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1. Complexity in Real-Life Transportation Problems
Part 1: Rich Vehicle Routing Problems: Challenges and Algorithms
2. Economic and Environmental Impacts
3. Serial and Parallel Algorithms
4. Metaheuristic Algorithms
5. Bio-Inspired Algorithms
6. Hybrid Algorithms
7. Heuristic Algorithms
Part 2: Smart Delivery Systems: Challenges, Algorithms, and Applications
9. Machine Learning
10. Advanced Data Analysis
12. Smart Delivery Systems Practical Applications
Smart Delivery Systems: Solving Complex Vehicle Routing Problems examines both exact and approximate methods for delivering optimal solutions to rich vehicle routing problems, showing both the advantages and disadvantages of each approach. It shows how to apply machine learning and advanced data analysis techniques to improve routing systems, familiarizing readers with the concepts and technologies used in successfully implemented delivery systems. The book explains both the latest theoretical and practical advances in intelligent delivery and scheduling systems and presents practical applications for designing new algorithms for real-life scenarios.
- Emphasizes both sequential and parallel algorithms
- Uniquely combines methods and algorithms, real-life applications, and parallel computing
- Includes recommendations on how to choose between different methods for solving applications
- Provides learning aids, end of chapter references, bibliography, worked examples and exercises
Transportation and Computer Science researchers, graduate students, and professors. Transportation Planners, software developers, Operational Research Engineers
- No. of pages:
- © Elsevier 2020
- 21st November 2019
- Paperback ISBN:
- eBook ISBN:
Jakub Nalepa is an Assistant Professor in the Department of Automatic Control, Electronics and Computer Science at Silesian University of Technology. His interdisciplinary research encompasses academic and industry applications for vehicle routing optimization, machine learning, evolutionary algorithms, pattern recognition, and parallel computing.
Assistant Professor, Senior Research Scientist in Machine Learning, Evolutionary Computation, Deep Learning