Promoção de uma oferta equitativa no setor dos cuidados continuados integrados: desenvolvimento de uma abordagem multi-período e multiobjetivo para apoio ao planeamento da oferta de cuidados

Este estudo propõe um modelo de programação matemática multi‑objetivo e multi‑período para apoiar as decisões de planeamento no setor dos cuida‑ dos continuados integrados (CCI). O modelo proposto permite apoiar o pla‑ neamento da oferta de CCI em regime de internamento, tanto em termos de seleção das melhores localizações para esses serviços, como também da capa‑ cidade a instalar, e isto com o propósito de construir uma rede de cuidados mais equitativa. Serão, assim, considerados três objetivos de equidade – equi‑ dade de acesso, equidade geográfica e equidade socioeconómica. Serão tam‑ bém contabilizados os custos, mas na forma de restrições do modelo. A funçãoobjetivo do modelo incorpora estes múltiplos objetivos de equidade através da atribuição de pesos que são obtidos com recurso à metodologia Measuring Attractiveness by a Category‑Based Evaluation TecHnique (MACBETH). A uti‑ lidade do modelo é ilustrada através da sua aplicação a um caso de estudo na região da Grande Lisboa em Portugal.info:eu-repo/semantics/publishedVersio

Considering scale within optimization procedures for water management decisions: Balancing environmental flows and human needs

A key issue in optimization model development is the selection of spatial and temporal scale representing the system. This study proposes a framework for reasoning about scale in this context, drawing on a review of studies applying multi-objective optimization for water management involving environmental flows. We suggest that scale is determined by the management problem, constrained by data availability, computational, and model capabilities. There is therefore an inherent trade-off between problem perception and available modelling capability, which can either be resolved by obtaining data needed or tailoring analysis to the data available. In the interest of fostering transparency in this trade-off process, this paper outlines phases of model development, associated decisions, and available options, and scale implications of each decision. The problem perception phase collects system information about objectives, limiting conditions, and management options. The problem formulation phase collects and uses data, information, and methods about system structure and behaviour.Joseph Guillaume received funding from an Australian Research Council Discovery Early Career Researcher Award (project no. DE190100317). Avril Horne received funding from Australian Research Council Discovery Early Career Researcher Award (project no. DE180100550

A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA

[EN] Traditionally, in a multiobjective optimization problem, the aim is to find the set of optimal solutions, the Pareto front, which provides the decision-maker with a better understanding of the problem. This results in a more knowledgeable decision. However, multimodal solutions and nearly optimal solutions are ignored, although their consideration may be useful for the decision-maker. In particular, there are some of these solutions which we consider specially interesting, namely, the ones that have distinct characteristics from those which dominate them (i.e., the solutions that are not dominated in their neighborhood). We call these solutions potentially useful solutions. In this work, a new genetic algorithm called nevMOGA is presented, which provides not only the optimal solutions but also the multimodal and nearly optimal solutions nondominated in their neighborhood. This means that nevMOGA is able to supply additional and potentially useful solutions for the decision-making stage. This is its main advantage. In order to assess its performance, nevMOGA is tested on two benchmarks and compared with two other optimization algorithms (random and exhaustive searches). Finally, as an example of application, nevMOGA is used in an engineering problem to optimally adjust the parameters of two PI controllers that operate a plant.This work was partially supported by the Ministerio de Economia y Competitividad (Spain) Grant numbers DPI2015-71443-R and FPU15/01652, by the local administration Generalitat Valenciana through the project GV/2017/029, and by the National Council of Scientific and Technological Development of Brazil (CNPq) through the grant PQ-2/304066/2016-8.Pajares-Ferrando, A.; Blasco, X.; Herrero Durá, JM.; Reynoso-Meza, G. (2018). A Multiobjective Genetic Algorithm for the Localization of Optimal and Nearly Optimal Solutions Which Are Potentially Useful: nevMOGA. Complexity. 2018:1-22. https://doi.org/10.1155/2018/1792420S1222018Reynoso-Meza, G., Sanchis, J., Blasco, X., & Martínez, M. (2013). Algoritmos Evolutivos y su empleo en el ajuste de controladores del tipo PID: Estado Actual y Perspectivas. Revista Iberoamericana de Automática e Informática Industrial RIAI, 10(3), 251-268. doi:10.1016/j.riai.2013.04.001Reynoso-Meza, G., Sanchis, J., Blasco, X., & García-Nieto, S. (2014). Physical programming for preference driven evolutionary multi-objective optimization. Applied Soft Computing, 24, 341-362. doi:10.1016/j.asoc.2014.07.009SANCHIS, J., MARTINEZ, M., & BLASCO, X. (2008). Integrated multiobjective optimization and a priori preferences using genetic algorithms. Information Sciences, 178(4), 931-951. doi:10.1016/j.ins.2007.09.018Loridan, P. (1984). ?-solutions in vector minimization problems. Journal of Optimization Theory and Applications, 43(2), 265-276. doi:10.1007/bf00936165White, D. J. (1986). Epsilon efficiency. Journal of Optimization Theory and Applications, 49(2), 319-337. doi:10.1007/bf00940762Vasile, M., & Locatelli, M. (2008). A hybrid multiagent approach for global trajectory optimization. Journal of Global Optimization, 44(4), 461-479. doi:10.1007/s10898-008-9329-3Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257-271. doi:10.1109/4235.797969Herrero, J. M., García-Nieto, S., Blasco, X., Romero-García, V., Sánchez-Pérez, J. V., & Garcia-Raffi, L. M. (2008). Optimization of sonic crystal attenuation properties by ev-MOGA multiobjective evolutionary algorithm. Structural and Multidisciplinary Optimization, 39(2), 203-215. doi:10.1007/s00158-008-0323-7Schütze, O., Coello Coello, C. A., & Talbi, E.-G. (2007). Approximating the ε-Efficient Set of an MOP with Stochastic Search Algorithms. Lecture Notes in Computer Science, 128-138. doi:10.1007/978-3-540-76631-5_13Schutze, O., Vasile, M., & Coello, C. A. C. (2011). Computing the Set of Epsilon-Efficient Solutions in Multiobjective Space Mission Design. Journal of Aerospace Computing, Information, and Communication, 8(3), 53-70. doi:10.2514/1.46478Sareni, B., & Krahenbuhl, L. (1998). Fitness sharing and niching methods revisited. IEEE Transactions on Evolutionary Computation, 2(3), 97-106. doi:10.1109/4235.735432Schutze, O., Esquivel, X., Lara, A., & Coello, C. A. C. (2012). Using the Averaged Hausdorff Distance as a Performance Measure in Evolutionary Multiobjective Optimization. IEEE Transactions on Evolutionary Computation, 16(4), 504-522. doi:10.1109/tevc.2011.2161872Blasco, X., Herrero, J. M., Sanchis, J., & Martínez, M. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences, 178(20), 3908-3924. doi:10.1016/j.ins.2008.06.01

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Advances in Data-Driven Modeling and Global Optimization of Constrained Grey-Box Computational Systems

The effort to mimic a chemical plant’s operations or to design and operate a completely new technology in silico is a highly studied research field under process systems engineering. As the rising computation power allows us to simulate and model systems in greater detail through careful consideration of the underlying phenomena, the increasing use of complex simulation software and generation of multi-scale models that spans over multiple length and time scales calls for computationally efficient solution strategies that can handle problems with different complexities and characteristics. This work presents theoretical and algorithmic advancements for a range of challenging classes of mathematical programming problems through introducing new data-driven hybrid modeling and optimization strategies. First, theoretical and algorithmic advances for bi-level programming, multi-objective optimization, problems containing stiff differential algebraic equations, and nonlinear programming problems are presented. Each advancement is accompanied with an application from the grand challenges faced in the engineering domain including, food-energy-water nexus considerations, energy systems design with economic and environmental considerations, thermal cracking of natural gas liquids, and oil production optimization. Second, key modeling challenges in environmental and biomedical systems are addressed through employing advanced data analysis techniques. Chemical contaminants created during environmental emergencies, such as hurricanes, pose environmental and health related risks for exposure. The goal of this work is to alleviate challenges associated with understanding contaminant characteristics, their redistribution, and their biological potential through the use of data analytics