Advancements in Optimization and Nature-Inspired Computing for Solutions in Contemporary Engineering Challenges

This book brings together cutting-edge research, methodologies, and applications in the field of optimization and nature-inspired computing, providing a comprehensive overview of the latest advancements and their applications in addressing contemporary challenges in engineering. The book demonstrates diverse applications of mathematical modeling in various aspects of production, logistic, design, energy, materials, and other engineering areas. The book includes topics in optimization algorithms nature-inspired computing multi-objective optimization hybrid optimization techniques evolutionary algorithms swarm intelligence machine learning for optimization applications of optimization in engineering sustainable engineering solutions big data analytics for optimization metaheuristic approaches cloud computing in optimization cyber-physical systems decision support systems emerging trends in optimization.

Introduction chapter.- Data mining to build the OD matrix in developing countries: preliminary analysis in an Argentinean case study.- Spatial – Temporal Trajectory Mining for sustainable Mobility and Transport.- Multiobjective evolutionary approach for flight planning of an autonomous fleet of unmanned aerial vehicles in exploration and surveillance missions.- Genetic algorithm for generating alternative corridor locations.- An optimization model for the investment portfolio allocation problem: case study.- Continuous User Authentication System using Different Biometrics.- Automation of Student marking system.- Mini review of models and algorithms for the Cell Switch-Off Optimization problem in 5G/6G telecommunications ultradense networks.- Enhancing Human-Computer Interaction through Vision-Based Hand Gesture Recognition: An OpenCV and Keras Approach.- Sustainable Creations: A Holistic Guide to Optimize Digital Footprints and Foster Climate-Conscious Innovation.- Biometric Insights - Harnessing Nature’s Blueprints for Advanced Computing Solutions.- Kalman Filtering Based on Interval Type-2 Evolving Takagi-Sugeno Fuzzy Model Via Multivariable Spectral Analysis of Experimental Data.- A Comparative Analysis of Machine Learning Classifiers in Demand Forecasting.- Optimizing Sorting Algorithms for Small Arrays: A Comprehensive Study of the Chunked Insertion Sort Approach.- Use of Neural Networks to model an inventory system as a family of industrial belts within the supply chain.- A system for making decisions in the subcontracting of transport providers.

Diego Rossit is a full-time adjunct professor at the Universidad Nacional del Sur, Argentina (UNS), and an assistant researcher at CONICET, Argentina. He obtained his Industrial Engineering degree in 2014 and his Ph.D. in Engineering in 2018. His areas of expertise include optimization models and tools, as well as data analysis in industrial engineering applications. These applications encompass the management of transportation systems, urban solid waste management, energy management in households, transportation logistics, production planning, among others. To date, more than 40 indexed papers with an h-index of 11 in Scopus and 27 indexed papers with an h-index of 8 in Web of Science. He has performed research stays at the Universidad de la República (Uruguay), the Australian National University (Australia), the University of Bologna (Italy), the University of Malaga (Spain), the University of Texas at Austin (USA), the Universidad Autónoma del Estado de Morelos (México), and the Universidad Nacional de Asunción (Paraguay).

Carlos Enrique Torres-Aguilar is an associate professor in Mechanical Electrical Engineering Academy of the Engineering and Architecture Academic Division, Universidad Juárez Autónoma de Tabasco, Mexico. In 2023, he obtained a Ph.D. in Mechanical Engineering at the Centro Nacional de Investigación y Desarrollo Tecnológico-TecNM. His research focuses on studying heat transfer mechanisms and fluid mechanics through modeling and simulation of phenomena implementing numerical methods oriented to energy efficiency in buildings and related solar systems.  Among his recent studies is the dynamic modeling of constructive and passive ventilation and thermal comfort systems. He has also worked on optimizing numerical solutions of radiative exchange models with participating mediums. During his career, he has published several scientific articles in journals indexed in the Journal Citations Report (JCR) and has participated in national and international conferences.

Adrián Toncovich is an associate professor at Universidad Nacional del Sur. He obtained his degrees in Industrial Engineering and his master degree in Engineering from the Universidad Nacional del Sur (UNS), Argentina, in 2001, and 2006, respectively, his Advanced Studies Diploma from the University of Zaragoza, Spain, in 2009, and his Diploma of Specialization in Production Logistics from the Zaragoza Logistics Center, Spain, in 2009. He is currently a professor of undergraduate courses in Production Planning and Control in the Department of Engineering at UNS and in the Department of Industrial Organization at the National Technological University. He also teaches graduate-level courses at the aforementioned universities on topics related to operations management and supply chain management. His research interests include: production planning and scheduling, supply chain management, industry 4.0 technologies, and multicriteria decision-making and optimization.