Stochastic combinatorial optimization problems are combinatorial optimization problems where part of the problem data are probabilistic. The focus of this thesis is on stochastic routing problems, a class of stochastic combinatorial optimization problems that arise in distribution management. Stochastic routing problems involve finding the best solution to distribute goods across a logistic network. In the problems we tackle, we consider a setting in which the cost of a solution is described by a random variable; the goal is to find the solution that minimizes the expected cost. Solving such stochastic routing problems is a challenging task because of two main factors. First, the number of possible solutions grows exponentially with the instance size. Second, computing the expected cost of a solution is computationally very expensive. <br> To tackle stochastic routing problems, stochastic local search algorithms such as iterative improvement algorithms and metaheuristics are quite promising because they offer effective strategies to tackle the combinatorial nature of these problems. However, a crucial factor that determines the success of these algorithms in stochastic settings is the trade-off between the computation time needed to search for high quality solutions in a large search space and the computation time spent in computing the expected cost of solutions obtained during the search. <br> To compute the expected cost of solutions in stochastic routing problems, two classes of approaches have been proposed in the literature: analytical computation and empirical estimation. The former exactly computes the expected cost using closed-form expressions; the latter estimates the expected cost through Monte Carlo simulation. <br> Many previously proposed metaheuristics for stochastic routing problems use the analytical computation approach. However, in a large number of practical stochastic routing problems, due to the presence of complex constraints, the use of the analytical computation approach is difficult, time consuming or even impossible. Even for the prototypical stochastic routing problems that we consider in this thesis, the adoption of the analytical computation approach is computationally expensive. Notwithstanding the fact that the empirical estimation approach can address the issues posed by the analytical computation approach, its adoption in metaheuristics to tackle stochastic routing problems has never been thoroughly investigated. <br> In this thesis, we study two classical stochastic routing problems: the probabilistic traveling salesman problem (PTSP) and the vehicle routing problem with stochastic demands and customers (VRPSDC). The goal of the thesis is to design, implement, and analyze effective metaheuristics that use the empirical estimation approach to tackle these two problems. The main results of this thesis are: 1) The empirical estimation approach is a viable alternative to the widely-adopted analytical computation approach for the PTSP and the VRPSDC; 2) A principled adoption of the empirical estimation approach in metaheuristics results in high performing algorithms for tackling the PTSP and the VRPSDC. The estimation-based metaheuristics developed in this thesis for these two problems define the new state-of-the-art.

Measuring Biodiversity is an important phenomenon to serve best to our ecology and also keep environment sound. Variety of life on different levels, like an ecosystem, life forms on a site, landscape collectively known as Biodiversity. Species richness and evenness combine measures as Biodiversity. Separate formula, index, equation are widely using to measure Biodiversity in each level. Swedish Environmental Protection Agency aimed to establish an index that consists of landscape functionality and landscape heterogeneity. For landscape functionality assessment, there BBCI (Biotope biodiversity Capacity index) is going to use. High BBCI indicates a high biodiversity for each biotope. However, empirically estimate species richness how much matched with BBCI that not been evaluated. The aim of this paper to see the relationship between empirical estimated Biodiversity and BBCI. A relationship between Shannon diversity index and BBCI also ran to see the matches between them. Collect the empirical data from selected 15 landscapes using Artportalen.se and sort the data for further calculation. Results showed that there was a strong positive relationship between empirical estimated Biodiversity and BBCI. Again Shannon diversity index and BBCI also demonstrated a positive correlation between them. It showed BBCI could explain 60%-69% of species richness data and 17%-22% of Shannon diversity index. It indicates the acceptance of theoretical study of measure Biodiversity.

Title: Numerical study on simultanious equations Author: Vojtěch Šaroch Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Petr Lachout, CSc. Abstract: In this thesis we deal with the simultaneous equation model. In the first chapter we introduce theoretical aspect of this problem, especially estimation procedures and their properties. We mention issues of an identification and an inconsistency of OLS-estimates for simultaneous modeling. In th second chapter we introduce theory of estimation, especially we will focus on the interval estimation and precision. We mention empirical approach too. In the third chapter we perform a numerical study on the simple macroeconomic model of generated dates. We are interested in properties of interval estimations of parameters, the convergence rate, difference between the empirical and theoretical extimation etc. Keywords: simultaneous equations model, interval estimation, empirical estimation 1

Title: Numerical study on simultanious equations Author: Vojtěch Šaroch Department: Department of Probability and Mathematical Statistics Supervisor: doc. RNDr. Petr Lachout, CSc. Abstract: In this thesis we deal with simultaneous equation model. In first chapter we introduce to theoretical aspect of this problem, especially estimation procedures and their properties. We mention issues of identification and an inconsistency of OLS-estimates for the simultaneous modeling. In second chapter we introduce theory of estimation, especially we will focus on interval estimation and precision. We mention empirical approach too. In the third chapter we perform a numerical study on simple macroeconomic model on generated dates. We are interested in properties interval estimations of parameters, the convergence rate, difference between empirical and theoretical extimation etc. Keywords: simultaneous equations model, interval estimation, empirical estimation 1

The aim of this paper is to discuss the outcomes of neoliberal globalization from the perspective of labor in the developing countries, with a particular emphasis on the crises that followed the substantial liberalization in capital accounts in the 1990s. Although a lot has been said about the effects of capital account liberalization on the macroeconomic performance of the economies, less attention is paid to the different effects on labor vs. capital. This paper analyses the outcomes of neoliberal globalization for labor in nine developing countries, and focuses on the episodes of crisis as part of the general class struggle where the question on who will carry the burden of adjustment is a part of the struggle. The paper describes the corner stones of the regime of growth in the neoliberal era, by analyzing the trends in growth, investment, unemployment, and labor's share in income, and discusses the effects of the shocks generated by crises on these variables. We empirically test whether the lower wage share has had any effect on unemployment, as the neoclassical theory claims, or whether unemployment is primarily driven by the goods market conditions a la Keynes. An empirical analysis about the cyclical behaviour of labor's share is carried on to understand whether the crises episodes change the effect of demand on distribution. Since the source of growth can also be important on how the generated output is distributed, we also discuss the effects of investment performance on labor's share. Then we proceed with an analysis of the specific consequences of economic policy choices on distribution, in terms of exchange rate and fiscal policies. Finally we discuss the core stones of an alternative policy framework. (author's abstract) / Series: Working Papers Series "Growth and Employment in Europe: Sustainability and Competitiveness"

Stochastic combinatorial optimization problems are combinatorial optimization problems where part of the problem data are probabilistic. The focus of this thesis is on stochastic routing problems, a class of stochastic combinatorial optimization problems that arise in distribution management. Stochastic routing problems involve finding the best solution to distribute goods across a logistic network. In the problems we tackle, we consider a setting in which the cost of a solution is described by a random variable; the goal is to find the solution that minimizes the expected cost. Solving such stochastic routing problems is a challenging task because of two main factors. First, the number of possible solutions grows exponentially with the instance size. Second, computing the expected cost of a solution is computationally very expensive. <p><br><p>To tackle stochastic routing problems, stochastic local search algorithms such as iterative improvement algorithms and metaheuristics are quite promising because they offer effective strategies to tackle the combinatorial nature of these problems. However, a crucial factor that determines the success of these algorithms in stochastic settings is the trade-off between the computation time needed to search for high quality solutions in a large search space and the computation time spent in computing the expected cost of solutions obtained during the search. <p><br><p>To compute the expected cost of solutions in stochastic routing problems, two classes of approaches have been proposed in the literature: analytical computation and empirical estimation. The former exactly computes the expected cost using closed-form expressions; the latter estimates the expected cost through Monte Carlo simulation.<p><br><p>Many previously proposed metaheuristics for stochastic routing problems use the analytical computation approach. However, in a large number of practical stochastic routing problems, due to the presence of complex constraints, the use of the analytical computation approach is difficult, time consuming or even impossible. Even for the prototypical stochastic routing problems that we consider in this thesis, the adoption of the analytical computation approach is computationally expensive. Notwithstanding the fact that the empirical estimation approach can address the issues posed by the analytical computation approach, its adoption in metaheuristics to tackle stochastic routing problems has never been thoroughly investigated. <p><br><p>In this thesis, we study two classical stochastic routing problems: the probabilistic traveling salesman problem (PTSP) and the vehicle routing problem with stochastic demands and customers (VRPSDC). The goal of the thesis is to design, implement, and analyze effective metaheuristics that use the empirical estimation approach to tackle these two problems. The main results of this thesis are: <p>1) The empirical estimation approach is a viable alternative to the widely-adopted analytical computation approach for the PTSP and the VRPSDC; <p>2) A principled adoption of the empirical estimation approach in metaheuristics results in high performing algorithms for tackling the PTSP and the VRPSDC. The estimation-based metaheuristics developed in this thesis for these two problems define the new state-of-the-art. / Doctorat en Sciences de l'ingénieur / info:eu-repo/semantics/nonPublished