Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.

We consider a scheduling problem where each job requires multiple classes of resources, which we refer to as the multiple resource constrained scheduling problem(MRCSP). Potential applications include team scheduling problems that arise in service industries such as consulting and operating room scheduling. We focus on two general cases of the problem. The first case considers uncertainty of processing times, due dates, and resource availabilities consumption, which we denote as the stochastic MRCSP with uncertain parameters (SMRCSP-U). The second case considers uncertainty in the number of jobs to schedule, which arises in consulting and defense contracting when companies bid on future contracts but may or may not win the bid. We call this problem the stochastic MRCSP with job bidding (SMRCSP-JB).We first provide formulations of each problem under the framework of two-stage stochastic programming with recourse. We then develop solution methodologies for both problems. For the SMRCSP-U, we develop an exact solution method based on the L-shaped method for problems with a moderate number of scenarios. Several algorithmic enhancements are added to improve efficiency. Then, we embed the L-shaped method within a sampling-based solution method for problems with a large number of scenarios. We modify a sequential sampling procedure to allowfor approximate solution of integer programs and prove desired properties. The sampling-based method is applicable to two-stage stochastic integer programs with integer first-stage variables. Finally, we compare the solution methodologies on a set of test problems.For SMRCSP-JB, we utilize the disjunctive decomposition (D2 ) algorithm for stochastic integer programs with mixed-binary subproblems. We develop several enhancements to the D2 algorithm. First, we explore the use of a cut generation problem restricted to a subspace of the variables, which yields significant computational savings. Then, we examine generating alternative disjunctive cuts based on the generalized upper bound (GUB) constraints that appear in the second-stage of the SMRCSP-JB. We establish convergence of all D2 variants and present computational results on a set of instances of SMRCSP-JB.

Dryland salinity is increasing in the upper catchments of central and northern New South Wales, Australia. Consequently, salts may be exported downstream, which could adversely affect cotton irrigated-farming systems. In order to assess the potential threat of salinity a simple salt balance model based on progressively saline water (i.e., ECiw 0.4, 1.5, 4.0 and 9.0 dS/m) was used to simulate the potential impact of salinisation due to the farming systems. The study was carried out in the lower Namoi valley of northern New South Wales, Australia. A comparison has been made of the various non-linear techniques (indicator kriging, multiple indicator kriging and disjunctive kriging) to determine an optimal simulation method for the risk assessment. The simulation results indicate that potential salinisation due to application of the water currently used for irrigation (ECiw) is minimal and may not pose any problems to sustainability of irrigated agriculture. The same results were obtained by simulation based on irrigation using slightly more saline water (ECiw 1.4 dS/m). However, simulations based on irrigation using water of even lower quality (ECiw of 4 and 9.0 dS/m), shows potential high salinisation, which will require management inputs for sustainable cropping systems, especially legumes and wheat, which are used extensively in rotation with cotton. Disjunctive kriging was the best simulation method, as it produced fewer misclassifications in comparison with multiple-indicator kriging and indicator kriging. This study thus demonstrates that we can predict the salinity risk due to application of irrigation water of lower quality than that of the current water used. In addition, the results suggest here problems of excessive deep drainage and inefficient use of water might be a problem. The second part of this thesis deals with soil information required at the field scale for management practices particularly in areas where deep drainage is large. Unfortunately, traditional methods of soil inventory at the field level involve the design and adoption of sampling regimes and laboratory analysis that are time-consuming and costly. Because of this more often than not only limited data are collected. In areas where soil salinity is prevalent, detailed quantitative information for determining its cause is required to prescribe management solutions. This part deals with the description of a Mobile Electromagnetic Sensing System (MESS) and its application in an irrigated-cotton field suspected of exhibiting soil salinity. The field is within the study area of part one of this thesis-located about 2 km south west of Wee Waa. The EM38 and EM31 (ECa) data provide information, which was used in deciding where soil sample sites could be located in the field. The ECa data measured by the EM38 instrument was highly correlated with the effective cation exchange capacity. This relationship can be explained by soil mineralogy. Using different soil chemical properties (i.e. ESP and Ca/Mg ratio) a detailed transect study was undertaken to measure soil salinity adjoining the water storage. It is concluded that the most appropriate management option to remediation of the problem would be to excavate the soil directly beneath the storage floor where leakage is suspected. It is recommended that the dam not be enlarged from its current size owing to the unfavourable soil mineralogy (i.e. kaolin/illite) located in the area where it is located.

Manufactural activity is the basis of every sound economy. The risk for today's industrial establishments in our let us say european conditions is to hold competitiveness in the terms of global economy. This diploma thesis is focusing on solving problems of manufacturing scheduling with the view of theory and practice. It is impeach of real-life production. Scheduling belongs to hard combinatorial problems and therefore are usually solved by various heuristic or metaheuristic methods. For application of mentioned metaheuristic methods is important to use suitable choice of representative data.

To adapt to climate change which results in increasing flood frequency and intensity, the European Community has proposed Flood Directive 2007/60/EC. It requires member states to conduct risk assessments of all river basins and coastal areas and to establish Flood Risk Management Plans focused on prevention, protection and preparedness by 2015. Sustainable Flood Retention Basins (SFRB) that impound water are a new concept that arose in 2006. They can have a pre-defined or potential role in flood defense and were supposed to facilitate the implementation of the Flood Directive. Early and preliminary studies of SFRB were derived from case studies in Southern Baden, Germany. In Scotland, there are a relatively high number of SFRB which could contribute to flood management control. This research aimed to produce a guidance manual for the rapid survey of SFRB and to propose a series of frameworks for comprehensive analysis and assessment of SFRB. Precisely 372 SFRB in central Scotland and 202 SFRB in Southern Baden were investigated and characterized by 43 holistic variables. Based on this practical experience, a detailed guidance manual was created, guiding users to conduct a SFRB survey in a standardized and straightforward way. To explore the hidden data structure of data arising from the SFRB survey, various widely used machine learning algorithms and geo-statistical techniques were applied. For instance, cluster analysis showed intrinsic groupings of SFRB data, assisting with SFRB categorization. Principal Component Analysis (PCA) was applied to reduce the dimensions of SFRB data from the original 43 to 23, simplifying the SFRB system. Self-organizing Maps (SOM) visualized the relationships among variables and predicted certain variables as well as the types of SFRB by using the highly related variables. Three feature-selection techniques (Information Gain, Mutual Information and Relief) and four benchmark classifiers (Support Vector Machine, K-Nearest Neighbours, C4.5 Decision Tree and Naive Bayes) were used to select and verify the optimal subset of variables, respectively. Findings indicated that only nine important variables were required to accurately classify SFRB. Three popular multi-label classifiers (Multi-Label Support Vector Machine (MLSVM), Multi-Label K-Nearest Neighbour (MLKNN) and Back- Propagation for Multi-Label Learning (BP-MLL)) were applied to classify SFRB with multiple types. Experiments demonstrated that the classification framework achieved promising results and outperformed traditional single-label classifiers. Ordinary Kriging was used to estimate the spatial properties of the flood-related variables across the research area, while Disjunctive kriging was used to assess the probability of these individual variables exceeding specific management thresholds. The results provided decision makers with an effective tool for spatial planning of flood risk management. To assess dam failure hazards and risks of SFRB, a rapid screening tool was proposed based on expert judgement. It demonstrated that the levels of Dam Failure Hazard and Dam Failure Risk varied for different SFRB types and in different regions of central Scotland. In all, this thesis provided a guidance manual for rapid survey of SFRB and presented various effective, efficient and comprehensive frameworks for SFRB analysis and assessment, helping to promote the understanding and management of SFRB and thus to contribute to Flood Risk Management Plans in the context of the Flood Directive.

In this dissertation, a finitely convergent disjunctive programming procedure, the Convex Hull Tree (CHT) algorithm, is proposed to obtain the convex hull of a general mixed–integer linear program with bounded integer variables. The CHT algorithm constructs a linear program that has the same optimal solution as the associated mixed-integer linear program. The standard notion of sequential cutting planes is then combined with ideasunderlying the CHT algorithm to help guide the choice of disjunctions to use within a new cutting plane method, the Cutting Plane Tree (CPT) algorithm. We show that the CPT algorithm converges to an integer optimal solution of the general mixed-integer linear program with bounded integer variables in finitely many steps. We also enhance the CPT algorithm with several techniques including a “round-of-cuts” approach and an iterative method for solving the cut generation linear program (CGLP). Two normalization constraints are discussed in detail for solving the CGLP. For moderately sized instances, our study shows that the CPT algorithm provides significant gap closures with a pure cutting plane method.

When we interpret the input vector of a Boolean function as a binary number, we define interval Boolean function fn [a,b] so that fn [a,b](x) = 1 if and only if a ≤ x ≤ b. Disjunctive normal form is a common way of representing Boolean functions. Minimization of DNF representation of an interval Boolean function can be per- formed in linear time. The natural generalization to k-interval functions seems to be significantly harder to tackle. In this thesis, we discuss the difficulties with finding an optimal solution and introduce a 2k-approximation algorithm.

Este trabalho propõe a aplicação de dois diferentes conceitos para a resolução do Problema de Estivagem de Unidades de Celulose - PEUC, que de acordo com Ribeiro e Lorena (2008) pode ser definido como um problema que busca alocar a máxima quantidade de unidades de celulose ao porão de cargas de um dado navio, respeitando as restrições físicas de dimensões, de posicionamento, de não-sobreposição das unidades e de capacidade máxima do porão do navio. Esse tipo de problema se encaixa, no contexto da Pesquisa Operacional, na classe de Corte e Empacotamento (Cutting and Packing - C&P) e pode ser classificado, de acordo com a tipologia de Wäscher, Haußner e Schumann (2007), como sendo um Single Large Object Placement Problem (SLOPP). Em última instância, o objetivo do PEUC é definir o melhor plano de estivagem para o carregamento de unidades de celulose em um dado porão de um navio, maximizando a área ocupada pelas unidades de celulose. Trata-se de um problema NP-Completo (DOWSLAND; DOWSLAND, 1992; BISCHOFF; WÄSCHER, 1995; MALAGUTI; DURáN; TOTH, 2013) e por isso foram propostas duas abordagens para buscar a melhoria das soluções encontradas e/ou redução do tempo computacional necessário. As abordagens propostas, o Modelo Matemático Modificado e o Método Iterativo de Solução, apresentaram bons resultados para instâncias experimentais, confirmando a efetividade de suas aplicações. Os resultados foram melhores tanto na qualidade das soluções (ocupação total do objeto), como no tempo computacional necessário. Também foram avaliadas quatro instâncias reais, com a comparação dos planos de estivagem resultantes da aplicação dos modelos matemáticos com os planos reais, elaborados manualmente por especialistas. Em três dos quatro casos os resultados das abordagens aqui propostas se mostraram melhores que os planos reais. / This work proposes the application of two different concepts to tackle the Woodpulp Stowage Problem - WSP, that according to Ribeiro e Lorena (2008) can be defined as a problem that seeks the allocation of the maximum quantity of woodpulp units inside the hold of a cargo vessel, always respecting the physical constraints, positioning constraints, non-overlapping of units and also the hold capacity. This kind of problem fits, in the context of Operational Research, into the class of Cutting & Packing and can be classified, according to Wäscher, Haußner e Schumann (2007) typology, as a Single Larga Object Placement Problem (SLOPP). Ultimately the objective of the WSP is to define the best stowage plan for the loading of woodpulp units inside a given hold of a given cargo vessel, maximizing the total area occupied by the woodpulp units. As it\'s a NP-Complete problem (DOWSLAND; DOWSLAND, 1992; BISCHOFF; WÄSCHER, 1995; MALAGUTI; DURáN; TOTH, 2013) two approaches were proposed to improve the quality of the resulting solutions and/or the reduction of the computational time needed. The proposed approaches, the Modified Mathematical Model and the Iterative Solution Method, showed good results for experimental instances, confirming the effectiveness of these approaches. The results were better regarding the quality of the solutions (total occupied area of the object) and also regarding the computational time needed. Also, four real instances were evaluated, comparing the results of the mathematical models with the real stowage plans, manually created by specialists. In three of the four instances, the proposed approaches showed better results than the real stowage plans.

Magistriniame darbe ,,Formulių redukcija multiplikatyvioje aritmetikoje” nagrinėjamas sekvencinis multiplikatyvios aritmetikos variantas su lygybe. Šis skaičiavimas yra bazė, kuriant skaičiavimus,naudojamus automatizuojant įrodymus įvairiuose aritmetikos fragmentuose. Darbo tikslas- susipažinti su įrodymo teorija bei jos taikymu sekvenciniame multiplikatyviosios aritmetikos variante.Darbas padalintas į 3 skyrius : pargindinės sąvokos, pagalbinės lemos ir formulių redukcija. Pradžioje pateikiamas trumpas įvadas į Peano aritmetiką.Apibrėžiamas sekvencinis skaičiavimas K, turintis neloginių simbolių signatūrą {0,',P, *,=}.Savarankišką darbo dalį sudaro antrasis bei trečiasis skyriai.Bet kuriai bekvantorinei skaičiavimo K formulei A(x) randama tam tikros formos jai ekvivalenti normalioji disjunkcinė forma.Taip pat nagrinėjama sutvarkytųjų formulių redukcija. / In this postgraduate work “Reduction of formulas in the multiplicative arithmetic” the sequential variant with equality of multiplicative arithmetic is being analyzed. This calculus is a base when creating calculations which are used in different fragments of arithmetic. The aim of this work is to get acquainted with a proving theory and its application in sequential variant of multiplicative arithmetic. The work is divided into 3 sections: main conceptions, auxiliary lemmas and formula’s reduction. The short introduction into Pean’s arithmetic is given in the beginning. The sequential calculus K, which has non-logical symbol’s signature {0,`,P,.,=} is being described. Sections 2 and 3 are self-sufficient parts of this work. For any formula A(x) of calculation K the equivalent normal disjunctive form is found. Also the reduction of ordered formulas is analyzed.

The phenotype inference from genotype in RNA viruses maps the viral genome/protein sequences to the molecular functions in order to understand the underlying molecular mechanisms that are responsible for the function changes. The inference is currently done through a laborious experimental process which is arguably inefficient, incomplete, and unreliable. The wealth of RNA virus sequence data in the presence of different phenotypes promotes the rise of computational approaches to aid the inference. Key residue identification and genotype-phenotype mapping function learning are two approaches to identify the critical positions out of hitchhikers and elucidate the relations among them. The existing computational approaches in this area focus on prediction accuracy, yet a number of fundamental problems have not been considered: the scalability of the data, the capability to suggest informative biological experiments, and the interpretability of the inferences. A common scenario of inference done by biologists with mutagenesis experiments usually involves a small number of available sequences, which is very likely to be inadequate for the inference in most setups. Accordingly biologists desire models that are capable of inferring from such limited data, and algorithms that are capable of suggesting new experiments when more data is needed. Another important but always been neglected property of the models is the interpretability of the mapping, since most existing models behave as ’black boxes’. To address these issues, in the thesis I design a supervised combinatorial filtering algorithm that systematically and efficiently infers the correct set of key residue positions from available labeled data. For cases where more data is needed to fully converge to an answer, I introduce an active learning algorithm to help choose the most informative experiment from a set of unlabeled candidate strains or mutagenesis experiments to minimize the expected total laboratory time or financial cost. I also propose Disjunctive Normal Form (DNF) as an appropriate assumption over the hypothesis space to learn interpretable genotype-phenotype functions. The challenges of these approaches are the computational efficiency due to the combinatorial nature of our algorithms. The solution is to explore biological plausible assumptions to constrain the solution space and efficiently find the optimal solutions under the assumptions. The algorithms were validated in two ways: 1) prediction quality in a cross-validation manner, and 2) consistency with the domain experts’ conclusions. The algorithms also suggested new discoveries that have not been discussed yet. I applied these approaches to a variety of RNA virus datasets covering the majority of interesting RNA phenotypes, including drug resistance, Antigenicity shift, Antibody neutralization and so on to demonstrate the prediction power, and suggest new discoveries of Influenza drug resistance and Antigenicity. I also prove the extension of the approaches in the area of severe acute community disease.