Port automation and vehicle scheduling : advanced algorithms for scheduling problems of AGVs / Hassan Rashidi, Edward P.K. Tsang.

Format:

Book

Author/Creator:

Rashidi, Hassan, author.

Tsang, Edward P. K., author.

Language:

English

Subjects (All):

Automated guided vehicle systems.

Container terminals.

Transportation, Automotive--Dispatching.

Transportation, Automotive.

Harbors.

Physical Description:

1 online resource (305 pages)

Edition:

3rd ed.

Place of Publication:

Boca Raton, FL : CRC Press, Taylor & Francis Group, [2022]

Summary:

"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, Port Automation and Vehicle Scheduling, Advanced Algorithms for Scheduling Problems of AGVs, Third Edition provides advanced algorithms for handling the scheduling of Automated Guided Vehicles (AGVs) in ports. Building on earlier editions, previously entitled Vehicle Scheduling in Port Automation: Advanced Algorithms for Minimum Cost Flow Problems, this book includes two entirely new chapters and has been revised throughout. New material addresses the solutions to the modelling of decisions in Chapter 3, while in Chapter 11 the authors address an emerging challenge in automated container terminals with integrated management. This is a valuable reference 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"-- Provided by publisher.

Contents:

Cover

Half Title

Title Page

Copyright Page

Contents

List of Figures

List of Tables

List of Abbreviations

Preface

Acknowledgments

Authors

1. Introduction

1.1. Objectives

1.1.1. Optimization in Ports

1.1.2. Scheduling of AGVs and Development of Advanced Algorithms

1.2. Structure of Subsequent Chapters

PART 1: OPTIMIZATION PROBLEMS FACING MODERN CONTAINER TERMINALS

2. Problems in Container Terminals

2.1. Compartments

2.2. Operations

2.3. Decisions to Be Made

2.3.1. Allocation of Berths to Arriving Vessels and QCs to Docked Vessels

2.3.2. Storage Space Assignment

2.3.3. Rubber Tyred Gantry Crane Deployment

2.3.4. Scheduling and Routing of Vehicles

2.3.5. Appointment Times to XTs

3. Formulations of the Problems

3.1. Allocation of Berths to Arriving Vessels and Quay Cranes to Docked Vessels

3.1.1. Assumptions

3.1.2. Decision Variables and Domains

3.1.3. Constraints

3.1.4. Objective Function

3.2. Storage Space Assignment

3.2.1. Assumptions

3.2.2. Decision Variables and Domains

3.2.3. Constraints

3.2.4. Objective Function

3.3. Rubber Tyred Gantry Crane Deployment

3.3.1. Assumptions

3.3.2. Decision Variables and Domains

3.3.3. Constraints

3.3.4. Objective Function

3.4. Scheduling and Routing of Vehicles

3.4.1. Assumptions

3.4.2. Decision Variables and Domains

3.4.3. Constraints

3.4.4. Objective Function

3.5. Appointment Times to eXternal Trucks

3.5.1. Assumptions

3.5.2. Decision Variables and Domains

3.5.3. Constraints

3.5.4. Objective Function

3.6. Container Terminals over the World: A Survey

3.7. Summary and Conclusion

4. Solutions to the Decisions: Review and Suggestions

4.1. Simulation of Container Terminals

4.2. Selecting an Architecture

4.3. Classification of Scheduling Methods.

4.4. Frameworks for Optimization and Scheduling Problems

4.5. Solution Methods for Vehicle Problems, Developed before 2000

4.6. Solution Methods for Vehicle Problems, Developed in the Twenty-First Century

4.7. Suggestions for How to Do the Simulation

4.7.1. Microscopic Simulation

4.7.1.1. Entities

4.7.1.2. Resources

4.7.1.3. Control Elements

4.7.1.4. Operations

4.7.2. Macroscopic Simulation

4.7.2.1. Agent-Based Simulation (ABS)

4.7.2.2. Object-Based Simulation (OBS)

4.8. Proposed Frameworks for Implementation

4.9. Evaluation and Monitoring

4.10. Summary and Conclusion

PART 2: ADVANCED ALGORITHMS FOR THE SCHEDULING PROBLEM OF AUTOMATED GUIDED VEHICLES

5. Vehicle Scheduling: A Minimum Cost Flow Problem

5.1. Reasons to Choose This Problem

5.2. Assumptions

5.3. Variables and Notations

5.4. The Minimum Cost Flow Model

5.4.1. Graph Terminology

5.4.2. The Standard Form of the Minimum Cost Flow Model

5.4.3. Applications of the Minimum Cost Flow Model

5.5. The Special Case of the MCF Model for Automated Guided Vehicles Scheduling

5.5.1. Nodes and Their Properties in the Special Graph

5.5.2. Arcs and Their Properties in the Special Graph

5.5.3. The MCF-AGV Model for the Automated Guided Vehicles Scheduling

5.6. Summary and Conclusion

6. Network Simplex: The Fastest Algorithm

6.1. Reasons to Choose NSA

6.2. The Network Simplex Algorithm

6.2.1. Spanning Tree Solutions and Optimality Conditions

6.2.2. The Algorithm NSA

6.2.3. The Difference between NSA and Original Simplex

6.2.4. A Literature over Pricing Rules

6.2.5. Strongly Feasible Spanning Tree

6.3. Simulation Software

6.3.1. The Features of Our Software

6.3.2. The Implementation of NSA in Our Software

6.3.3. How the Program Works

6.3.4. The Circulation Problem.

6.4. Experimental Results

6.5. An Estimate of the Algorithm's Complexity in Practice

6.6. Limitation of the NSA in Practice

6.7. Summary and Conclusion

7. Network Simplex Plus: Complete Advanced Algorithm

7.1. Motivations

7.2. The Network Simplex Plus Algorithm (NSA+)

7.2.1. Anti-Cycling in NSA+

7.2.2. Memory Technique and Heuristic Approach in NSA+

7.2.3. The Differences between NSA and NSA+

7.3. A Comparison between NSA and NSA+

7.4. Statistical Test for the Comparison

7.5. Complexity of Network Simplex Plus Algorithm (NSA+)

7.6. Software Architecture for Dynamic Aspect

7.7. Experimental Results from the Dynamic Aspect

7.8. Summary and Conclusion

8. Dynamic Network Simplex: Dynamic Complete Advanced Algorithm

8.1. Motivations

8.2. Classification of Graph Algorithms and Dynamic Flow Model

8.3. The Dynamic Network Simplex Algorithm

8.3.1. Data Structures

8.3.2. Memory Management

8.3.3. The Algorithms DNSA and DNSA+

8.4. Software Architecture for Dynamic Aspect

8.5. A Comparison between DNSA+ and NSA+

8.6. Statistical Test for the Comparison

8.7. Complexity of the Algorithm

8.8. Summary and Conclusion

9. Greedy Vehicle Search: An Incomplete Advanced Algorithm

9.1. Motivations

9.2. Problem Formalization

9.2.1. Nodes and Their Properties in the Incomplete Graph

9.2.2. Arcs and Their Properties in the Incomplete Graph

9.2.3. The Special Case of the MCF-AGV Model for Automated Guided Vehicles Scheduling

9.3. Algorithm Formalization

9.4. Software Architecture for Dynamic Aspect

9.5. A Comparison between GVS and NSA+ and Quality of the Solutions

9.6. Statistical Test for the Comparison

9.7. Complexity of Greedy Vehicle Search

9.7.1. Complexity of GVS for Static Problem

9.7.2. Complexity of GVS for Dynamic Problem.

9.8. A Discussion over GVS and Meta-Heuristic

9.9. Summary and Conclusion

10. Multi-Load and Heterogeneous Vehicles Scheduling: Hybrid Solutions

10.1. Motivation

10.2. Assumptions and Formulation

10.2.1. Assumptions

10.2.2. Formulation

10.2.3. Decision Variable

10.2.4. Constraints and Objective Function

10.3. Solutions to the Problem

10.3.1. Simulated Annealing Method for the Multi-Load AGVs

10.3.2. The Hybrid of SAM and NSA for Heterogeneous AGVs

10.4. Experimental Results

10.5. Summary and Conclusion

11. Integrated Management of Equipment in Automated Container Terminals

11.1. Introduction

11.2. Motivations

11.3. Related Works over Automated Container Terminals

11.4. Problem Description and Modeling

11.4.1. Complexity of the Problem

11.4.2. Problem Formulation

11.5. The Proposed Method

11.5.1. Chromosome

11.5.2. Crossover Operator

11.5.3. Mutation Operator

11.6. Simulation and Evaluation of the Proposed Method

11.6.1. Parameters

11.6.2. Numerical Experiments

11.7. Summary and Conclusions

12. Conclusions and Future Research

12.1. Summary of This Research Done

12.2. Observations and Conclusions

12.3. Research Contributions

12.4. Future Research

12.4.1. Scheduling and Routing of the Vehicles

12.4.2. Economic and Optimization Model

12.4.3. Automated Container Terminal

12.4.4. The Next Generation of Container Terminal

Appendix: Information on Web

Overview of This Research

Assumptions

Development

Some Interfaces of Our Software

References

Index.

Notes:

Description based upon print version of record.

Description based on print version record.

ISBN:

9781003308386

1003308384

9781000629309

1000629309

9781000629354

100062935X

OCLC:

1338675901