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2019 | Book

Computational Methods for Application in Industry 4.0

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About this book

This book presents computational and statistical methods used by intelligent systems within the concept of Industry 4.0. The methods include among others evolution-based and swarm intelligence-based methods. Each method is explained in its fundamental aspects, while some notable bibliography is provided for further reading. This book describes each methods' principles and compares them. It is intended for researchers who are new in computational and statistical methods but also to experienced users.

Table of Contents

Frontmatter
Chapter 1. General Aspects of the Application of Computational Methods in Industry 4.0
Abstract
Since the beginning of the first industrial revolution, engineers were always attempting to resolve problems related to the operation of machinery and their maintenance. They also aimed at the improvement of the efficiency of manufacturing processes and generally at the organization of the production and other relative subjects. As it was anticipated, systematic approaches for the scientific study of industry-related problems were established and the solutions were proposed. However, after the introduction of computers and development of computational methods, a new promising era for solving industry-related problems emerged, as advanced computational techniques were capable of providing approximate but significantly accurate solutions. Especially, when it is desired to increase the efficiency of manufacturing processes by determining the optimum process parameters or when the solution of hard production-based problems, such as scheduling, is required, optimization methods can be employed.
Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
Chapter 2. Evolutionary-Based Methods
Abstract
In the current section, several metaheuristics involving the evolutionary of a population in order to create new generations of genetically superior individuals are presented. These algorithms are usually significantly influenced by the most prominent (and earliest) among them, the Genetic Algorithm (GA). Details about their basic characteristics and function, as well as some important variants, are described and applications in the field of industrial engineering are highlighted. A detailed description of the basic features of the genetic algorithm is presented at the beginning of this chapter and afterwards, other Evolutionary Algorithms (EA) are summarized. In specific, both relatively older and well established, as well as newer but promising methods are included, namely Differential Evolutionary, Memetic Algorithm, Imperialist Competitive Algorithm, Biogeography-Based Optimization algorithm, Teaching-Learning-Based optimization, Sheep Flock Heredity algorithm, Shuffled Frog-Leaping algorithm, and Bacteria Foraging Optimization algorithm.
Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
Chapter 3. Swarm Intelligence-Based Methods
Abstract
The term “Swarm Intelligence” refers directly to the collective behavior of a group of animals, which are following very basic rules, or to an Artificial Intelligence approach, which aims at the solution of a problem using algorithms based on collective behavior of social animals. For over three decades, several algorithms based on the observation of the behavior of groups of animals were developed, such as Particle Swarm Optimization, from the observation of flocks of birds. Some of the most established Swarm Intelligence (SI) methods include the Ant Colony Optimization method, the Harmony Search method and the Artificial Bee Colony algorithm.
Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
Chapter 4. Other Computational Methods for Optimization
Abstract
The last chapter of the present work is dedicated to methods that contain a few or no similarities to the methods presented in the two previous chapters but however, is worth mentioning due to their popularity or promising capabilities in the field of industrial engineering. These methods include Simulated Annealing, Tabu Search, Electromagnetism-like Mechanism, and Response Surface Methodology methods. More specifically, Simulated Annealing method is related to the metallurgical process of annealing and its objective function is related to the reduction of the internal energy of the system, by appropriate variation of its temperature. Tabu Search method exhibits essentially no nature-inspired characteristics, as its basic feature is a list of unacceptable moves, which is used to prevent the solution process to get trapped in a local optimum point. Electromagnetism-like Mechanism is using the natural mechanism of attraction-repulsion in electromagnetism, in order to lead the solution process to the global optimum point.
Nikolaos E. Karkalos, Angelos P. Markopoulos, J. Paulo Davim
Metadata
Title
Computational Methods for Application in Industry 4.0
Authors
Nikolaos E. Karkalos
Dr. Angelos P. Markopoulos
Prof. Dr. J. Paulo Davim
Copyright Year
2019
Electronic ISBN
978-3-319-92393-2
Print ISBN
978-3-319-92392-5
DOI
https://doi.org/10.1007/978-3-319-92393-2

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