Call for Papers on Advanced Optimization Methods for Uncertainties in Intelligent Industrial Systems
Special Issue on Advanced Optimization Methods for Uncertainties in Intelligent Industrial Systems
Theme. As the field of industrial engineering is developing, a higher influence of computerized methodologies such as artificial intelligence, advanced computational models and mathematical programming models are observed in manufacturing, production, and distribution domains. Also, the incorporation of multi-site production and supply chain models are prevailing in this digital era. These distributed intelligent systems are ought to enhance the reliability and resource utilization for achieving an effective task allocation and industrial collaboration into existence. On the other hand, these advancements make the industrial systems more complex with uncertainties. To overcome these challenges, researchers are working towards the development of optimization techniques to permeate all human activities and exhibit a remarkably high diversity by coping with the complexity of problems and applications.
With the rising complexity of problems, optimization has an inherent element of uncertainty that is expressed in many formal ways and this necessitates the need to develop novel techniques to create optimum and interpretable solutions for industrial operations. This special issue aims to discuss the intelligent optimization algorithms including particle swarm optimization, genetic algorithm, differential evolution, distribution estimation, ant colony optimization, artificial bee colony, greedy optimization, and many others, which have been successfully applied to different distributed scheduling problems as well as the real-time industrial systems.
This special issue aims to establish a platform to discuss the recent research progress on the advanced intelligent optimization method for dynamic industrial environments. This special issue will publish novel research works, reviews and real-time applications but not limited to the following fields:
- Distributed industrial operations scheduling and optimization
- Distributed multi-objective optimization
- Granular Computing based optimization models
- Evolutionary computation driven uncertain optimization methods
- Advanced neural networks-based optimization methods
- Industrial data forecasting and optimization models
- Linear and non-linear optimization
- Multi-objective and multi-period optimization
- Novel stochastic and meta-heuristic methods
- Real-time computing and uncertainty analysis
- Real-time applications of distributed machine learning and optimization
- Optimization frameworks for solving inverse problems
- Case studies and real-time applications
Manuscript preparation and submission
Computer Assisted Methods in Engineering and Science (CAMES) is a refereed international journal, published quarterly and indexed by Scopus and EBSCO databases.
All manuscripts must be submitted through the journal website: https://cames.ippt.pan.pl/index.php/cames/about/submissions
Submission must be clearly marked in the system as “Advanced Optimization Methods for Uncertainties in Intelligent Industrial Systems”. The publication in this Special Issue is free of charge for the Authors and the published manuscript will be freely available for the Readers through the Journal website (https://cames.ippt.pan.pl).
Deadline for full-length paper submission: October 30, 2021
Notification of reviewers’ 1st feedback: December 30, 2021
Final manuscript submission: January 30, 2022.
Notification of final decision: March 15, 2022
Dr. I. Jeena Jacob, Associate Professor, Department of CSE, GITAM University, Bangalore Campus, India
Dr. Badrul Hisham bin Ahmad, Associate Professor, Universiti Teknikal Malaysia, Malaysia
Dr. Z. Faizal Khan, College of Computing and Information Technology, Shaqra University, Saudi Arabia
Prof. Michał Kleiber – ECCOMAS President
Prof. Tadeusz Burczyński – Chairman of TC on Computational Solids & Structural Mechanics of ECCOMAS, Director of Institute of Fundamental Technological Research