Port automation and vehicle scheduling advanced algorithms for scheduling problems of AGVs
"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,...
| Otros Autores: | , |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press, Taylor & Francis Group
[2022]
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| Edición: | 3rd ed |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009757939206719 |
Tabla de Contenidos:
- 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.
