This research develops an algorithm for solving a class of multiple objective decision problems. These problems are characterized by continuous policy variables, nonlinear constraints, and nonlinear criterion functions. Our underlying philosophy is that of the Gestalt psychologists-- we cannot separate the problem and its solution from the environment in which the problem is placed. The decision maker is necessarily a part of this environment, thus implying that he, as an individual, must be part of the solution of the problem. Another central assumption in this research is that there is not an "optimal" answer to the problem, only "satisfactory" solutions. The reasons for this are based partly on the insensitivities of the body to minute changes and to the insensitivity of our preferences within certain ranges of acceptance. In addition, we assure that the individual is capable of solving decision situations involving a maximum of about 10 goals and that he operates upon them in some sort of serial manner as he searches for a satisfactory alternative. The serial manner is a reflection of his current ranking of the goals. Based on these assumptions we have developed a cyclical interactive algorithm in which the decision maker guides a search mechanism in attempting to find a satisfactory alternative. Each cycle in the search consists of an optimization phase and an evaluation phase, after which the decision maker can define a new direction of search or terminate the algorithm. The optimization phase is based on a linearization technique which has been quite effective in terms of the problems we have attempted to solve. It is capable of solving general nonlinear programming problems with a large number of nonlinear constraints. Although the constraint set must be convex in order to guarantee the location of a global optimum, we can use the method on concave sets recognizing that we may find only a local optimum. An extensive synthetic case study of a water pollution decision problem with 6 conflicting goals is provided to demonstrate the feasibility of the algorithm. Finally, the limitations of the research are discussed. We tentatively conclude that we have developed a method applicable to our research problem and that the method can be applied to "real world" decision situations.

Orientador: Margarida Pinheiro Mello / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-08T02:52:04Z (GMT). No. of bitstreams: 1 Lima_RodrigoSilva_M.pdf: 952013 bytes, checksum: b746a6fed088b1d0474822b76dd7092f (MD5) Previous issue date: 2007 / Resumo: Robótica, computação gráfica, aeronáutica e biomecânica têm em comum o estudo de movimentos rígidos, aqueles compostos por rotações e translações. Ao contrário das translações, cuja representação matemática não apresenta dificuldades, as rotações trazem consideráveis desafios. Neste trabalho de mestrado estudamos algumas propriedades do grupo de rotações 50(3) bem como três maneiras de se representar uma rotação: via mapa exponencial, via ângulos de Euler e via quatérnions unitários. Como as duas últimas representações são as mais comuns na literatura, fazemos uma comparação do uso de ângulos de Euler e quatérnions unitários na formulação e resolução de dois problemas concretos: o problema do empacotamento de moléculas e o problema da orientação absoluta. / Abstract: Robotics, graphics computation, aeronautics and biomechanics have in common the study of rigid motions, composed by rotations and translations. The translations are mathematically simple, but the treatment of rotations raises some difficulties. In this work we study some properties of rotation group 80(3) and three commonly used methods to describe rotations, namely, the exponencial map, Euler angles and unitary quaternions. We employ the latter two in the formulation of two optimization problems: the absolute orientation problem and the molecule-packing problem. Several computational experiments are performed in order to establish the relative efficiency of the two representations in these examples. In the first optimization problem there is a tie, whereas in the molecule-packing problem the representation using quaternions is slightly superior. / Mestrado / Otimização e Pesquisa Operacional / Mestre em Matemática Aplicada

Orientador: Maria Aparecida Diniz Ehrhardt / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-18T12:52:22Z (GMT). No. of bitstreams: 1 Detsch_DeniseTrevisoli_M.pdf: 1081241 bytes, checksum: c29e7e81ee2ecb8467802991fc75be51 (MD5) Previous issue date: 2011 / Resumo: Este trabalho teve como foco o estudo de problemas SOCP, tanto nos seus aspectos teóricos quanto nos seus aspectos práticos. Problemas SOCP são problemas convexos de otimização nos quais uma função linear 'e minimizada sobre restrições lineares e restrições de cone quadrático. Tivemos dois objetivos principais: estudar o conceito, as aplicações e os métodos de resolução de problemas SOCP, permitindo verificar a viabilidade de trabalhar com tais problemas; e verificar na prática o benefício de se utilizar uma ferramenta específica de SOCP para a resolução de problemas que se enquadram nessa classe. Para a avaliação prática utilizamos um software de otimização genérica (fmincon) e outro específico de SOCP (CVXOPT). A análise ficou concentrada nos requisitos robustez, número de iterações e variação do tempo com o aumento da dimensão dos problemas. Diante dos resultados obtidos com os testes numéricos, pudemos concluir que 'e interessante usar SOCP sempre que possível / Abstract: This dissertation focuses on the study of SOCP problems, both in its theoretical, and in its practical aspects. SOCP problems are convex optimization problems in which a linear function is minimized over linear constraints and second-order cone constraints. We had two main objectives: study the concept, applications and methods for solving the SOCP problem, making it possible to verify the feasibility of working with such problems; and to verify the practical benefits of using a SOCP specific tool for the resolution of problems of this class. The experimental evaluation used a generic optimization software (fmincon) and other SOCP specific software (CVXOPT). The analysis was concentrated on the robustness, number of iterations and time variation with the increasing scale of the problems. From results obtained with the numerical tests, we concluded that SOCP is worth to be used whenever possible / Mestrado / Matematica Aplicada / Mestre em Matemática Aplicada

No presente trabalho, estudamos e desenvolvemos algoritmos com análise de complexidade de avaliação de pior caso para problemas de programação não linear. Para minimização irrestrita, estabelecemos dois algoritmos semelhantes que exploram modelos de ordem superior com estratégia de regularização. Propusemos uma implementação computacional que preserva as boas propriedades teóricas de complexidade, e fizemos experimentos numéricas com problemas clássicos da literatura, a fim de atestar a implementação e avaliar a aplicabilidade de métodos que empreguem modelos de ordem superior. Para minimização com restrições, estabelecemos um algoritmo de duas fases que converge a pontos que satisfazem condições de otimalidade de primeira ordem não escaladas para o problema de programação não linear. / In the present work, we have studied and developed algorithms with worst-case evaluation complexity analysis for nonlinear programming problems. For the unconstrained optimization case, we have established two similar algorithms that explore high-order regularization models. We have proposed a computational implementation that preserves the good properties of the evaluation complexity theory, and we made numerical experiments with classical problems from the literature, in order to check the implementation and certify the practical applicability of methods that employ high-order models. For the constrained optimization case, we have established a two phases algorithm that converges to points that meet the unscaled first-order optimality condition for the nonlinear programming problem.

This thesis deals with optimization of a vehicle’s (racing) drive on a track. The model of a vehicle and a track is built in this thesis. The first chapter is devoted to the fastest pass problem formulation. The problem optimizes (in the least time) the vehicle’s drive from a start line to a finish line. The problem is formulated as an optimal control theory problem. In the second chapter the optimal control theory problem is suitably discretised and transformed into a nonlinear programming problem. The transformation of the fastest pass problem into nonlinear programming problem, its detailed and illustrative derivation and reformulation form the main part of the thesis. Third chapter presents the implementation and solution of the problem using GAMS and MATLAB. This thesis is a part of a specific research project on which the author has participated. The main contribution of the author is an original formulation of the fastest pass problem as a nonlinear programming problem and its implementation and solving using GAMS.

A dissertation submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in Engineering. University of the Witwatersrand, Johannesburg, 2016 / Given a flight schedule with fixed departure times and cost, solving the fleet assignment problem assists airlines to find the minimum cost or maximum revenue assignment of aircraft types to flights. The result is that each flight is covered exactly once by an aircraft and the assignment can be flown using the available number of aircraft of each fleet type. This research proposes a novel, non-linear integer programming fleet assignment model which differs from the linear time-space multi-commodity network fleet assignment model which is commonly used in industry. The performance of the proposed model with respect to the amount of time it takes to create a flight schedule is measured. Similarly, the performance of the time-space multicommodity fleet assignment model is also measured. The objective function from both mathematical models is then compared and results reported. Due to the non-linearity of the proposed model, a genetic algorithm (GA) is used to find a solution. The time taken by the GA is slow. The objective function value, however, is the same as that obtained using the time-space multi-commodity network flow model. The proposed mathematical model has advantages in that the solution is easier to interpret. It also simultaneously solves fleet assignment as well as individual aircraft routing. The result may therefore aid in integrating more airline planning decisions such as maintenance routing. / MT2017

This report considers an application of mixed-integer disjunctive programming (MIDP)where a theoretical robot can jump from one point to another and where the number ofjumps is to be minimized. The robot is only able to jump to the north, south, east andwest. Furthermore, the robot should also be able to navigate and jump around or across anypotential obstacles on the way. The algorithm for solving this problem is set to terminatewhen the robot has reached a set of end coordinates. The goal of this report is to find amethod for solving this problem and to investigate the time complexity of such a method.The problem is converted to big-M representation and solved numerically. Gurobi is theoptimization solver used in this thesis. The model created and implemented with Gurobiyielded optimal solutions to problems of the form above of varying complexity. For most ofcases tested, the time complexity appeared to be linear, but this is likely due to presolvingperformed by Gurobi before running the optimization. Further tests are needed to determinethe time complexity of Gurobi’s optimization algorithm for this specific type of problem.

Low-thrust interplanetary spacecraft trajectory optimization poses a uniquely difficult problem to solve because of the inherent nonlinearities of the dynamics and constraints as well as the large size of the search space of possible solutions. Tools currently exist that optimize low-thrust interplanetary trajectories, but these tools are rarely openly available to the public, and when they are available they require multiple interfaces between multiple different packages. The goal of this work is to present a new piece of low-thrust interplanetary spacecraft trajectory optimization software that is open-source and entirely self-contained so that more people can have access to the ability to design interplanetary trajectories. To achieve this goal, a gradient-descent based nonlinear programming method, called the interior point method, was used. The nonlinear programming method was chosen so that results from this work could be compared and contrasted with results from Spacecraft Trajectory Optimization Suite (STOpS), which uses heuristics to iterate towards a solution. Interior point methods are popular because of their ability to handle large amounts of equality and inequality constraints, which is a characteristic that is valuable for low-thrust interplanetary spacecraft trajectories. The software developed, Interior Point Optimizer (IP Optimizer), was then validated against test cases with known solutions to ensure that the software delivered the intended results. Lastly, a constraint satisfaction, a minimum-time, and a maximum-final-mass optimization problem were solved and compared with literature to illustrate the advantages of IP Optimizer and the methods it employs. For the constraint satisfaction problem, IP Optimizer was able to find a solution that exactly satisfied the desired terminal constraints whereas STOpS had an error of 2.29 percent. In this case, IP Optimizer had a reduced runtime of 15 percent compared to STOpS as well. When minimizing time for a spacecraft transfer, IP Optimizer improved upon the solution found by STOpS by 5.3 percent. The speed of convergence for IP Optimizer was almost twice as fast as STOpS for this case. These results show that IP Optimizer is faster than STOpS at converging on a solution and the solution it converges to has a better objective value and more accurately satisfies the terminal constraints than STOpS. Lastly, the maximum-final-mass problem resulted in an objective value that was only 0.5 percent lower than the value found in literature.

<p> Control structure design is an essential step in control system synthesis and has big impact on achievable closed-loop performance. This thesis develops a systematic approach of selecting optimal control structures based on closed-loop dynamic performance and other criteria, such as integrity.</p> <p> The main contribution of this thesis is a rigorous mathematical formulation for control structure design problem that includes full closed-loop transient analysis with additional integrity requirement. The multi-objective framework is extendable so that different control performance objectives can be easily added. Unique process requirements and engineer inputs can be taken into account as additional constraints. The proposed formulation is a Mixed Integer Nonlinear Programming (MINLP) with complementarity constraints. The research scope is limited to linear process models and linear controller algorithms.</p> <p> The tailored solving strategy that makes this challenging problem computationally tractable is introduced in this thesis. The modified Branch and Bound algorithm takes advantage of the special problem structure by using control knowledge to generate valid lower bound efficiently. Prior knowledge can be cooperated as heuristic tuning parameters to guide the solving process so that a reasonably good solution can be found early in the solving process. The complexity study shows the solving strategy can attack design problem size up to 8x8. Considering the percentage of good structures needing evaluation will decrease with problem size even larger problems will be tractable.</p> <p> The common control structures in process industries, such as square and nonsquare Single-Input-Single-Output (SISO) loop pairing using PID controller and block-centralized structure using Model Predictive Controller (MPC), are addressed in this thesis. The usefulness of this research has been demonstrated by several case studies, include Tennessee Eastman problem. The proposed methodology finds a physically sound pairing with good performance for Tennessee Eastman problem in less than one hour, while several off-the-shelf NLP, MINLP and global solvers cannot find a solution in five days.</p> / Thesis / Doctor of Philosophy (PhD)

This thesis presents frameworks for the optimal strategic production planning of petroleum refineries operating in competition in multiple markets. The game theoretic concept of the Cournot oligopoly is used as the basic competitive model, and the Nash equilibrium as the solution concept for the formulated problems, which are reformulated into potential games. Nonlinear programming potential game frameworks are developed for static and dynamic production planning problems, as well for mixed integer nonlinear expansion planning problems in which refiners have access to potential upgrades increasing their competitiveness. This latter model represents a novel problem in game theory as it contains both integer and continuous variables and thus must satisfy both discrete and continuous mathematical definitions of the Nash equilibrium. The concept of the mixed-integer game is introduced to explore this problem and the theoretical properties of the new class of games, for which conditions are identified defining when a class of two-player games will possess Nash equilibria in pure strategies, and conjectures offered regarding the properties of larger problems and the class as a whole. In all examples, petroleum refinery problems are solved to optimality (equilibrium) to illustrate the competitive utility of the mathematical frameworks. The primary benefit of such frameworks is the incorporation of the influence of market supply and demand on refinery profits, resulting in rational driving forces in the underlying production planning problems. These results are used to justify the development of frameworks for enterprise optimization as a means of decision making in competitive industries. / Thesis / Doctor of Philosophy (PhD) / This thesis presents a mathematical framework in which refinery production planning problems are solved to optimal solutions in competing scenarios. Concepts from game theory are used to formulate these competitive problems into mathematical programs under single objective functions which coordinate the interests of the competing refiners. Several different cases are considered presenting refinery planning problems as static and dynamic programs in which decisions are time independent or dependent, respectively. A theoretical development is also presented in the concept of the mixed integer game, a game theoretic problem containing both continuous and discrete valued variables and which must satisfy both continuous and discrete definitions of Nash equilibrium. This latter development is used to examine refinery problems in which individual refiners have access to numerous unit upgrades which can potentially improve performance. The results are used to justify a game theoretic approach to enterprise optimization.