Coupling soft computing, simulation and optimization in supply chain applications : review and taxonomy

Supply chain networks are typical examples of complex systems. Thereby, making decisions in such systems remains a very hard issue. To assist decision makers in formulating the appropriate strategies, robust tools are needed. Pure optimization models are not appropriate for several reasons. First, an optimization model cannot capture the dynamic behavior of a complex system. Furthermore, most common practical problems are very constrained to be modeled as simple tractable models. To fill in the gap, hybrid optimization/simulation techniques have been applied to improve the decision-making process. In this paper we explore the near-full spectrum of optimization methods and simulation techniques. A review and taxonomy were performed to give an overview of the broad field of optimization/simulation approaches applied to solve supply chain problems. Since the possibilities of coupling them are numerous, we launch a discussion and analysis that aims at determining the appropriate framework for the studied problem depending on its characteristics. Our study may serve as a guide for researchers and practitioners to select the suitable technique to solve a problem and/or to identify the promising issues to be further explored

Evolutionary Computation 2020

Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms

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

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects

An investigation of production and transportation policies for multi-item and multi-stage production systems

Die vorliegende kumulative Dissertation besteht aus fünf Artikeln, einem Arbeitspapier und vier Artikeln, die in wissenschaftlichen Zeitschriften veröffentlicht wurden. Alle fünf Artikel beschäftigen sich mit der Losgrößenplanung, jedoch mit unterschiedlichen Schwerpunkten. Artikel 1 bis 4 untersuchen das Economic Lot Scheduling Problem (ELSP), während sich der fünfte Artikel mit einer Variante des Joint Economic Lot Size (JELS) Problems beschäftigt. Die Struktur dieser Dissertation trägt diesen beiden Forschungsrichtungen Rechnung und ordnet die ersten vier Artikel dem Teil A und den fünften Artikel dem Teil B zu

Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems

The electrical power system is undergoing a revolution enabled by advances in telecommunications, computer hardware and software, measurement, metering systems, IoT, and power electronics. Furthermore, the increasing integration of intermittent renewable energy sources, energy storage devices, and electric vehicles and the drive for energy efficiency have pushed power systems to modernise and adopt new technologies. The resulting smart grid is characterised, in part, by a bi-directional flow of energy and information. The evolution of the power grid, as well as its interconnection with energy storage systems and renewable energy sources, has created new opportunities for optimising not only their techno-economic aspects at the planning stages but also their control and operation. However, new challenges emerge in the optimization of these systems due to their complexity and nonlinear dynamic behaviour as well as the uncertainties involved.This volume is a selection of 20 papers carefully made by the editors from the MDPI topic “Optimisation, Optimal Control and Nonlinear Dynamics in Electrical Power, Energy Storage and Renewable Energy Systems”, which was closed in April 2022. The selected papers address the above challenges and exemplify the significant benefits that optimisation and nonlinear control techniques can bring to modern power and energy systems

Bee Hive Monitoring System - Solutions for the automated health monitoring

Cerca de um terço da produção global de alimentos depende da polinização das abelhas, tornando-as vitais para a economia mundial. No entanto, existem diversas ameaças à sobrevivência das espécies de abelhas, tais como incêndios florestais, stress humano induzido, subnutrição, poluição, perda de biodiversidade, agricultura intensiva e predadores como as vespas asiáticas. Destes problemas, pode-se observar um aumento da necessidade de soluções automatizadas que possam auxiliar na monitorização remota de colmeias de abelhas. O objetivo desta tese é desenvolver soluções baseadas em Aprendizagem Automática para problemas que podem ser identificados na apicultura, usando técnicas e conceitos de Deep Learning, Visão Computacional e Processamento de Sinal. Este documento descreve o trabalho da tese de mestrado, motivado pelo problema acima exposto, incluindo a revisão de literatura, análise de valor, design, planeamento de testes e validação e o desenvolvimento e estudo computacional das soluções. Concretamente, o trabalho desta tese de mestrado consistiu no desenvolvimento de soluções para três problemas – classificação da saúde de abelhas a partir de imagens e a partir de áudio, e deteção de abelhas e vespas asiáticas. Os resultados obtidos para a classificação da saúde das abelhas a partir de imagens foram significativamente satisfatórios, excedendo os que foram obtidos pela metodologia definida no trabalho base utilizado para a tarefa, que foi encontrado durante a revisão da literatura. No caso da classificação da saúde das abelhas a partir de áudio e da deteção de abelhas e vespas asiáticas, os resultados obtidos foram modestos e demonstram potencial aplicabilidade das respetivas metodologias desenvolvidas nos problemas-alvo. Pretende-se que as partes interessadas desta tese consigam obter informações, metodologias e perceções adequadas sobre o desenvolvimento de soluções de IA que possam ser integradas num sistema de monitorização da saúde de abelhas, incluindo custos e desafios inerentes à implementação das soluções. O trabalho futuro desta dissertação de mestrado consiste em melhorar os resultados dos modelos de classificação da saúde das abelhas a partir de áudio e de deteção de objetos, incluindo a publicação de artigos para obter validação pela comunidade científica.Up to one third of the global food production depends on the pollination of honey bees, making them vital for the world economy. However, between forest fires, human-induced stress, poor nutrition, pollution, biodiversity loss, intensive agriculture, and predators such as Asian Hornets, there are plenty of threats to the honey bee species’ survival. From these problems, a rise of the need for automated solutions that can aid with remote monitoring of bee hives can be observed. The goal of this thesis is to develop Machine Learning based solutions to problems that can be identified in beekeeping and apiculture, using Deep Learning, Computer Vision and Signal Processing techniques and concepts. The current document describes master thesis’ work, motivated from the above problem statement, including the literature review, value analysis, design, testing and validation planning and the development and computational study of the solutions. Specifically, this master thesis’ work consisted in developing solutions to three problems – bee health classification through images and audio, and bee and Asian wasp detection. Results obtained for the bee health classification through images were significantly satisfactory, exceeding those reported by the baseline work found during literature review. On the case of bee health classification through audio and bee and Asian wasp detection, these obtained results were modest and showcase potential applicability of the respective developed methodologies in the target problems. It is expected that stakeholders of this thesis obtain adequate information, methodologies and insights into the development of AI solutions that can be integrated in a bee health monitoring system, including inherent costs and challenges that arise with the implementation of the solutions. Future work of this master thesis consists in improving the results of the bee health classification through audio and the object detection models, including publishing of papers to seek validation by the scientific community

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

On the Application of Multiobjective Optimization to Software Development Process and Antenna Designing

Esta tesis doctoral, presentada como compendio de artículos, explora los beneficios prácticos del uso combinado de la optimización multi-objetivo con aplicaciones de simulación. En esta tesis, con un caracter de aplicación, se aportan ideas prácticas sobre cómo combinar meta-heurísticas aplicadas a la optimización de problemas con herramientas y técnicas de simulación. La simulación permite estudiar problemas complejos antes de implementarlos en el mundo real. Los problemas de optimización son de los más complicados de resolver. Involucran 3 o más variables y en muchos casos no pueden ser resueltos matemáticamente. La simulación permite modelar el problema, pero son una ayuda insuficiente a la hora de encontrar las mejores soluciones a dicho problema. En estos casos, el trabajo conjunto de la herramienta de simulación con metaheurísticas de optimización permiten abordar estos problemas con costes computacionales razonables, obteniendo resultados muy cercanos al óptimo. Debe tenerse en cuenta que las soluciones de los problemas multiobjetivo contienen un conjunto de variables donde habitualmente mejorar (optimizar) una variable, suponga empeorar (hacer menos óptima) otra(s). Por tanto, lo deseable es encontrar un conjunto de soluciones donde cada variable se optimiza teniendo en cuenta el posible impacto negativo en el resto de variables. A ese conjunto de soluciones, se le suele conocer como el Frente de Pareto Óptimo. Esta tesis presenta dos problemas reales, complejos y pertenecientes a campos totalmente distintos, que han sido resueltos de forma existosa, aplicando la misma técnica: Simulación combinada con optimización multiobjetivo. Esta tesis comienza presentando un caso de técnicas de optimización multiobjetivo a través de la simulación para ayudar a los directores de proyectos de software a encontrar las mejores configuraciones para los proyectos basados ITIL (Information Technology Infrastructure Library), de manera que se optimicen las estimaciones de calendario para un proyecto determinado, el tiempo y la productividad. Los datos de gestión de proyectos pueden obtenerse mediante simulación, por ejemplo, para optimizar el número de recursos utilizados en cada fase de la vida del proyecto. También se presenta otro caso de estudio sobre la forma en que la optimización de la simulación puede ayudar en el diseño de cualquier tipo de antena. En este caso de estudio, el objetivo es lograr una antena helicoidal, de doble banda, lo más compacta posible, para la telemetría, el seguimiento y el control (TTC) de los satélites. En los satélites es esencial reducir el volumen y el peso de los dispositivos instalados, manteniendo al mismo tiempo los requisitos de funcionamiento. Adicionalmente, esta tesis realiza un aporte teórico proponiendo un nuevo algoritmo llamado MNDS (Merge Non-Dominated Sorting) que mejora el rendimiento de los algoritmos de optimización multi-objectivo basados en el cálculo del Pareto Front