Alma Rodriguez – författare

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies.

Book Features:

The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

Metaheuristic algorithms are considered as generic optimization tools that can solve very complex problems characterized by having very large search spaces. Metaheuristic methods reduce the effective size of the search space through the use of effective search strategies.

Book Features:

The material has been written from a teaching perspective and, for this reason, this book is primarily intended for undergraduate and postgraduate students of artificial intelligence, metaheuristic methods, and/or evolutionary computation. The objective is to bridge the gap between metaheuristic techniques and complex optimization problems that profit from the convenient properties of metaheuristic approaches. Therefore, engineer practitioners who are not familiar with metaheuristic computation will appreciate that the techniques discussed are beyond simple theoretical tools, since they have been adapted to solve significant problems that commonly arise in such areas.

This book includes two objectives. The first goal is to present advances and developments which have proved to be effective in their application to several complex problems. The second objective is to present the performance comparison of various metaheuristic techniques when they face complex optimization problems. The material has been compiled from a teaching perspective. Most of the problems in science, engineering, economics, and other areas can be translated as an optimization or a search problem. According to their characteristics, some problems can be simple that can be solved by traditional optimization methods based on mathematical analysis. However, most of the problems of practical importance in engineering represent complex scenarios so that they are very hard to be solved by using traditional approaches. Under such circumstances, metaheuristic has emerged as the best alternative to solve this kind of complex formulations. This book is primarily intended for undergraduate and postgraduate students. Engineers and application developers can also benefit from the book contents since it has been structured so that each chapter can be read independently from the others, and therefore, only potential interesting information can be quickly available for solving an industrial problem at hand. 

This textbook provides readers with a comprehensive exploration of optimization techniques in industrial engineering, with a specific focus on the Economic Order Quantity (EOQ) problem. It strikes a unique balance by thoroughly discussing the underlying concepts and theories, equipping the reader with the knowledge needed to develop their own programs for solving complex optimization problems in the field. A distinctive feature of this book is its extensive use of MATLAB implementations, which serves as a practical tool to bridge the gap between theory and real-world application. The book is structured with the understanding that learning is accelerated when theoretical concepts are complemented by practical, code-based problem-solving examples. This approach is particularly beneficial for students who may have a weaker background in mathematics, as it demonstrates the practicality and effectiveness of optimization in a more accessible manner. The inclusion of ready-made code examples not only makes the subject matter more engaging for students but also encourages them to experiment, modify, and enhance the code with their own ideas. This method of learning is designed to be less daunting and more stimulating, particularly for those who might feel overwhelmed by the prospect of developing complex programs from scratch. The book''s approach is aimed at demystifying the complexities of optimization in industrial engineering, making it more approachable and interesting for students and practitioners alike.

Diverging from other texts that primarily focus on classical techniques for addressing optimization problems in industrial engineering, this book sets itself apart by delving into modern metaheuristic methods. Metaheuristic techniques have gained recognition for their efficacy in tackling complex problems that are often laden with diverse and challenging constraints. These methods, which include algorithms such as simulated annealing, and particleswarm optimization, offer a more dynamic and flexible approach to finding solutions compared to traditional methods. They are particularly adept at navigating vast search spaces and identifying optimal or near-optimal solutions in scenarios where conventional approaches might struggle. This inclusion of metaheuristic methods gives the book a unique quality, providing readers with a comprehensive understanding of both the established foundations and the cutting-edge advancements in the field of optimization. The book''s exploration of these advanced techniques not only broadens the reader''s knowledge base but also equips them with the tools to effectively solve more intricate and nuanced problems encountered in industrial engineering. This dual focus on classical and modern methods positions the book as a valuable and forward-thinking resource in the realm of industrial optimization.