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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Professor Said Aqil obtained an aggregation in mechanical engineering from the Hight Normal school of sciences and technology in Rabat, Morocco, in 1998. He obtained his graduate degree in industrial engineering at the Mohammadia School of Engineers in Rabat. He then completed a PhD in Applied Mathematics and Industrial Engineering at the Mohammedia Faculty of Science and Technology. Currently, he is a professor of industrial engineering at the National School of Arts and Crafts in Casablanca, Morocco. His scientific contributions focus on scheduling, metaheuristics, mathematical modeling, and industrial optimization. Throughout his career, Professor AQIL has conducted in-depth research aimed at enhancing the efficiency and performance of industrial production systems. His work particularly addresses complex scheduling problems, which are critical in modern production environments where resource management and time constraints play a crucial role. He specializes in developing and applying advanced metaheuristic algorithms, such as genetic algorithms, simulated annealing, and artificial bee colony algorithm, to solve these challenges efficiently. By combining mathematical modeling with algorithmic approaches, he has significantly contributed to improving optimization methodologies in industrial engineering His work has been published in a number of international scientific journals, including “expert system with application” and “Engineering Applications of Artificial Intelligence”, providing innovative solutions to the challenges of scheduling and production management. At the same time, he plays an active role in the training and supervision of doctoral students, promoting the advancement of industrial optimization research. Additionally, he actively participates in graduate student supervision and research collaboration, fostering advancements in industrial optimization and mathematical modeling.

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