Estimation of Parameters in Random Effect Models with Incidence Matrix Uncertainty

Random effect models have been widely applied in many fields of research. However, models with uncertain design matrices for random effects have been little investigated before. In some applications with such problems, an expectation method has been used for simplicity. This method does not include the extra information of uncertainty in the design matrix is not included. The closed solution for this problem is generally difficult to attain. We therefore propose an two-step algorithm for estimating the parameters, especially the variance components in the model. The implementation is based on Monte Carlo approximation and a Newton-Raphson-based EM algorithm. As an example, a simulated genetics dataset was analyzed. The results showed that the proportion of the total variance explained by the random effects was accurately estimated, which was highly underestimated by the expectation method. By introducing heuristic search and optimization methods, the algorithm can possibly be developed to infer the 'model-based' best design matrix and the corresponding best estimates.

Optimal Rehabilitation Strategy In Water Distribution Systems Considering Reduction In Greenhouse Gas Emissions

Most of water distribution systems (WDS) need rehabilitation due to aging infrastructure leading to decreasing capacity, increasing leakage and consequently low performance of the WDS. However an appropriate strategy including location and time of pipeline rehabilitation in a WDS with respect to a limited budget is the main challenge which has been addressed frequently by researchers and practitioners. On the other hand, selection of appropriate rehabilitation technique and material types is another main issue which has yet to address properly. The latter can affect the environmental impacts of a rehabilitation strategy meeting the challenges of global warming mitigation and consequent climate change. This paper presents a multi-objective optimization model for rehabilitation strategy in WDS addressing the abovementioned criteria mainly focused on greenhouse gas (GHG) emissions either directly from fossil fuel and electricity or indirectly from embodied energy of materials. Thus, the objective functions are to minimise: (1) the total cost of rehabilitation including capital and operational costs; (2) the leakage amount; (3) GHG emissions. The Pareto optimal front containing optimal solutions is determined using Non-dominated Sorting Genetic Algorithm NSGA-II. Decision variables in this optimisation problem are classified into a number of groups as: (1) percentage proportion of each rehabilitation technique each year; (2) material types of new pipeline for rehabilitation each year. Rehabilitation techniques used here includes replacement, rehabilitation and lining, cleaning, pipe duplication. The developed model is demonstrated through its application to a Mahalat WDS located in central part of Iran. The rehabilitation strategy is analysed for a 40 year planning horizon. A number of conventional techniques for selecting pipes for rehabilitation are analysed in this study. The results show that the optimal rehabilitation strategy considering GHG emissions is able to successfully save the total expenses, efficiently decrease the leakage amount from the WDS whilst meeting environmental criteria.

Essays on decision sciences: exploring cognition, information processing, and complexity

This thesis provides three original contributions to the field of Decision Sciences. The first contribution explores the field of heuristics and biases. New variations of the Cognitive Reflection Test (CRT--a test to measure "the ability or disposition to resist reporting the response that first comes to mind"), are provided. The original CRT (S. Frederick [2005] Journal of Economic Perspectives, v. 19:4, pp.24-42) has items in which the response is immediate--and erroneous. It is shown that by merely varying the numerical parameters of the problems, large deviations in response are found. Not only the final results are affected by the proposed variations, but so is processing fluency. It seems that numbers' magnitudes serve as a cue to activate system-2 type reasoning. The second contribution explores Managerial Algorithmics Theory (M. Moldoveanu [2009] Strategic Management Journal, v. 30, pp. 737-763); an ambitious research program that states that managers display cognitive choices with a "preference towards solving problems of low computational complexity". An empirical test of this hypothesis is conducted, with results showing that this premise is not supported. A number of problems are designed with the intent of testing the predictions from managerial algorithmics against the predictions of cognitive psychology. The results demonstrate (once again) that framing effects profoundly affect choice, and (an original insight) that managers are unable to distinguish computational complexity problem classes. The third contribution explores a new approach to a computationally complex problem in marketing: the shelf space allocation problem (M-H Yang [2001] European Journal of Operational Research, v. 131, pp.107--118). A new representation for a genetic algorithm is developed, and computational experiments demonstrate its feasibility as a practical solution method. These studies lie at the interface of psychology and economics (with bounded rationality and the heuristics and biases programme), psychology, strategy, and computational complexity, and heuristics for computationally hard problems in management science.

Busca heurística através de algoritmo genético e memético com construção de vocábulos para o problema de atribuição de localidades a anéis Sonet

Telecommunications play a key role in contemporary society. However, as new technologies are put into the market, it also grows the demanding for new products and services that depend on the offered infrastructure, making the problems of planning telecommunications networks, despite the advances in technology, increasingly larger and complex. However, many of these problems can be formulated as models of combinatorial optimization, and the use of heuristic algorithms can help solving these issues in the planning phase. In this project it was developed two pure metaheuristic implementations Genetic algorithm (GA) and Memetic Algorithm (MA) plus a third hybrid implementation Memetic Algorithm with Vocabulary Building (MA+VB) for a problem in telecommunications that is known in the literature as Problem SONET Ring Assignment Problem or SRAP. The SRAP arises during the planning stage of the physical network and it consists in the selection of connections between a number of locations (customers) in order to meet a series of restrictions on the lowest possible cost. This problem is NP-hard, so efficient exact algorithms (in polynomial complexity ) are not known and may, indeed, even exist

Análise comparativa do desempenho de um Controlador Fuzzy acoplado a um PID Neural sintonizado por um Algoritmo Genético com Controladores Inteligentes Convencionais

On this paper, it is made a comparative analysis among a controller fuzzy coupled to a PID neural adjusted by an AGwith several traditional control techniques, all of them applied in a system of tanks (I model of 2nd order non lineal). With the objective of making possible the techniques involved in the comparative analysis and to validate the control to be compared, simulations were accomplished of some control techniques (conventional PID adjusted by GA, Neural PID (PIDN) adjusted by GA, Fuzzy PI, two Fuzzy attached to a PID Neural adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA) to have some comparative effects with the considered controller. After doing, all the tests, some control structures were elected from all the tested techniques on the simulating stage (conventional PID adjusted by GA, Fuzzy PI, two Fuzzy attached to a PIDN adjusted by GA and Fuzzy MISO (3 inputs) attached to a PIDN adjusted by GA), to be implemented at the real system of tanks. These two kinds of operation, both the simulated and the real, were very important to achieve a solid basement in order to establish the comparisons and the possible validations show by the results

Uma implementação paralela híbrida para o problema do caixeiro viajante usando algoritmos genéticos, GRASP e aprendizagem por reforço

The metaheuristics techiniques are known to solve optimization problems classified as NP-complete and are successful in obtaining good quality solutions. They use non-deterministic approaches to generate solutions that are close to the optimal, without the guarantee of finding the global optimum. Motivated by the difficulties in the resolution of these problems, this work proposes the development of parallel hybrid methods using the reinforcement learning, the metaheuristics GRASP and Genetic Algorithms. With the use of these techniques, we aim to contribute to improved efficiency in obtaining efficient solutions. In this case, instead of using the Q-learning algorithm by reinforcement learning, just as a technique for generating the initial solutions of metaheuristics, we use it in a cooperative and competitive approach with the Genetic Algorithm and GRASP, in an parallel implementation. In this context, was possible to verify that the implementations in this study showed satisfactory results, in both strategies, that is, in cooperation and competition between them and the cooperation and competition between groups. In some instances were found the global optimum, in others theses implementations reach close to it. In this sense was an analyze of the performance for this proposed approach was done and it shows a good performance on the requeriments that prove the efficiency and speedup (gain in speed with the parallel processing) of the implementations performed

Algoritmos genéticos: uso de lógica nebulosa e análise de convergência por cadeia de Markov

In this work, the Markov chain will be the tool used in the modeling and analysis of convergence of the genetic algorithm, both the standard version as for the other versions that allows the genetic algorithm. In addition, we intend to compare the performance of the standard version with the fuzzy version, believing that this version gives the genetic algorithm a great ability to find a global optimum, own the global optimization algorithms. The choice of this algorithm is due to the fact that it has become, over the past thirty yares, one of the more importan tool used to find a solution of de optimization problem. This choice is due to its effectiveness in finding a good quality solution to the problem, considering that the knowledge of a good quality solution becomes acceptable given that there may not be another algorithm able to get the optimal solution for many of these problems. However, this algorithm can be set, taking into account, that it is not only dependent on how the problem is represented as but also some of the operators are defined, to the standard version of this, when the parameters are kept fixed, to their versions with variables parameters. Therefore to achieve good performance with the aforementioned algorithm is necessary that it has an adequate criterion in the choice of its parameters, especially the rate of mutation and crossover rate or even the size of the population. It is important to remember that those implementations in which parameters are kept fixed throughout the execution, the modeling algorithm by Markov chain results in a homogeneous chain and when it allows the variation of parameters during the execution, the Markov chain that models becomes be non - homogeneous. Therefore, in an attempt to improve the algorithm performance, few studies have tried to make the setting of the parameters through strategies that capture the intrinsic characteristics of the problem. These characteristics are extracted from the present state of execution, in order to identify and preserve a pattern related to a solution of good quality and at the same time that standard discarding of low quality. Strategies for feature extraction can either use precise techniques as fuzzy techniques, in the latter case being made through a fuzzy controller. A Markov chain is used for modeling and convergence analysis of the algorithm, both in its standard version as for the other. In order to evaluate the performance of a non-homogeneous algorithm tests will be applied to compare the standard fuzzy algorithm with the genetic algorithm, and the rate of change adjusted by a fuzzy controller. To do so, pick up optimization problems whose number of solutions varies exponentially with the number of variables