Overview

Distributed manufacturing systems have become increasingly complex, requiring more sophisticated approaches to manage the challenges of scheduling. Traditional methods have fallen short in handling the dynamic and large-scale challenges in these manufacturing environments. Using metaheuristic algorithms such as genetic algorithms, particle swarm optimization, and hybrid approaches offer a powerful solution in optimizing scheduling tasks. These advanced techniques can enhance flexibility and responsiveness. Advanced Metaheuristics for Scheduling in Distributed Manufacturing Systems provides an in-depth examination of advanced metaheuristic algorithms, addressing fundamental theoretical concepts, emerging challenges, and practical case studies that illustrate their real-world applicability. Special attention is given to how these techniques enhance decision-making in distributed settings, improve resource allocation, and adapt to dynamic production constraints. Covering topics such as artificial intelligence, manufacturing schedules, and supply chain optimization, this book is an excellent resource for researchers, academicians, industry practitioners, engineers, data scientists, graduate and postgraduate students, and more.

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