Optimal power management strategy for energy storage with stochastic loads

In this paper, a power management strategy (PMS) has been developed for the control of energy storage in a system subjected to loads of random duration. The PMS minimises the costs associated with the energy consumption of specific systems powered by a primary energy source and equipped with energy storage, under the assumption that the statistical distribution of load durations is known. By including the variability of the load in the cost function, it was possible to define the optimality criteria for the power flow of the storage. Numerical calculations have been performed obtaining the control strategies associated with the global minimum in energy costs, for a wide range of initial conditions of the system. The results of the calculations have been tested on a MATLAB/Simulink model of a rubber tyre gantry (RTG) crane equipped with a flywheel energy storage system (FESS) and subjected to a test cycle, which corresponds to the real operation of a crane in the Port of Felixstowe. The results of the model show increased energy savings and reduced peak power demand with respect to existing control strategies, indicating considerable potential savings for port operators in terms of energy and maintenance costs.

Algorithms for two-dimensional cutting stock and strip packing problems using dynamic programming and column generation

We investigate several two-dimensional guillotine cutting stock problems and their variants in which orthogonal rotations are allowed. We first present two dynamic programming based algorithms for the Rectangular Knapsack (RK) problem and its variants in which the patterns must be staged. The first algorithm solves the recurrence formula proposed by Beasley; the second algorithm - for staged patterns - also uses a recurrence formula. We show that if the items are not so small compared to the dimensions of the bin, then these algorithms require polynomial time. Using these algorithms we solved all instances of the RK problem found at the OR-LIBRARY, including one for which no optimal solution was known. We also consider the Two-dimensional Cutting Stock problem. We present a column generation based algorithm for this problem that uses the first algorithm above mentioned to generate the columns. We propose two strategies to tackle the residual instances. We also investigate a variant of this problem where the bins have different sizes. At last, we study the Two-dimensional Strip Packing problem. We also present a column generation based algorithm for this problem that uses the second algorithm above mentioned where staged patterns are imposed. In this case we solve instances for two-, three- and four-staged patterns. We report on some computational experiments with the various algorithms we propose in this paper. The results indicate that these algorithms seem to be suitable for solving real-world instances. We give a detailed description (a pseudo-code) of all the algorithms presented here, so that the reader may easily implement these algorithms. (c) 2007 Elsevier B.V. All rights reserved.

A stopping rule while searching for optimal solution of facility-location

Solutions to combinatorial optimization, such as p-median problems of locating facilities, frequently rely on heuristics to minimize the objective function. The minimum is sought iteratively and a criterion is needed to decide when the procedure (almost) attains it. However, pre-setting the number of iterations dominates in OR applications, which implies that the quality of the solution cannot be ascertained. A small branch of the literature suggests using statistical principles to estimate the minimum and use the estimate for either stopping or evaluating the quality of the solution. In this paper we use test-problems taken from Baesley's OR-library and apply Simulated Annealing on these p-median problems. We do this for the purpose of comparing suggested methods of minimum estimation and, eventually, provide a recommendation for practioners. An illustration ends the paper being a problem of locating some 70 distribution centers of the Swedish Post in a region.

Optimization heuristic solutions, how good can they be? : With empirical applications in location problems

Combinatorial optimization problems, are one of the most important types of problems in operational research. Heuristic and metaheuristics algorithms are widely applied to find a good solution. However, a common problem is that these algorithms do not guarantee that the solution will coincide with the optimum and, hence, many solutions to real world OR-problems are afflicted with an uncertainty about the quality of the solution. The main aim of this thesis is to investigate the usability of statistical bounds to evaluate the quality of heuristic solutions applied to large combinatorial problems. The contributions of this thesis are both methodological and empirical. From a methodological point of view, the usefulness of statistical bounds on p-median problems is thoroughly investigated. The statistical bounds have good performance in providing informative quality assessment under appropriate parameter settings. Also, they outperform the commonly used Lagrangian bounds. It is demonstrated that the statistical bounds are shown to be comparable with the deterministic bounds in quadratic assignment problems. As to empirical research, environment pollution has become a worldwide problem, and transportation can cause a great amount of pollution. A new method for calculating and comparing the CO2-emissions of online and brick-and-mortar retailing is proposed. It leads to the conclusion that online retailing has significantly lesser CO2-emissions. Another problem is that the Swedish regional division is under revision and the border effect to public service accessibility is concerned of both residents and politicians. After analysis, it is shown that borders hinder the optimal location of public services and consequently the highest achievable economic and social utility may not be attained.

How does the use of different road networks effect the optimal location of facilities in rural areas?

The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. Distance measure can vary if the accuracy of the road network varies. The rst aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to nd an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using dierent algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with an asymmetrically distributed population, Dalecarlia. The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is optimized for. It is also shown that the simulated annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better location solutions.

Effective measures to decrease air contaminants through risk and control visualization : a study of the effective use of QR codes to facilitate safety training

Woodworking industries still consists of wood dust problems. Young workers are especially vulnerable to safety risks. To reduce risks, it is important to change attitudes and increase knowledge about safety. Safety training have shown to establish positive attitudes towards safety among employees. The aim of current study is to analyze the effect of QR codes that link to Picture Mix EXposure (PIMEX) videos by analyzing attitudes to this safety training method and safety in student responses. Safety training videos were used in upper secondary school handicraft programs to demonstrate wood dust risks and methods to decrease exposure to wood dust. A preliminary study was conducted to investigate improvement of safety training in two schools in preparation for the main study that investigated a safety training method in three schools. In the preliminary study the PIMEX method was first used in which students were filmed while wood dust exposure was measured and subsequently displayed on a computer screen in real time. Before and after the filming, teachers, students, and researchers together analyzed wood dust risks and effective measures to reduce exposure to them. For the main study, QR codes linked to PIMEX videos were attached at wood processing machines. Subsequent interviews showed that this safety training method enables students in an early stage of their life to learn about risks and safety measures to control wood dust exposure. The new combination of methods can create awareness, change attitudes and motivation among students to work more frequently to reduce wood dust. 

Model Prediction Control For Water Management Using Adaptive Prediction Accuracy

In the field of operational water management, Model Predictive Control (MPC) has gained popularity owing to its versatility and flexibility. The MPC controller, which takes predictions, time delay and uncertainties into account, can be designed for multi-objective management problems and for large-scale systems. Nonetheless, a critical obstacle, which needs to be overcome in MPC, is the large computational burden when a large-scale system is considered or a long prediction horizon is involved. In order to solve this problem, we use an adaptive prediction accuracy (APA) approach that can reduce the computational burden almost by half. The proposed MPC scheme with this scheme is tested on the northern Dutch water system, which comprises Lake IJssel, Lake Marker, the River IJssel and the North Sea Canal. The simulation results show that by using the MPC-APA scheme, the computational time can be reduced to a large extent and a flood protection problem over longer prediction horizons can be well solved.

Essays on asymmetric information problems without the single-crossing property: applications to corporate finance

The objective of this dissertation is to re-examine classical issues in corporate finance, applying a new analytical tool. The single-crossing property, also called Spence-irrlees condition, is not required in the models developed here. This property has been a standard assumption in adverse selection and signaling models developed so far. The classical papers by Guesnerie and Laffont (1984) and Riley (1979) assume it. In the simplest case, for a consumer with a privately known taste, the single-crossing property states that the marginal utility of a good is monotone with respect to the taste. This assumption has an important consequence to the result of the model: the relationship between the private parameter and the quantity of the good assigned to the agent is monotone. While single crossing is a reasonable property for the utility of an ordinary consumer, this property is frequently absent in the objective function of the agents for more elaborate models. The lack of a characterization for the non-single crossing context has hindered the exploration of models that generate objective functions without this property. The first work that characterizes the optimal contract without the single-crossing property is Araújo and Moreira (2001a) and, for the competitive case, Araújo and Moreira (2001b). The main implication is that a partial separation of types may be observed. Two sets of disconnected types of agents may choose the same contract, in adverse selection problems, or signal with the same levei of signal, in signaling models.

Courts as healthcare policy-makers: the problem, the responses to the problem and problems in the responses

This article starts by analysing healthcare litigation in Brazil by means of a literature review of articles that contribute with empirical findings on this phenomenon. Based on this review, I argue that health care litigation in Brazil makes the public health system less fair and rational. In the second part of this article, I discuss the three most overarching responses to control the level of litigation and its impact on the public health system: (i) the public hearing held by the Supreme Federal Court and the criteria the court established thereafter; (ii) the recommendations by the National Council of Justice aimed at building courts’ institutional capacity; and (iii) the enactment of the Federal Law 12.401/11, which created a new health technology assessment system. I argue that latter is the best response because it keeps the substantive decisions on the allocation of healthcare resources in the institution that is in the best position to make them. Moreover, this legislation will make the decisions about provision of health treatments more explicit, making easier for courts to control the procedure and the reasons for these decisions.

Multiple large shareholders in corporate governance

Large shareholders of firms with majority bIocks are often at the heIm of their companies and do not necessarily have the same interests as minority shareholders. We show that bargaining problems Ied by the presence of muItipIe controlling shareholders protect minority shareholders. The same bargaining problems, however, prevent efficient decisions. By solving this trade-off we find that i) muItipIe controlling shareholders should be present in firms with Iarge costs of diIuting minority shareholders and in firms with Iarge financing requirements, ii) an optimal ownership structure requires the presence of a dass of shareholders - the minority shareholders - with no control over corporate decisions. Evidence on the ownership structure of dose corporations in the V.S. is consistent with our model.

Non-asymptotic confidence bounds for the optimal value of a stochastic program

We discuss a general approach to building non-asymptotic confidence bounds for stochastic optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper and lower bounds for the optimal value of the problem which are essentially better than the quality of the corresponding optimal solutions. At the same time, such bounds are more reliable than “standard” confidence bounds obtained through the asymptotic approach. We also discuss bounding the optimal value of MinMax Stochastic Optimization and stochastically constrained problems. We conclude with a small simulation study illustrating the numerical behavior of the proposed bounds.

Limited liability and non-responsiveness in moral hazard and adverse selection problems

This work analyses the optimal menu of contracts offered by a risk neutral principal to a risk averse agent under moral hazard, adverse selection and limited liability. There are two output levels, whose probability of occurrence are given by agent’s private information choice of effort. The agent’s cost of effort is also private information. First, we show that without assumptions on the cost function, it is not possible to guarantee that the optimal contract menu is simple, when the agent is strictly risk averse. Then, we provide sufficient conditions over the cost function under which it is optimal to offer a single contract, independently of agent’s risk aversion. Our full-pooling cases are caused by non-responsiveness, which is induced by the high cost of enforcing higher effort levels. Also, we show that limited liability generates non-responsiveness.

The combination of neural estimates in prediction and decision problems

In this dissertation, different ways of combining neural predictive models or neural-based forecasts are discussed. The proposed approaches consider mostly Gaussian radial basis function networks, which can be efficiently identified and estimated through recursive/adaptive methods. Two different ways of combining are explored to get a final estimate – model mixing and model synthesis –, with the aim of obtaining improvements both in terms of efficiency and effectiveness. In the context of model mixing, the usual framework for linearly combining estimates from different models is extended, to deal with the case where the forecast errors from those models are correlated. In the context of model synthesis, and to address the problems raised by heavily nonstationary time series, we propose hybrid dynamic models for more advanced time series forecasting, composed of a dynamic trend regressive model (or, even, a dynamic harmonic regressive model), and a Gaussian radial basis function network. Additionally, using the model mixing procedure, two approaches for decision-making from forecasting models are discussed and compared: either inferring decisions from combined predictive estimates, or combining prescriptive solutions derived from different forecasting models. Finally, the application of some of the models and methods proposed previously is illustrated with two case studies, based on time series from finance and from tourism.

Effect of Fusarium graminearum and infection index on germination and vigor of maize seeds

Patógenos em sementes de milho (Zea mays) causam sérios problemas, como a perda de sua capacidade germinativa. O objetivo do trabalho foi determinar qual o melhor tempo para infecção das sementes de milho com Fusarium graminearum, para posterior avaliação dos danos causados pelo fungo na germinação e vigor das mesmas. As sementes foram colocadas sobre meio de BDA contendo o patógeno e incubadas por 4, 8, 16 e 32 h. Após os respectivos períodos de incubação, estas foram submetidas ao teste de sanidade (papel de filtro), com duas variações, sem e com assepsia superficial, usando hipoclorito de sódio a 1% de cloro ativo, por 3 min. Determinado o melhor tempo para infecção, outras sementes foram infetadas com o patógeno, para realização dos testes de germinação e vigor (envelhecimento acelerado e teste de frio) com uma mistura de sementes sadias (colocadas sobre o meio BDA) e sementes inoculadas, resultando em 0, 20, 40, 60, 80 e 100% de sementes infetadas com o fungo em estudo. Os resultados obtidos mostraram que o período de incubação de 32 h foi suficiente para se obter sementes infetadas. Com relação à germinação, não houve diferenças significativas entre os diferentes níveis de infecção, provavelmente devido ao alto vigor das sementes de milho testadas. Quanto aos testes de vigor, os níveis de infecção diferiram significativamente da testemunha, apesar de não terem diferido entre si.

Diversidad genética de aislamientos de Bacillus subtilis con potencial para el control biológico de Colletotrichum acutatum y Guignardia citricarpa

O setor citrícola enfrenta sérios problemas representados por doenças de flores e frutos jovens que, além de diminuir a produtividade, depreciam os frutos pelo aspecto que conferem aos mesmos. Tais doenças são representadas, principalmente, pela mancha preta dos frutos cítricos (MPC) e pela queda prematura dos frutos cítricos (QPFC), onde a medida predominante de controle é a pulverização com produtos químicos. Entretanto, os custos financeiros e ambientais de aplicações com tais produtos, aliado às crescentes restrições à presença de resíduos, estão a exigir o estudo de novas alternativas. Entre estas, o controle biológico surge como alternativa importante. Sabendo-se que, o conhecimento da biodiversidade dos seres vivos é importante para determinação de suas funções potenciais, o presente trabalho teve por objetivo estudar a diversidade genética, através de marcadores moleculares AFLP, de 32 isolados de B. subtilis com a finalidade de se encontrar, dentre os mesmos, um (ou mais isolados) que apresentasse maior similaridade com o isolado ACB-69, o qual apresenta potencial para o controle da doença. Diante disso, os resultados obtidos neste trabalho, permitiram concluir que: a) os isolados de B. subtilis estudados agruparam-se no filograma de distância genética, independente da procedência ou do hospedeiro; b) os isolados ACB-69 e ACB-83, com potenciais para o controle da queda prematura dos frutos cítricos, compartilham da mesma ancestralidade, o que pode ser inferido pela metodologia aplicada; c) em termos biológicos; o isolado ACB-83 merece mais estudos quanto à viabilidade de controle de doenças de citros, como a queda prematura dos frutos cítricos e a manha preta dos frutos cítricos, sob condições de campo.