Evolutionary Computations for Manufacturing

Contents: Preface. 1. Introduction. 2. Genetic algorithm. 3. Particle swarm optimization. 4. Artificial bee colony algorithm. 5. Shuffled frog leaping algorithm. 6. Harmony search algorithm. 7. Simulated annealing algorithm. 8. Teaching learning based optimization.  9. Fuzzy logic applications in optimization. 10. Multi-objective optimization methods. References. Subject index.

In the next generation of Industry-Industry 4.0, the manufacturing systems will be flexible and adaptive in nature. However, it will create new challenges for engineers such as supply chain visibility, inventory optimization. Optimizing planning and scheduling in an integrated manner, real time process optimization, making robots and machines autonomous, fine tuning of product quality, etc. Artificial intelligence (AI) can offer solutions to most of these challenges. Cognitive computing is one of the AI technologies which makes the manufacturing system capable of anticipating new problems, modeling possible solutions and makes decisions by its own. Evolutionary computing being a subset of cognitive computing, its acquaintance is very essential to explore the applications of technological drivers of Industry 4.0. This book therefore provides theoretical concepts and practical applications of several successful evolutionary computational methods such as genetic algorithms, particle swarm optimization, artificial bee colony algorithm, shuffled frog leaping algorithm, simulated annealing algorithm, harmony search algorithm, teaching learning based optimization algorithm, fuzzy optimization and multi-objective optimization. Salient features of this book are: 1. Basic concepts of various evolutionary computational methods are explained in step by step manner through simple examples at the beginning of chapters. 2. Applications of various algorithms are demonstrated through about 20 real life case studies. Most of these case studies are based on the research work of the author and their results are practically implemented and validated. 3. Several variants of each algorithm are also demonstrated through examples.