras e com baixa demanda, enquanto as empresas intermediárias apresentam comportamento oposto. Quando se utilizou a pegada de carbono, verificou-se que a triagem de pneus ganha mais importância, dada a redução nas emissões do reuso ou reforma dos pneus em relação à fabricação de novos pneus.

Tato práce se zabývá technologií Multiprotocol Label Switching a to zejména moderními metodami, které je možné použít v rámci této technolologie. Jako příklad lze uvést využití podpory kvality služeb při směrování. V práci jsou navrhnuty a simulovány různé topologie a scénáře, které ověřují možnosti využití MPLS v podpoře kvality služeb.

South African Air Quality Units are continuously undergoing changes, and improving their performance remains a constant endeavour. In addition, these units are also experiencing several challenges in terms of improving communication across the different spheres, accessing air quality data and using the information to support the decision-making required for efficient management of air quality in South Africa. This study investigated the concept of output efficiency within the South African air quality management context. Models that enable efficient resource allocation can be used to assist managers in understanding how to become efficient. There are, however, few models that focus on the output efficiency of the public sector and air quality management units. The primary purpose of the study was to develop a model to predict the extent to which organisational efficiency could be explained by the percentage of man-hours allocated to a range of management activities. In this study, the development of a model using the logistic regression technique is discussed. Data was collected for two financial years (2005/6 and 2006/7) from the air quality officers in the national, provincial and local spheres of government (N=228). The logistic regression model fitted indicates that the proportion of time spent on knowledge management activities contributes the most to the likelihood of an Air Quality Unit being efficient. The resource allocation model developed will ensure that air quality officers allocate resources appropriately and improve their output performance. / Graduate School for Business Leadership / D.B. L.

South African Air Quality Units are continuously undergoing changes, and improving their performance remains a constant endeavour. In addition, these units are also experiencing several challenges in terms of improving communication across the different spheres, accessing air quality data and using the information to support the decision-making required for efficient management of air quality in South Africa. This study investigated the concept of output efficiency within the South African air quality management context. Models that enable efficient resource allocation can be used to assist managers in understanding how to become efficient. There are, however, few models that focus on the output efficiency of the public sector and air quality management units. The primary purpose of the study was to develop a model to predict the extent to which organisational efficiency could be explained by the percentage of man-hours allocated to a range of management activities. In this study, the development of a model using the logistic regression technique is discussed. Data was collected for two financial years (2005/6 and 2006/7) from the air quality officers in the national, provincial and local spheres of government (N=228). The logistic regression model fitted indicates that the proportion of time spent on knowledge management activities contributes the most to the likelihood of an Air Quality Unit being efficient. The resource allocation model developed will ensure that air quality officers allocate resources appropriately and improve their output performance. / Graduate School for Business Leadership / D.B. L.

In this study, methodologies for modeling grey uncertainty in water resources systems are developed, specifically for the problems in two identified areas in water resources: waste load allocation in streams and floodplain planning. A water resources system is associated with some degree of uncertainty, due to randomness of hydrologic and hydraulic parameters, imprecision and subjectivity in management goals, inappropriateness in model selection, inexactness of different input parameters for inadequacy of data, etc. Uncertainty due to randomness of input parameters could be modeled by the probabilistic models, when probability distributions of the parameters may be estimated. Uncertainties due to imprecision in the management problem may be addressed by the fuzzy decision models. In addition, some parameters in any water resources problems need to be addressed as grey parameters, due to inadequate data for an accurate estimation but with known extreme bounds of the parameter values. Such inexactness or grey uncertainty in the model parameters can be addressed by the inexact or grey optimization models, representing the parameters as interval grey numbers. The research study presented in this thesis deals with the development of grey and fuzzy optimization models, and the combination of the two for water resources systems decision-making. Three grey fuzzy optimization models for waste load allocation, namely (i) Grey Fuzzy Waste Load Allocation Model (GFWLAM), (ii) two-phase GFWLAM and (iii) multiobjective GFWLAM, and a Grey Integer Programming (GIP) model for floodplain planning, are developed in this study. The Grey Fuzzy Waste Load Allocation Model (GFWLAM) for water quality management of river system addresses uncertainty in the membership functions for imprecisely stated management goals of the Pollution Control Agency (PCA) and dischargers. To address the imprecision in fixing the boundaries of membership functions (also known as membership parameters), the membership functions themselves are treated as imprecise in the model and the membership parameters are expressed as interval grey numbers. The conflict between the fuzzy goals of PCA and dischargers is modeled using the concept of fuzzy decision, but because of treating the membership parameters as interval grey numbers, in the present study, the notion of ‘fuzzy decision’ is extended to the notion of ‘grey fuzzy decision’. A terminology ‘grey fuzzy decision’ is used to represent the fuzzy decision resulting from the imprecise membership functions. The model provides flexibility for PCA and dischargers to specify their aspirations independently, as the membership parameters for membership functions are interval grey numbers in place of a deterministic real number. In the solution, optimal fractional removal levels of the pollutants are obtained in the form of interval grey numbers. This enhances the flexibility and applicability in decision-making, as the decision-maker gets a range of optimal solutions for fixing the final decision scheme considering technical and economic feasibility of the pollutant treatment levels. The methodology is demonstrated with the case studies of a hypothetical river system and the Tunga-Bhadra river system in Karnataka, India. Formulation of GFWLAM is based on the approach for solving fuzzy multiple objective optimization problem using max-min as the operator, which usually may not result in a unique solution. The two-phase GFWLAM captures all the alternative optimal solutions of the GFWLAM. The solution technique in the Phase 1 of two-phase GFWLAM is the same as that of GFWLAM. The Phase 2 maximizes upper bounds and minimizes lower bounds of decision variables, keeping the optimal value of goal fulfillment level same as obtained in the Phase 1. The two-phase GFWLAM gives the unique, widest, intervals of the optimal fractional removal levels of pollutant corresponding to the optimal value of goal fulfillment level. The solution increases the widths of interval-valued fractional removal levels of pollutants by capturing all the alternative optimal solutions and thus enhances the flexibility and applicability in decision-making. The model is applied to the case study of Tunga-Bhadra river system, which shows the existence of multiple solutions when the GFWLAM is applied to the same case study. The width of the interval of optimal fractional removal level plays an important role in the GFWLAM, as more width in the fractional removals implies a wider choice to the decision-makers and more applicability in decision-making. The multiobjective GFWLAM maximizes the width of the interval-valued fractional removal levels for providing a latitude in decision-making and minimizes the width of goal fulfillment level for reducing the system uncertainty. The multiobjective GFWLAM gives a new methodology to get a satisfactory deterministic equivalent of a grey fuzzy optimization problem, using the concept of acceptability index for a meaningful ranking between two partially or fully overlapping intervals. The resulting multiobjective optimization model is solved by fuzzy multiobjective optimization technique. The consistency of the solution is verified by solving the problem with fuzzy goal programming technique. The multiobjective GFWLAM avoids intermediate submodels unlike GFWLAM, so that the solution from a single deterministic equivalent of the GFWLAM adequately covers all possible situations. Although the solutions obtained from multiobjective GFWLAM provide more flexibility than those of the GFWLAM, its application is limited to grey fuzzy goals expressed by linear imprecise membership functions only, whereas GFWLAM has the capability to solve the model with any monotonic nonlinear imprecise membership functions also. The methodology is demonstrated with the case studies of a hypothetical river system and the Tunga-Bhadra river system in Karnataka, India. The Grey Integer Programming (GIP) model for floodplain planning is based on the floodplain planning model developed by Lund (2002), to identify an optimal mix of flood damage reduction options with probabilistic flood descriptions. The model demonstrates how the uncertainty of various input parameters in a floodplain planning problem can be modeled using interval grey numbers in the optimization model. The GIP model for floodplain planning does not replace a post-optimality analysis (e.g., sensitivity analysis, dual theory, parametric programming, etc.), but it provides additional information for interpretation of the optimal solutions. The results obtained from GIP model confirm that the GIP is a useful technique for interpretation of the solutions particularly when a number of potential feasible measures are available in a large scale floodplain planning problem. Though the present study does not directly compare the GIP technique with sensitivity analysis, the results indicate that the rigor and extent of post-optimality analyses may be reduced with the use of GIP for a large scale floodplain planning problem. Application of the GIP model is demonstrated with the hypothetical example as presented in Lund (2002).

No description available.