An Ant Colony Optimization Algorithm For Load Balancing In Parallel Machines With Sequence Dependent Setup Times
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Applied Systemic Studies
This book is a collection of a wide range of research papers that combine both the humanities and sciences in applied informatics. In particular, it is intended for readers interested in the fields of artificial intelligence, data science, virtual reality, and intelligent systems. Technologies and findings in artificial intelligence, data science, virtual reality, and intelligent systems are being used in all academic disciplines today. This book is a compilation of specific and advanced research findings from a wide range of research fields where they are being applied today. The papers included are based on those presented in August 2022 at the International Conference on Systems Engineering (ICSEng-Tokyo), a prestigious academic conference that has been held annually since 1974. The papers have been rigorously reviewed and selected by multiple peer reviewers.
Proceeding of International Conference on Computational Science and Applications
The book consists of high-quality papers presented at the International Conference on Computational Science and Applications (ICCSA 2019), held at Maharashtra Institute of Technology World Peace University, Pune, India, from 7 to 9 August 2019. It covers the latest innovations and developments in information and communication technology, discussing topics such as soft computing and intelligent systems, web of sensor networks, drone operating systems, web of sensor networks, wearable smart sensors, automated guided vehicles and many more.
An Ant Colony Optimization Algorithm for Load Balancing in Parallel Machines with Sequence-Dependent Setup Times
This study introduces the problem of minimizing average relative percentage of imbalance (ARPI) with sequence-dependent setup times in a parallel-machine environment. A mathematical model that minimizes ARPI is proposed. Some heuristics, and two metaheuristics, an ant colony optimization algorithm and a genetic algorithm are developed and tested on various random data. The proposed ant colony optimizationmethod outperforms heuristics and genetic algorithm. On the other hand, heuristics using the cumulative processing time obtain better results than heuristics using setup avoidance and a hybrid rule in assignment.