Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

An examination of heuristics for the shelf space allocation problem.

Wong, Mei Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2010.Includes bibliographical references (p. 115-120).Abstracts in English and Chinese.Chapter 1. --- Introduction --- p.1Chapter 1.1 --- Background --- p.1Chapter 1.2 --- Our Contributions --- p.4Chapter 1.3 --- Framework of Shelf Space Allocation Problem --- p.4Chapter 1.4 --- Organization --- p.6Chapter 2. --- Literature Review --- p.7Chapter 2.1 --- Introduction --- p.7Chapter 2.2 --- Commercial Approaches --- p.7Chapter 2.3 --- Experimental Approaches --- p.8Chapter 2.4 --- Optimization Approaches --- p.11Chapter 2.4.1 --- Exact Approaches --- p.11Chapter 2.4.2 --- Heuristics Approaches --- p.16Chapter 2.5 --- Summary --- p.19Chapter 3. --- Overview of Shelf Space Allocation Problem --- p.21Chapter 3.1 --- Introduction --- p.21Chapter 3.2 --- Problem description --- p.22Chapter 3.2.1 --- Mathematical Model --- p.24Chapter 3.2.1.1 --- Notations --- p.25Chapter 3.2.1.2 --- Model --- p.25Chapter 3.2.1.3 --- Assumption --- p.26Chapter 3.2.1.4 --- Notations of final model --- p.27Chapter 3.2.1.5 --- Final model --- p.27Chapter 3.3 --- Original Heuristic --- p.28Chapter 3.3.1 --- Yang (2001) Method --- p.28Chapter 3.3.2 --- Remarks on Original Heuristic --- p.29Chapter 3.4 --- Original Heuristic with Yang's Adjustment --- p.30Chapter 3.4.1 --- Remarks on Yang's Adjustment --- p.32Chapter 3.5 --- New Neighborhood Movements --- p.33Chapter 3.5.1 --- New Adjustment Phase --- p.33Chapter 3.6 --- Network Flow Model --- p.35Chapter 3.6.1 --- ULSSAP --- p.35Chapter 3.6.2 --- Transforming shelf space allocation problem (SSAP) --- p.38Chapter 3.7 --- Tabu Search --- p.41Chapter 3.7.1 --- Tabu Search Algorithm --- p.42Chapter 3.7.1.1 --- Neighborhood search moves --- p.42Chapter 3.7.1.2 --- Candidate list strategy --- p.45Chapter 3.7.1.3 --- Tabu list --- p.46Chapter 3.7.1.4 --- Aspiration criteria.........................................: --- p.47Chapter 3.7.1.5 --- Intensification and Diversification --- p.48Chapter 3.7.1.6 --- Stopping criterion --- p.49Chapter 3.7.1.7 --- Probabilistic choice --- p.50Chapter 3.7.2 --- General Process of Tabu Search --- p.51Chapter 3.7.3 --- Application of Tabu Search to SSAP --- p.54Chapter 3.7.4 --- Analysis of Tabu Search --- p.58Chapter 4. --- Tabu Search with Path Relinking --- p.60Chapter 4.1 --- Introduction --- p.60Chapter 4.2 --- Foundations of path relinking --- p.62Chapter 4.3 --- Path Relinking Template --- p.65Chapter 4.4 --- Identification of Reference set --- p.69Chapter 4.5 --- Choosing initial and guiding solution --- p.73Chapter 4.6 --- Neighborhood structure --- p.74Chapter 4.7 --- Moving along paths --- p.81Chapter 4.8 --- Application of Tabu Search with Path Relinking --- p.87Chapter 4.9 --- Conclusion --- p.90Chapter 5. --- Computational Studies --- p.92Chapter 5.1 --- Introduction --- p.92Chapter 5.2 --- General Parameter Setting --- p.92Chapter 5.3 --- Parameter values for Tabu search --- p.94Chapter 5.4 --- Sensitivity test for Tabu search with Path Relinking --- p.95Chapter 5.4.1 --- Reference Set Strategies and Initial and Guiding Solution Criteria --- p.96Chapter 5.4.2 --- Frequency of Path Relinking --- p.99Chapter 5.4.3 --- Size of reference set --- p.101Chapter 5.4.4 --- Comparison with Tabu Search --- p.102Chapter 5.5 --- Comparison with other heuristics --- p.105Chapter 5.6 --- Conclusion --- p.109Chapter 6. --- Conclusion --- p.111Chapter 6.1 --- Summary of achievements --- p.112Chapter 6.2 --- Future Works --- p.113Bibliography --- p.11

The problems of optimal design in the automotive industry

The paper considers the general problems of optimization of parameters of a vehicle at the design stage. The approaches to the solution of the task are set out. The questions of a choice of the objective function and the system of limitations are in focus, as the problem of optimization makes sense, if there are several possible variants of its solution

Development of a multi-objective optimization algorithm based on lichtenberg figures

This doctoral dissertation presents the most important concepts of multi-objective optimization and a systematic review of the most cited articles in the last years of this subject in mechanical engineering. The State of the Art shows a trend towards the use of metaheuristics and the use of a posteriori decision-making techniques to solve engineering problems. This fact increases the demand for algorithms, which compete to deliver the most accurate answers at the lowest possible computational cost. In this context, a new hybrid multi-objective metaheuristic inspired by lightning and Linchtenberg Figures is proposed. The Multi-objective Lichtenberg Algorithm (MOLA) is tested using complex test functions and explicit contrainted engineering problems and compared with other metaheuristics. MOLA outperformed the most used algorithms in the literature: NSGA-II, MOPSO, MOEA/D, MOGWO, and MOGOA. After initial validation, it was applied to two complex and impossible to be analytically evaluated problems. The first was a design case: the multi-objective optimization of CFRP isogrid tubes using the finite element method. The optimizations were made considering two methodologies: i) using a metamodel, and ii) the finite element updating. The last proved to be the best methodology, finding solutions that reduced at least 45.69% of the mass, 18.4% of the instability coefficient, 61.76% of the Tsai-Wu failure index and increased by at least 52.57% the natural frequency. In the second application, MOLA was internally modified and associated with feature selection techniques to become the Multi-objective Sensor Selection and Placement Optimization based on the Lichtenberg Algorithm (MOSSPOLA), an unprecedented Sensor Placement Optimization (SPO) algorithm that maximizes the acquired modal response and minimizes the number of sensors for any structure. Although this is a structural health monitoring principle, it has never been done before. MOSSPOLA was applied to a real helicopter’s main rotor blade using the 7 best-known metrics in SPO. Pareto fronts and sensor configurations were unprecedentedly generated and compared. Better sensor distributions were associated with higher hypervolume and the algorithm found a sensor configuration for each sensor number and metric, including one with 100% accuracy in identifying delamination considering triaxial modal displacements, minimum number of sensors, and noise for all blade sections.Esta tese de doutorado traz os conceitos mais importantes de otimização multi-objetivo e uma revisão sistemática dos artigos mais citados nos últimos anos deste tema em engenharia mecânica. O estado da arte mostra uma tendência no uso de meta-heurísticas e de técnicas de tomada de decisão a posteriori para resolver problemas de engenharia. Este fato aumenta a demanda sobre os algoritmos, que competem para entregar respostas mais precisas com o menor custo computacional possível. Nesse contexto, é proposta uma nova meta-heurística híbrida multi-objetivo inspirada em raios e Figuras de Lichtenberg. O Algoritmo de Lichtenberg Multi-objetivo (MOLA) é testado e comparado com outras metaheurísticas usando funções de teste complexas e problemas restritos e explícitos de engenharia. Ele superou os algoritmos mais utilizados na literatura: NSGA-II, MOPSO, MOEA/D, MOGWO e MOGOA. Após validação, foi aplicado em dois problemas complexos e impossíveis de serem analiticamente otimizados. O primeiro foi um caso de projeto: otimização multi-objetivo de tubos isogrid CFRP usando o método dos elementos finitos. As otimizações foram feitas considerando duas metodologias: i) usando um meta-modelo, e ii) atualização por elementos finitos. A última provou ser a melhor metodologia, encontrando soluções que reduziram pelo menos 45,69% da massa, 18,4% do coeficiente de instabilidade, 61,76% do TW e aumentaram em pelo menos 52,57% a frequência natural. Na segunda aplicação, MOLA foi modificado internamente e associado a técnicas de feature selection para se tornar o Seleção e Alocação ótima de Sensores Multi-objetivo baseado no Algoritmo de Lichtenberg (MOSSPOLA), um algoritmo inédito de Otimização de Posicionamento de Sensores (SPO) que maximiza a resposta modal adquirida e minimiza o número de sensores para qualquer estrutura. Embora isto seja um princípio de Monitoramento da Saúde Estrutural, nunca foi feito antes. O MOSSPOLA foi aplicado na pá do rotor principal de um helicóptero real usando as 7 métricas mais conhecidas em SPO. Frentes de Pareto e configurações de sensores foram ineditamente geradas e comparadas. Melhores distribuições de sensores foram associadas a um alto hipervolume e o algoritmo encontrou uma configuração de sensor para cada número de sensores e métrica, incluindo uma com 100% de precisão na identificação de delaminação considerando deslocamentos modais triaxiais, número mínimo de sensores e ruído para todas as seções da lâmina

Emerging Trends in Mechatronics

Mechatronics is a multidisciplinary branch of engineering combining mechanical, electrical and electronics, control and automation, and computer engineering fields. The main research task of mechatronics is design, control, and optimization of advanced devices, products, and hybrid systems utilizing the concepts found in all these fields. The purpose of this special issue is to help better understand how mechatronics will impact on the practice and research of developing advanced techniques to model, control, and optimize complex systems. The special issue presents recent advances in mechatronics and related technologies. The selected topics give an overview of the state of the art and present new research results and prospects for the future development of the interdisciplinary field of mechatronic systems

Collision-free path planning for robots using B-splines and simulated annealing

This thesis describes a technique to obtain an optimal collision-free path for an automated guided vehicle (AGV) and/or robot in two and three dimensions by synthesizing a B-spline curve under geometric and intrinsic constraints. The problem is formulated as a combinatorial optimization problem and solved by using simulated annealing. A two-link planar manipulator is included to show that the B-spline curve can also be synthesized by adding kinematic characteristics of the robot. A cost function, which includes obstacle proximity, excessive arc length, uneven parametric distribution and, possibly, link proximity costs, is developed for the simulated annealing algorithm. Three possible cases for the orientation of the moving object are explored: (a) fixed orientation, (b) orientation as another independent variable, and (c) orientation given by the slope of the curve. To demonstrate the robustness of the technique, several examples are presented. Objects are modeled as ellipsoid type shapes. The procedure to obtain the describing parameters of the ellipsoid is also presented

NASA thesaurus. Volume 2: Access vocabulary

The Access Vocabulary, which is essentially a permuted index, provides access to any word or number in authorized postable and nonpostable terms. Additional entries include postable and nonpostable terms, other word entries, and pseudo-multiword terms that are permutations of words that contain words within words. The Access Vocabulary contains 40,738 entries that give increased access to the hierarchies in Volume 1 - Hierarchical Listing

Relationships Between Structure, Dynamics, And Flow In Sheared Amorphous Materials

Amorphous solids, those composed of haphazardly arranged constituents, are found everywhere from our windows as silicate glass, in the ground and foundations as mud and concrete, and our grocery stores as granular piles of oranges. Even though they can be found over a huge range of length scales, it remains a challenge to systematically design their mechanical properties using knowledge of their microstructure. In this thesis, I investigate the link between the microstructure and the mechanical properties of a-thermal solids. First, I probe the particle trajectories for chaotic signatures that relate to bulk rheology. Particles are confirmed to exhibit chaotic, Brownian like motion during cyclic shear, even though the particles are large enough that thermal motion is negligible. I also find that, the average area traced by returning particles is proportional to the amplitude of strain, which could be useful for in situ measurements in industrial, granular, mixing applications. Next, I examine the interconnection between particle dynamics and the arrangements of the constituents. I calculate the characteristic time for particles to shift past each other, called relaxation time, and the configurational entropy of the system in excess of a reference ideal gas. I show that the relaxation time at any given instant is related to the excess entropy a quarter shear cycle later, which implies that the dynamics of particles shape the eventual structure. This means it is possible to take a snapshot of particle positions and infer its mechanical past. Finally, I focus on the interplay between particle positions and bulk yield by using concepts from kinetics, thermodynamics, statistical mechanics, and shear transformation zone theory. I establish a relationship between excess entropy and energy dissipation and uncover a novel definition for the yield transition based on memory signatures within the microstructure. Using these observations, I derive a phenomenological model that links the microstructure to bulk rheology that is physically informed and whose parameters are all quantitatively measurable. This dissertation elucidates how the statistics of particle configurations and dynamics give rise to the macroscopic transition from elasticity to plasticity during yield of amorphous, a-thermal solids

An Evolutionary Multi-Objective Optimization Framework for Bi-level Problems

Genetic algorithms (GA) are stochastic optimization methods inspired by the evolutionist theory on the origin of species and natural selection. They are able to achieve good exploration of the solution space and accurate convergence toward the global optimal solution. GAs are highly modular and easily adaptable to specific real-world problems which makes them one of the most efficient available numerical optimization methods. This work presents an optimization framework based on the Multi-Objective Genetic Algorithm for Structured Inputs (MOGASI) which combines modules and operators with specialized routines aimed at achieving enhanced performance on specific types of problems. MOGASI has dedicated methods for handling various types of data structures present in an optimization problem as well as a pre-processing phase aimed at restricting the problem domain and reducing problem complexity. It has been extensively tested against a set of benchmarks well-known in literature and compared to a selection of state-of-the-art GAs. Furthermore, the algorithm framework was extended and adapted to be applied to Bi-level Programming Problems (BPP). These are hierarchical optimization problems where the optimal solution of the bottom-level constitutes part of the top-level constraints. One of the most promising methods for handling BPPs with metaheuristics is the so-called "nested" approach. A framework extension is performed to support this kind of approach. This strategy and its effectiveness are shown on two real-world BPPs, both falling in the category of pricing problems. The first application is the Network Pricing Problem (NPP) that concerns the setting of road network tolls by an authority that tries to maximize its profit whereas users traveling on the network try to minimize their costs. A set of instances is generated to compare the optimization results of an exact solver with the MOGASI bi-level nested approach and identify the problem sizes where the latter performs best. The second application is the Peak-load Pricing (PLP) Problem. The PLP problem is aimed at investigating the possibilities for mitigating European air traffic congestion. The PLP problem is reformulated as a multi-objective BPP and solved with the MOGASI nested approach. The target is to modulate charges imposed on airspace users so as to redistribute air traffic at the European level. A large scale instance based on real air traffic data on the entire European airspace is solved. Results show that significant improvements in traffic distribution in terms of both schedule displacement and air space sector load can be achieved through this simple, en-route charge modulation scheme