Vehicle Scheduling in Port Automation : Advanced Algorithms for Minimum Cost Flow Problems, Second Edition book cover
2nd Edition

Vehicle Scheduling in Port Automation
Advanced Algorithms for Minimum Cost Flow Problems, Second Edition

ISBN 9780367575595
Published June 30, 2020 by CRC Press
259 Pages

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Book Description

Container terminals are constantly being challenged to adjust their throughput capacity to match fluctuating demand. Examining the optimization problems encountered in today’s container terminals, Vehicle Scheduling in Port Automation: Advanced Algorithms for Minimum Cost Flow Problems, Second Edition provides advanced algorithms for handling the scheduling of automated guided vehicles (AGVs) in ports.

The research reported in this book represents a complete package that can help readers address the scheduling problems of AGVs in ports. The techniques presented are general and can easily be adapted to other areas.

This book is ideal for port authorities and researchers, including specialists and graduate students in operation research. For specialists, it provides novel and efficient algorithms for network flow problems. For students, it supplies the most comprehensive survey of the field along with a rigorous formulation of the problems in port automation.

This book is divided into two parts. Part one explores the various optimization problems in modern container terminals. The second part details advanced algorithms for the minimum cost flow (MCF) problem and for the scheduling problem of AGVs in ports.

The book classifies optimization problems into five scheduling decisions. For each decision, it supplies an overview, formulates each of the decisions as constraint satisfaction and optimization problems, and then covers possible solutions, implementation, and performance.

The book extends the dynamic network simplex algorithm, the fastest algorithm for solving the minimum cost flow problem, and develops four new advanced algorithms. In order to verify and validate the algorithms presented, the authors discuss the implementation of the algorithm to the scheduling problem of AGVs in container terminals.

Table of Contents

Introduction. Problems in Container Terminals. Formulations of the Problems and Solutions. Vehicle Scheduling: A Minimum Cost Flow Problem. Network Simplex: The Fastest Algorithm. Network Simplex Plus: Complete Advanced Algorithm. Dynamic Network Simplex: Dynamic Complete Advanced Algorithm. Greedy Vehicle Search: An Incomplete Advanced Algorithm. Multi-Load and Heterogeneous Vehicle Scheduling: Hybrid Solutions. Conclusions and Future Research. Appendix: Information on the Web.

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Hassan Rashidi earned a BSc in computer engineering in 1986 as well as an MSc in systems engineering and planning in 1989 with the highest honors at Isfahan University of Technology, Iran. He joined the Department of Computer Science at the University of Essex in the United Kingdom, as a PhD student in October 2002 and earned his PhD in 2006. He was a researcher in the British Telecom research centre in United Kingdom in 2005. He is currently an associate professor at Allameh Tabataba’i University, Tehran, Iran, and a visiting academic at the University of Essex. He is an international expert in the applications of the network simplex algorithm to automated vehicle scheduling and has published many conference and journal papers.

Edward Tsang
has a first degree in business administration (major in finance) and an MSc and PhD in computer science. He has broad interests in applied artificial intelligence, particularly constraint satisfaction, computational finance, heuristic search, and scheduling. He is currently a professor at the School of Computer Science and Electronic Engineering at the University of Essex, where he leads the computational finance group and the constraint satisfaction and optimization group. He is also the director of the Centre for Computational Finance and Economic Agents, an interdisciplinary center. He founded the Technical Committee for Computational Finance and Economics under the IEEE Computational Intelligence Society.