Quantum Inspired Algorithms for Intelligent Manufacturing and Agro-Forestry Systems

The continuous and exponential advancements in digital technologies and their implementation in industrial manufacturing in the last decade have completely revolutionized conventional manufacturing. The advent of modern technologies like AI (Artificial Intelligence), IoT (Internet of Things), 3D printing, CPS (Cyber-Physical Systems) and DT (Digital Twin), etc., have brought a new era of industrialization known as Industry 4.0 sometimes also referred as 4th industrial revolution. ‘Digital twin’ brings up the idea of smart manufacturing hubs and digital optimization of physical machines, processes, and human workforce.

Owing to intense global competition, recession and rapidly changing customer requirements, manufacturing environments are demanding optimization of processes and resources to maximize their profits and minimize superfluous expenses. Traditional approaches to manufacturing systems cannot cope up with this new situation. Tools that improve organizational agility using optimization techniques and artificial intelligence approaches are required to bring the flexibility and efficiency needed by today's manufacturing systems.

Similarly on account of rising demand and depleting resources there exists a need for optimizing agricultural systems. Keeping in view the depleting forest cover and rapid deterioration of the environment the immediate objective should be to protect the forest cover and enhance the existing productivity levels to deal with the increasing level of population growth, uncertainties, and climatic variables.

In this book, an effort is made to develop optimization techniques that can efficiently be used both in intelligent manufacturing and agricultural systems. More specifically the main objectives of this research are the development and implementation of highly efficient Quantum-inspired Algorithms (QIAs) for the optimization of manufacturing processes and agro-forestry systems. Hence, novel Quantum-inspired Evolutionary Algorithms (QIEA's) are developed which combine the advantages of quantum computing and evolutionary computation for improved characteristics of fast convergence rate and good global search capabilities. The effectiveness of the proposed optimized methodologies is demonstrated with applications to various manufacturing and agro-forestry systems.