Route Optimization Techniques
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Route and Operating Optimization of Maritime Vessels Using Machine Learning Techniques
Author: Mohammad Hossein Moradi
language: en
Publisher: Logos Verlag Berlin GmbH
Release Date: 2024-02-02
The shipping industry handles over 90% of the global trade volume and is responsible for approximately 3% of global CO2 emissions. Meanwhile, trade by the shipping industry is expected to increase by up to 130% by 2050 compared to 2008. At the same time, the goal is to reduce Green House Gas (GHG) emissions from the shipping industry to half of the 2008 level by 2050. In support of this goal, this thesis is concerned with a comprehensive approach for optimizing the ship's operation, i.e., an optimization approach that simultaneously involves route selection, energy management, propeller pitch, and engine control. In addition, this thesis also analyses the application of wind propulsion systems. The optimization of the ship's operation is implemented in the form of Reinforcement Learning (RL) methods. The use of RL-based methods to simultaneously optimize various aspects of the ship's trajectory and controls is a novel approach compared to the current state-of-art and embodies this thesis' inherent innovation. The results specifically highlight the importance of parallelizing route optimization with the optimization of other control aspects. Ultimately, it is found that the solution emanating from a purely RL-based approach can be further enhanced when the optimized route, speed, and power profiles are used to perform individual DP-based optimizations on the energy management in a post-processing step.
Distributed Computing and Optimization Techniques
This book introduces research presented at the International Conference on Distributed Computing and Optimization Techniques (ICDCOT–2021), a two-day conference, where researchers, engineers, and academicians from all over the world came together to share their experiences and findings on all aspects of distributed computing and its applications in diverse areas. The book includes papers on distributed computing, intelligent system, optimization method, mathematical modeling, fuzzy logic, neural networks, grid computing, load balancing, communication. It will be a valuable resource for students, academics, and practitioners in the industry working on distributed computing.
Multi-objective Optimization Techniques
The book establishes how to design, develop, and test different hybrids of multi-objective optimization algorithms. It presents several application areas of multi-objective optimization algorithms. Presents a thorough analysis of equations, mathematical models, and mechanisms of multi-objective optimization algorithms. Explores different alternatives of multi-objective optimization algorithms to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems. Illustrates how to design, develop, and test different hybrids of multi-objective optimization algorithms. Discusses multi-objective optimization techniques for cloud, fog, and edge computing. Highlights applications of multi-objective optimization in diverse sectors such as engineering, e-healthcare, and scheduling. The text is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics, communications engineering, computer science and engineering, and mathematics.