Dissertations / Theses on the topic 'WASTE ALLOCATION'

The Nuclear Waste Fund constitutes a financial system that finances future costs of the management of spent nuclear fuel as well as decommissioning of nuclear power plants. The fund invests its capital under strict rules which are stipulated in the investment policy established by the board. The policy stipulates that the fund can only invest according to certain allocation limits, and restricts it to invest solely in nominal and inflation-linked bonds issued by the Swedish state as well as treasury securities. A norm portfolio is built to compare the performance of the NWF’s investments. On average, the NWF has outperformed the norm portfolio on recent years, but it may not always have been optimal. Recent studies suggest that allocation limits should be revised over time as the return and risk parameters may change over time. This study focused on simulating three different portfolios where the allocation limits and investment options were extended to see if these extensions would outperform the norm portfolio while maintaining a set risk limit. Portfolio A consisted of OMRX REAL and OMRX TBOND indexes, Portfolio B consisted of OMRX REAL, OMRX TBOND and S&P Sweden 1+ Year Investment Grade Corporate Bond Indexes, and Portfolio C consisted of OMXR REAL, OMRX TBOND and OMXSPI indexes. The return of each portfolio for different weight distributions of the assets were simulated in MATLAB, and polynomial regression models were built in order to optimize the return as a function of the assets’ weights using a Lagrange Multiplier approach for each portfolio. The results depicted that the maximal returns of Portfolios A, B and C were 4.00%, 4.13% and 7.93% respectively, outperforming the norm portfolio’s average return of 3.69% over the time period 2009-2016.

The efficient organization of waste collection systems based on bins located along the streets involves the solution of several tactical optimization problems. In particular, the bin configuration and sizing at each collection site as well as the service frequency over a given planning horizon have to be decided. In this context, a higher service frequency leads to higher routing costs, but at the same time less or smaller bins are required, which leads to lower bin allocation investment costs. The bins used have different types and different costs and there is a limit on the space at each collection site as well as a limit on the total number of bins of each type that can be used. In this paper we consider the problem of designing a collection system consisting of the combination of a vehicle routing and a bin allocation problem in which the trade-off between the associated costs has to be considered. The solution approach combines an effective variable neighborhood search metaheuristic for the routing part with a mixed integer linear programming-based exact method for the solution of the bin allocation part. We propose hierarchical solution procedures where the two decision problems are solved in sequence, as well as an integrated approach where the two problems are considered simultaneously. Extensive computational testing on synthetic and real-world instances with hundreds of collection sites shows the benefit of the integrated approaches with respect to the hierarchical ones.

<p>Waste management is a global environmental issue which concerns about a very significant problem in today’s world. There is a considerable amount of disposal of waste without proper segregation which has lead to both economic and environment sufferings. It is still practiced in many cities. There is a tremendous amount of loss in terms of environmental degradation, health hazards and economic descend due to direct disposal of waste. It is better to segregate the waste at the initial stages where it is generated, rather than going for a later option which is inconvenient and expensive. There has to be appropriate planning for proper waste management by means of analysis of the waste situation of the area.</p><p>This paper would deal with, how Geographical Information System can be used as a decision support tool for planning waste management. A model is designed for the case study area in an Indian city for the purpose of planning waste management. The suggestions for amendments in the system through GIS based model would reduce the waste management workload to some extent and exhibit remedies for some of the SWM problems in the case study area. The waste management issues are considered to solve some of the present situation problems like proper allocation and relocation of waste bins, check for unsuitability and proximity convenience due to waste bin to the users, proposal of recyclable waste bins for the required areas and future suggestions. The model will be implemented on the Aurangabad city’s case study area data for the analysis and the results will suggest some modification in the existing system which is expected to reduce the waste management workload to a certain extent.</p>

L'amelioration de la qualite du cadre de vie, les nouvelles dispositions reglementaires dictees par la loi du 13 juillet 1992 concernant les centres d'enfouissement technique. . . Augmentent considerablement les productions de dechets vegetaux. Un tel phenomene amene les collectivites a rechercher de nouvelles methodes de traitements et d'elimination. Parmi les solutions techniquement envisageables, le compostage apparait comme l'une des issues d'avenir la plus prometteuse. La creation de nouvelles unites pose des problemes d'organisation spatiale. Il s'agira de determiner le nombre, la taille des plateformes de compostage, le choix des terrains qui par leur superficie et distance aux zones d'habitation. . . Seraient susceptibles d'accueillir les nouvelles usines de compostage. Le but de la presente recherche est de construire un outil d'aide a la decision, un systeme d'information geographique qui debouche, d'une part sur une simulation des besoins, d'autre part sur des propositions d'organisation spatiale, de localisation des centres de compostage.

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<br>Universidade Federal de Minas Gerais<br>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.<br>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.

Cette thèse couvre deux sujets : le dessin des permis à polluer et la gestion des déchets. Le premier chapitre analyse la mise en œuvre des permis à polluer. Le chapitre se concentre sur les impacts de la distribution liés à la sévérité de l’allocation gratuite basée sur la production courante quand deux secteurs sont couverts par le marché des permis et le plafond reste constant. Un nouveau type d’augmentation des profits dans les secteurs qui ne sont pas exposés à la concurrence internationale a été démontré théoriquement. Le deuxième chapitre traite la question de la différenciation de l’allocation des permis dans les différentes régions, liée à la possibilité des entreprises à délocaliser. Les conditions dans lesquelles le bien-être décroît avec la délocalisation sont déterminées. Dans ce cas, des distributions gratuites de permis peuvent être utilisées pour éviter la délocalisation des entreprises. Le troisième chapitre compare l’efficacité des programmes de la responsabilité élargie du producteur (REP) avec l’efficacité d’une ex-ante taxe. La taxe permet plus de flexibilité ex-ante quant aux conditions du marché, mais la REP permet plus d’adaptation ex-post aux réalisations des coûts. Ainsi, l’efficacité relative de la REP augmente avec l’incertitude des coûts et la compétitivité du marché<br>This thesis covers two subjects. One is the design of pollution permits and the other is the waste management. The first chapter analyses the implementation of pollution permits. It focuses on the distributional impacts linked with the stringency of output-based allocation,when two sectors are covered by the market for permits and the total cap is held constant. Theoretically demonstrated is a new type of profit increase in sectors that are not exposed to international competition. The second chapter addresses the issue of differentiating permit allocation across areas, this being linked to the possibility of firms to relocate. The conditions under which welfare decreases with relocation are determined. In such a case, free allowances may be used to prevent firms from relocating. The third chapter compares the efficiency of extended producer responsibility (EPR) programs and the efficiency of an ex-ante tax. The tax allows more ex-ante flexibility regarding market conditions, but the EPR allows more ex-post adaption to cost realizations. As a result, the relative efficiency of the EPR increases with uncertainty of the costs and competitiveness of the market

碩士<br>國立交通大學<br>交通運輸研究所<br>88<br>Recently, because of the economic growth in Taiwan, the number of the vehicle was increased rapidly. Furthermore, many waste tires untreated were resulted from the incompleteness of the cleaning and treatment of waste tires. Because piling up the waste tires arbitrarily often causes environmental and sanitary problems and brings accidental disasters, many countries begin to value these problems. On the other hand, waste tire is a kind of resource and can bring in fairly added-value by being treated and re-exploited. So related authorities start to attack importance to the management of waste tires. About the cleaning and treatment of waste tires, most researches preferred the treating method but not the proper planning of the location of processing station and transportation routes. And, the achievement level of the goal is uncertain. So fuzzy multi-objective programming method is applied to plan these problems at the same time in this study. Operation cost and environmental pollution are considered when deciding the locations of processing station. And transportation cost and the influence on traffic are considered when choosing the routes. At last, this study cites an example which to deal with the problems of waste tires in Taoyuan by the fuzzy multi-objective planning model. As a result, decision makers can plan the location of processing station and transportation routes properly by fuzzy multi-objective programming method. And the satisfactory degree of the total objectives is 0.62.

碩士<br>國立交通大學<br>環境工程系所<br>93<br>In determining a proper waste load allocation for a river, in addition to the relation between pollution sources and the receiving water body, factors such as land uses, catchment areas and population density should also be considered. In this study, the equity and effectiveness of waste load allocations based on different factors were evaluated by the proposed optimization models . The QUAL2E model was used to simulate the water quality variation of the river water body. The Genetic Algorithm was used to calibrate model parameters for improving the quality of simulation. Several waste load allocation optimization models were developed based on the impact coefficients determined by the water quality model and several equity factors. Allowable total waste loads were calculated by the optimization models with consideration of various equity factors to avoid possible discharge bias favored to any single area. A web-based decision support system (DSS) was developed to facilitate the analysis and decision for a proper waste load allocation. The DSS has been made available on the Internet and can be accessed from any where and any time on the network. The DSS has six major modules including a database, a data analysis, the water quality simulation model, an optimization, a web-based geographical information system (GIS) and a decision support modules. The database module manages the data collected for water quality, hydrology and point source pollutants. The data analysis module produces various statistical charts. The water quality simulation model module provides a friendly interface to simulate the water quality. The optimization module generates various waste load allocation alternatives. The web-based GIS module illustrates spatial results by graphical map layers. The decision support module integrated all other modules to facilitate the analysis of waste load allocation alternatives. A case study for Wu River was implemented to explore and demonstrate the applicability and effectiveness of the DSS.

碩士<br>國立臺北大學<br>自然資源與環境管理研究所在職專班<br>100<br>Municipal Solid Waste (MSW) can be used as fuel of combustion equipments in enterprises by proper procedures. Turning MSW into Waste Derived Fuel (WDF) could not only solve the problem of waste disposal, but also save the cost of fuels for enterprises. Furthermore, in the viewpoint of GHGs reduction, WDF, which is a substitute for combustion, is one of the ways to reduce emission. This study focused on waste rubber and waste liquid, proceeded carbon footprint verification (CFV) of WDF. Nevertheless, It will affect the quality of data in carbon footprint accounting if enterprises using an irrational principle of allocation for the duration of the CFV, and will result in different carbon footprints. In order to solve the problem of allocation which enterprises must confront with, it’s necessary to conduct a proper allocation principle, which they can follow and catch on. This study analyzed the calorific values of WDF and soft coal for boilers of circulating fluid bed in a domestic paper mill (in normal operating condition) ; in accordance with the allocation data, the proportions were, waste rubber: 0.33, waste woods: 0.18, sludge of pulp and paper: 0.18, soft coal: 0.31; CO2 emission: 2,771.227 kg, CH4: 0.47 kg, N2O: 0.073 kg. The research also reviewed incinerators of rotary kiln form the domestic recycling facility, with a ton of combination of waste liquid and MSW, in normal operating condition of input proportion, the output were, CO2:1,735.162 kg, CH4: 0.171 kg, N2O: 0.025 kg and the ratio of input was 0.53: 0.47 (waste liquid: MSW). The case studies belong to the allocation of repeatedly use system. It’s necessary to categorize the WDF to open loop or close loop; if it’s categorized to both of them, the formula of calculating recycled materials considering both open and close loop can be used to estimate allocation. This study of allocation was in accordance with the input and calorific value of WDF, and didn’t consider low-carbon allocation and emission. We suggest that the principle can be build according to its economic value (such as air pollution control fee) in order to illustrate environmental burden of WDF system, try to seek the proper proportion of low-carbon emission and achieve the goal of mitigation of global warming.

With the granting of self-government to the colonies of eastern Australia in the 1850s, each colony became responsible for its own land legislation. Each produced legislation that enabled settlement by small farmers, the selectors. In New South Wales, Victoria and Queensland this led to conflict between the selectors and those who had previously established their sheep runs on the land, the squatters, as they became known in Australia. The land legislation also enabled the development of agriculture in those colonies. Tasmania produced twenty-one Waste Lands Acts over a period of thirty-one years, and introduced a number of land schemes to attract immigrants. In spite of these attempts, the Tasmanian economy remained in depression, agricultural output declined, and immigration stagnated. This thesis argues that the Waste Lands Acts of Tasmania were critical for the economic development of the country. Under British rule, the land legislation had created a monopoly in which the large landholders, the pastoralists, controlled the best land and the parliament. After self-government, the Waste Lands Acts determined how and where people lived and they determined the economic and political relationships between the small farmers and the monopolists. This thesis has two major lines of enquiry. The first is centered on the land legislation, the Waste Lands Acts of Tasmania, under which land was alienated from 1858 to 1889. The second examines the way people lived under the provisions related to small farming. The main sources used include the legislation, the parliamentary papers, the parliamentary debates, and the official archives. A number of farm diaries and associated correspondence, from both the Tasmanian Archive and Heritage Office (TAHO) and from private collections, have been used, as well as contemporary newspapers and journals. The thesis has three parts. The first contains introductory material. It examines the systems of land alienation and the way people lived under these prior to self-government. It then provides an economic history for the period studied here, 1858 to 1890. The second part analyses the Waste Lands Acts, the debates that drove them, their provisions, their economic impact and the way the new settlers lived under them. The third part is a case study of an agricultural area opened for settlement under the Waste Lands Acts. This thesis contributes to knowledge by providing an economic and social history of a period previously little studied. It found that democratization of land ownership, a major driving force behind the land legislation in the other Australian colonies, was largely absent in Tasmania. Instead, the Waste Lands Acts were driven by the ideal of improvement, which was to be achieved by settling yeoman farmers on the land. Their implementation was flawed. The financial constraints, under which the Tasmanian government operated, meant the primary purpose of the land legislation must be to raise revenue, not encourage agriculture. They fuelled a pastoral land grab. Settlement of agricultural lands and exploration of the rich mineral lands were delayed by the practice of withdrawing lands from selection on the grounds that they might be auriferous. The operation of the Waste Lands Acts was further hampered by the refusal of the Legislative Council, Tasmania’s upper house in parliament, to agree to the construction of roads and bridges in the new areas being opened up. This prolonged the economic depression. In spite of these hindrances, selectors did establish new farms, contributing to the restructuring of agriculture and helping to fuel the development of regional economies.

博士<br>國立交通大學<br>環境工程系所<br>100<br>Municipal solid waste (MSW) collection is significant components of waste management; consequently, assessment methods for MSW collection performance warrant evaluation. Data envelopment analysis (DEA), a method frequently used for performance assessment, assigns the most advantage weight set to maximize the performance value of an evaluated unit. However, high values in a few indicators can lead to a unit being regarded as ‘efficient,’ despite valuing poorly in other essential indicators. Furthermore, spatial differences resulting from varying population densities between regions influence the practicality of assessments. Distribution of resources also plays an important role in the effectiveness of MSW collection, but prior resource distribution methods have yet to take this into account. Therefore, this study has developed methods to improve MSW collection performance evaluation and resource usage. This study developed methods to assess MSWC services and resource allocation for 307 Taiwan local governments. For overcoming the drawback of the DEA method,, Reverse Data Envelopment Analysis (RDEA) was established to discern the relative significance of indicators. DEA and RDEA models were then combined in this study to develop a more precise method of analysis, referred to here as the Inefficiency Countervailed Data Envelopment Analysis (IC-DEA), which can be verified through evaluation of corporate environmental performance. The indicator framework of MSW collection evaluation is more complex than IC-DEA case; therefore, when assessing performance, work-loading must also be considered. This study developed the Enhanced Inefficiency Countervailed Data Envelopment Analysis (EIC-DEA) method to analyze the MSW collection performance. Because the different weight sets of EIC-DEA results and spatial issue heavily influence MSW collection performance, the Spatial Inefficiency Countervailed Common Weight (SIC-CW) method was enhanced to obtain suitable common weight, addressing the issue of ineffective compensation and differences in spatial distribution. Additionally, appropriate resource usage can increase the effectiveness of MSW collection. Consequently, a dual mode approach was employed to create the MSW Resource Usage Analysis (MSW-RUA) method, which increases resource efficiency. Additional procedure indicators with work loading using these methods resulted in improvements over previous approaches for evaluation. Considerations of spatial differences and common weighting can also adjust for differences in performance caused by disparities in spatial distribution, and provide a more practical weighting structure. Results from this study can be used as references to determine improvements necessary for resource efficiency as well as follow-up planning. Approaches of analysis proposed in the study can be used to improve MSW collection performance assessment and resource usage.

碩士<br>國立臺北大學<br>財政學系<br>103<br>While some scholars have engaged in group (collective) rent-seeking analysis or multiple-prize contests analysis, previous papers never discuss the effect of rent allocation on rent-seeking waste under group rent-seeking contest structure. This paper plans to extend the model of between-group rent-seeking contest in Katz and Tokatlidu (1996). We take into consideration the different possibility of rent allocation between winning and defeating groups, and conduct the between-group rent-seeking analysis. Using our model, we focus our discussion on the influence of governmental rent allocation on group and social rent-seeking waste. This paper finds that the influence of governmental rent allocation on social rent-seeking waste will depend on the relative size of rent-seeking groups.

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.

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.

Στην εργασία αυτή αντιμετωπίζεται το πρόβλημα της χωροθέτησης εγκαταστάσεων και της κατανομής της ζήτησης σε περιφερειακό επίπεδο μέσω της παρουσίασης σχετικών μαθηματικών υποδειγμάτων μικτού ακέραιου προγραμματισμού, τα οποία στη συνέχεια εφαρμόζονται σε περιπτωσιολογική μελέτη. Η εργασία αφορά την χωροθέτηση δημοσίων εγκαταστάσεων. Ειδικότερα, αναφέρεται στην κατηγορία των προβλημάτων χωροθέτησης που χρησιμοποιούν την τυπική συνάρτηση αποδοτικότητας minsum. Σε αυτές τις περιπτώσεις οι εγκαταστάσεις τοποθετούνται έτσι ώστε να ελαχιστοποιούν το άθροισμα του συνολικού κόστους μεταφοράς. Η εργασία επικεντρώνεται στην χωροθέτηση οχληρών εγκαταστάσεων και συγκεκριμένα στη χωροθέτηση εγκαταστάσεων διαχείρισης αστικών απορριμμάτων. Αυτές οι εγκαταστάσεις ασκούν επιδράσεις όχι μόνο στον χώρο που καταλαμβάνουν, αλλά και σε μεγάλες εκτάσεις γύρω τους. Το πρόβλημα χωροθέτησης σε αυτές τις περιπτώσεις γίνεται ακόμα πιο πολύπλοκο λόγω της αναγκαίας συνεκτίμησης πολλαπλών κριτηρίων. Για τον σκοπό αυτό αναπτύσσεται ένα εξειδικευμένο πλαίσιο παραγωγής μαθηματικών υποδειγμάτων. Ειδικότερα,. στην παρούσα διδακτορική εργασία, δημιουργήθηκαν μαθηματικά υποδείγματα βελτιστοποίησης μικτού ακέραιου προγραμματισμού, τα οποία στη συνέχεια αναλύονται με μεθόδους πολυκριτηριακής ανάλυσης. Τα υποδείγματα αυτά εφαρμόζονται σε περιπτωσιολογική μελέτη με θέμα το χωροθετικό σχεδιασμό ενός περιφερειακού συστήματος διαχείρισης των αστικών απορριμμάτων για τον νομό Αχαΐας.<br>This study considers a facility location problem with demand allocation in regional level through mixed integer programming models that have been developed for these propose. Afterwards, these models applied in a case study. The present research concerns mostly the public facility location problems. In particular, the PhD dissertation deals with models using the typical mathematical function of efficiency minsum. Therese models locate the facilities in order to minimize the sum of costs for opening facilities and variable transportation costs. The problem becomes more complicated if, in addition it is necessary to locate undesirable facilities. The selection of proper sites for the facilities with obnoxious characteristics is a sensitive issue that has often cause social and political tension. The multiobjective nature of the problem implies that a number of environmental social and political criteria have to be considered. The study presents a model generating framework for formulating location allocation models that applied in municipal solid waste management systems. These models analyzed via multicriteria methodology in order to select the locations of different types of facilities at the regional level. Finally, we apply these concepts in a case study for the development of a solid waste management system for a specific region in Greece.

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).

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).