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Este trabalho é focado num problema clássico das Ciências e Engenharia, que consiste em cobrir um objeto por esferas de mesmo raio, a ser minimizado. A abordagem prática usual conta com sérias desvantagens. Logo, faz-se necessário trabalhar com isto de forma diferenciada. A técnica proposta aqui envolve a utilização de resultados célebres da geometria algébrica real, que tem como peça central o positivstellensatz de Stengle e, fazendo a devida relação entre esses resultados e otimização com restrições envolvendo representações naturais por somas de quadrados, é possível reduzir o problema original a um de programação semidefinida não linear. Mas, por contar com particularidades que favorecem a aplicação do paradigma de restauração inexata, esta foi a técnica utilizada para resolvê-lo. A versatilidade da técnica e a possibilidade de generalização direta dos objetos envolvidos destacam-se como grandes vantagens desta abordagem, além da visão algébrica inovadora do problema. / This work is focused on a classic problem from Engineering. Basically, it consists of finding the optimal positioning and radius of a set of equal spheres in order to cover a given object. The common approach to this carries some substantial disadvantages, what makes it necessary to nd a dierent way. Here, we explore some renowned results from real algebraic geometry, which has Stengle\'s positivstellensatz as one of its central pieces, and SOS optimization. Once the proper link is made, the original problem can be reduced to a nonlinear semidenite programming one, which has peculiarities that favours the application of an inexact restoration paradigm. We point out the algebraic view and the no use of discretizations as great advantages of this approach, besides the notable versatility and easy generalization in terms of dimension and involved objects.

Este trabalho é focado num problema clássico das Ciências e Engenharia, que consiste em cobrir um objeto por esferas de mesmo raio, a ser minimizado. A abordagem prática usual conta com sérias desvantagens. Logo, faz-se necessário trabalhar com isto de forma diferenciada. A técnica proposta aqui envolve a utilização de resultados célebres da geometria algébrica real, que tem como peça central o positivstellensatz de Stengle e, fazendo a devida relação entre esses resultados e otimização com restrições envolvendo representações naturais por somas de quadrados, é possível reduzir o problema original a um de programação semidefinida não linear. Mas, por contar com particularidades que favorecem a aplicação do paradigma de restauração inexata, esta foi a técnica utilizada para resolvê-lo. A versatilidade da técnica e a possibilidade de generalização direta dos objetos envolvidos destacam-se como grandes vantagens desta abordagem, além da visão algébrica inovadora do problema. / This work is focused on a classic problem from Engineering. Basically, it consists of finding the optimal positioning and radius of a set of equal spheres in order to cover a given object. The common approach to this carries some substantial disadvantages, what makes it necessary to nd a dierent way. Here, we explore some renowned results from real algebraic geometry, which has Stengle\'s positivstellensatz as one of its central pieces, and SOS optimization. Once the proper link is made, the original problem can be reduced to a nonlinear semidenite programming one, which has peculiarities that favours the application of an inexact restoration paradigm. We point out the algebraic view and the no use of discretizations as great advantages of this approach, besides the notable versatility and easy generalization in terms of dimension and involved objects.

Covering arrays with row limit, CARLs, are a new family of combinatorial objects which we introduce as a generalization of group divisible designs and covering arrays. In the same manner as their predecessors, CARLs have a natural application as combinatorial models for interaction test suites. A CARL(N;t,k,v:w), is an N×k array with some empty cells. A component, which is represented by a column, takes values from a v-set called the alphabet. In each row, there are exactly w non-empty cells, that is the corresponding components have an assigned value from the alphabet. The parameter w is called the row limit. Moreover, any N×t subarray contains every of v^t distinct t-tuples of alphabet symbols at least once. This thesis is concerned with the bounds on the size and with the construction of CARLs when the row limit w(k) is a positive integer valued function of the number of columns, k. Here we give a lower bound, and probabilistic and algorithmic upper bounds for any CARL. Further, we find improvements on the upper bounds when w(k)ln(w(k)) = o(k) and when w(k) is a constant function. We also determine the asymptotic size of CARLs when w(k) = Θ(k) and when w(k) is constant. Next, we study constructions of CARLs. We provide two combinatorial constructions of CARLs, which we apply to construct families of CARLs with w(k)=ck, where c<1. Also, we construct optimal CARLs when t=2 and w=4, and prove that there exists a constant δ, such that for any v and k≥4, an optimal CARL(2,k,v:4) differs from the lower bound by at most δ rows, with some possible exceptions. Finally, we define a packing array with row limit, PARL(N;t,k,v:w), in the same way as a CARL(N;t,k,v:w) with the difference that any t-tuple is contained at most once in any N×t subarray. We find that when w(k) is a constant function, the results on the asymptotic size of CARLs imply the results on the asymptotic size of PARLs. Also, when t=2, we consider a transformation of optimal CARLs with row limit w=3 to optimal PARLs with w=3.

Covering arrays with row limit, CARLs, are a new family of combinatorial objects which we introduce as a generalization of group divisible designs and covering arrays. In the same manner as their predecessors, CARLs have a natural application as combinatorial models for interaction test suites. A CARL(N;t,k,v:w), is an N×k array with some empty cells. A component, which is represented by a column, takes values from a v-set called the alphabet. In each row, there are exactly w non-empty cells, that is the corresponding components have an assigned value from the alphabet. The parameter w is called the row limit. Moreover, any N×t subarray contains every of v^t distinct t-tuples of alphabet symbols at least once. This thesis is concerned with the bounds on the size and with the construction of CARLs when the row limit w(k) is a positive integer valued function of the number of columns, k. Here we give a lower bound, and probabilistic and algorithmic upper bounds for any CARL. Further, we find improvements on the upper bounds when w(k)ln(w(k)) = o(k) and when w(k) is a constant function. We also determine the asymptotic size of CARLs when w(k) = Θ(k) and when w(k) is constant. Next, we study constructions of CARLs. We provide two combinatorial constructions of CARLs, which we apply to construct families of CARLs with w(k)=ck, where c<1. Also, we construct optimal CARLs when t=2 and w=4, and prove that there exists a constant δ, such that for any v and k≥4, an optimal CARL(2,k,v:4) differs from the lower bound by at most δ rows, with some possible exceptions. Finally, we define a packing array with row limit, PARL(N;t,k,v:w), in the same way as a CARL(N;t,k,v:w) with the difference that any t-tuple is contained at most once in any N×t subarray. We find that when w(k) is a constant function, the results on the asymptotic size of CARLs imply the results on the asymptotic size of PARLs. Also, when t=2, we consider a transformation of optimal CARLs with row limit w=3 to optimal PARLs with w=3.

This dissertation studies optimization problems that involve probabilistic covering constraints. A probabilistic constraint evaluates and requires that the probability that a set of constraints involving random coefficients with known distributions hold satisfy a minimum requirement. A covering constraint involves a linear inequality on non-negative variables with a greater or equal to sign and non-negative coefficients. A variety of applications, such as set cover problems, node/edge cover problems, crew scheduling, production planning, facility location, and machine learning, in uncertain settings involve probabilistic covering constraints. In the first part of this dissertation we consider probabilistic covering linear programs. Using the sampling average approximation (SAA) framework, a probabilistic covering linear program can be approximated by a covering k-violation linear program (CKVLP), a deterministic covering linear program in which at most k constraints are allowed to be violated. We show that CKVLP is strongly NP-hard. Then, to improve the performance of standard mixed-integer programming (MIP) based schemes for CKVLP, we (i) introduce and analyze a coefficient strengthening scheme, (ii) adapt and analyze an existing cutting plane technique, and (iii) present a branching technique. Through computational experiments, we empirically verify that these techniques are significantly effective in improving solution times over the CPLEX MIP solver. In particular, we observe that the proposed schemes can cut down solution times from as much as six days to under four hours in some instances. We also developed valid inequalities arising from two subsets of the constraints in the original formulation. When incorporating them with a modified coefficient strengthening procedure, we are able to solve a difficult probabilistic portfolio optimization instance listed in MIPLIB 2010, which cannot be solved by existing approaches. In the second part of this dissertation we study a class of probabilistic 0-1 covering problems, namely probabilistic k-cover problems. A probabilistic k-cover problem is a stochastic version of a set k-cover problem, which is to seek a collection of subsets with a minimal cost whose union covers each element in the set at least k times. In a stochastic setting, the coefficients of the covering constraints are modeled as Bernoulli random variables, and the probabilistic constraint imposes a minimal requirement on the probability of k-coverage. To account for absence of full distributional information, we define a general ambiguous k-cover set, which is ``distributionally-robust." Using a classical linear program (called the Boolean LP) to compute the probability of events, we develop an exact deterministic reformulation to this ambiguous k-cover problem. However, since the boolean model consists of exponential number of auxiliary variables, and hence not useful in practice, we use two linear program based bounds on the probability that at least k events occur, which can be obtained by aggregating the variables and constraints of the Boolean model, to develop tractable deterministic approximations to the ambiguous k-cover set. We derive new valid inequalities that can be used to strengthen the linear programming based lower bounds. Numerical results show that these new inequalities significantly improve the probability bounds. To use standard MIP solvers, we linearize the multi-linear terms in the approximations and develop mixed-integer linear programming formulations. We conduct computational experiments to demonstrate the quality of the deterministic reformulations in terms of cost effectiveness and solution robustness. To demonstrate the usefulness of the modeling technique developed for probabilistic k-cover problems, we formulate a number of problems that have up till now only been studied under data independence assumption and we also introduce a new applications that can be modeled using the probabilistic k-cover model.

In this study, our aim is to determine the locations of bottle banks used in collecting recycled glass. The collection of recycled glass is done by a fleet of vehicles that visit some predetermined collection points, like restaurants and hospitals. The location of bottle banks depends on the closeness of the banks to the population zones where the recycled class is generated, and to the closeness of the banks to the predetermined collection points. A mathematical model, which combines the maximal covering problem in the presence of partial coverage and vehicle routing problem with profits, is presented. Heuristic procedures are proposed for the solution of the problem. Computational results based on generated test problems are provided. We also discuss a case study, where bottle banks are located in Yenimahalle, a district of Ankara

In this study, a metaheuristic procedure, particularly a variable neighborhood search procedure, is proposed to solve the combined location and selective traveling salesman problem in glass recycling. The collection of used glass is done by a collecting vehicle that visits a number of predefined collection centers, like restaurants and hospitals that are going to be referred to as compulsory points. Meanwhile, it is desired to locate a predetermined number of bottle banks to residential areas. The aim is to determine the location of these bottle banks and the route of the collecting vehicle so that all compulsory points and all bottle banks are visited and the maximum profit is obtained. Population zones are defined in residential areas and it is assumed that the people in a particular population zone will recycle their used glass to the closest bottle bank that fully or partially covers their zone. A Variable Neighborhood Search algorithm and its variant have been utilized for the solution of the problem. Computational experiments have been made on small and medium scale test problems, randomly generated and adapted from the literature.

Covering arrays with row limit, CARLs, are a new family of combinatorial objects which we introduce as a generalization of group divisible designs and covering arrays. In the same manner as their predecessors, CARLs have a natural application as combinatorial models for interaction test suites. A CARL(N;t,k,v:w), is an N×k array with some empty cells. A component, which is represented by a column, takes values from a v-set called the alphabet. In each row, there are exactly w non-empty cells, that is the corresponding components have an assigned value from the alphabet. The parameter w is called the row limit. Moreover, any N×t subarray contains every of v^t distinct t-tuples of alphabet symbols at least once. This thesis is concerned with the bounds on the size and with the construction of CARLs when the row limit w(k) is a positive integer valued function of the number of columns, k. Here we give a lower bound, and probabilistic and algorithmic upper bounds for any CARL. Further, we find improvements on the upper bounds when w(k)ln(w(k)) = o(k) and when w(k) is a constant function. We also determine the asymptotic size of CARLs when w(k) = Θ(k) and when w(k) is constant. Next, we study constructions of CARLs. We provide two combinatorial constructions of CARLs, which we apply to construct families of CARLs with w(k)=ck, where c<1. Also, we construct optimal CARLs when t=2 and w=4, and prove that there exists a constant δ, such that for any v and k≥4, an optimal CARL(2,k,v:4) differs from the lower bound by at most δ rows, with some possible exceptions. Finally, we define a packing array with row limit, PARL(N;t,k,v:w), in the same way as a CARL(N;t,k,v:w) with the difference that any t-tuple is contained at most once in any N×t subarray. We find that when w(k) is a constant function, the results on the asymptotic size of CARLs imply the results on the asymptotic size of PARLs. Also, when t=2, we consider a transformation of optimal CARLs with row limit w=3 to optimal PARLs with w=3.

Covering arrays with row limit, CARLs, are a new family of combinatorial objects which we introduce as a generalization of group divisible designs and covering arrays. In the same manner as their predecessors, CARLs have a natural application as combinatorial models for interaction test suites. A CARL(N;t,k,v:w), is an N×k array with some empty cells. A component, which is represented by a column, takes values from a v-set called the alphabet. In each row, there are exactly w non-empty cells, that is the corresponding components have an assigned value from the alphabet. The parameter w is called the row limit. Moreover, any N×t subarray contains every of v^t distinct t-tuples of alphabet symbols at least once. This thesis is concerned with the bounds on the size and with the construction of CARLs when the row limit w(k) is a positive integer valued function of the number of columns, k. Here we give a lower bound, and probabilistic and algorithmic upper bounds for any CARL. Further, we find improvements on the upper bounds when w(k)ln(w(k)) = o(k) and when w(k) is a constant function. We also determine the asymptotic size of CARLs when w(k) = Θ(k) and when w(k) is constant. Next, we study constructions of CARLs. We provide two combinatorial constructions of CARLs, which we apply to construct families of CARLs with w(k)=ck, where c<1. Also, we construct optimal CARLs when t=2 and w=4, and prove that there exists a constant δ, such that for any v and k≥4, an optimal CARL(2,k,v:4) differs from the lower bound by at most δ rows, with some possible exceptions. Finally, we define a packing array with row limit, PARL(N;t,k,v:w), in the same way as a CARL(N;t,k,v:w) with the difference that any t-tuple is contained at most once in any N×t subarray. We find that when w(k) is a constant function, the results on the asymptotic size of CARLs imply the results on the asymptotic size of PARLs. Also, when t=2, we consider a transformation of optimal CARLs with row limit w=3 to optimal PARLs with w=3.