Content: IntroductionObjectives Optimization in Ports Scheduling of AGVs and Development of Advanced Algorithms Structure of Subsequent Chapters Problems in Container Terminals Compartments Operations Decisions to Be Made Allocation of Berths to Arriving Vessels and QCs to Docked Vessels Storage Space Assignment RTGC Deployment Scheduling and Routing of Vehicles Appointment Times to External Trucks Formulations of the Problems and Solutions Allocation of Berths to Arriving Vessels and QCs to Docked Vessels Assumptions Decision Variables and Domains Constraints Objective FunctionStorage Space Assignment Assumptions Decision Variables and Domains Constraints Objective Function RTGC Deployment Assumptions Decision Variables and Domains Constraints Objective Function Scheduling and Routing of Vehicles Assumptions Decision Variables and Domains Constraints Objective Function Appointment Times to External Trucks Assumptions Decision Variables and Domains Constraints Objective Function Container Terminals over the World, a Survey Survey on Simulation, Implementation, Solution Methods, and Evaluation Simulation and Setting the Parameters Selecting an Architecture Solution Methods, a Survey Evaluation and Monitoring Summary and Conclusion Vehicle Scheduling: A Minimum Cost Flow Problem Reasons to Choose This Problem Assumptions Variables and Notations MCF Model Graph Terminology Standard Form of the MCF Model Applications of the MCF Model Special Case of the MCF Model for AGV Scheduling Nodes and Their Properties in the Special Graph Arcs and Their Properties in the Special Graph MCF-AGV Model for the AGV Scheduling Summary and ConclusionNetwork Simplex: The Fastest Algorithm Reasons to Choose NSA Network Simplex AlgorithmSpanning Tree Solutions and Optimality Conditions Steps of NSA Difference between NSA and Original Simplex Short Literature over Pricing Rules Strongly Feasible Spanning Tree Simulation Software Features of Our Software Implementation of NSA in Our Software How the Program Works Circulation Problem Experimental Results Estimate of the Algorithm's Complexity in Practice Limitation of the NSA in Practice Summary and Conclusion Network Simplex Plus: Complete Advanced Algorithm MotivationNetwork Simplex Plus Algorithm Anti-Cycling in NSA+Memory Technique and Heuristic Approach in NSA+ Differences between NSA and NSA+ Comparison between NSA and NSA+ Statistical Test for the Comparison Complexity of NSA+ Software Architecture for Dynamic Aspect Experimental Results from the Dynamic Aspect Summary and Conclusion Dynamic Network Simplex: Dynamic Complete Advanced Algorithm Motivation Classification of Graph Algorithms and Dynamic Flow Model Dynamic Network Simplex Algorithm Data Structures Memory Management DNSA and DNSA+ Software Architecture for Dynamic Aspect Comparison between DNSA+ and NSA+Statistical Test for the ComparisonComplexity of the Algorithm Summary and Conclusion Greedy Vehicle Search: An Incomplete Advanced Algorithm Motivation Problem Formalization Nodes and Their Properties in the Incomplete Graph Arcs and Their Properties in the Incomplete Graph Special Case of the MCF-AGV Model for AGV Scheduling Algorithm Formalization Software Architecture for Dynamic Aspect Comparison between GVS and NSA+ and Quality of the Solutions Statistical Test for the Comparison Complexity of GVS Complexity of GVS for Static Problems Complexity of GVS for Dynamic Problems Discussion over GVS and Meta-Heuristic Summary and Conclusion Multi-Load and Heterogeneous Vehicle Scheduling: Hybrid Solutions Motivation Assumptions and Formulation Assumptions Formulation Decision Variable Constraints and Objective Function Solutions to the ProblemSAM for the Multi-Load AGVs Hybrid of SAM and NSA for Heterogeneous AGVs Experimental Results Summary and Conclusion Conclusions and Future Research Summary of This Research Done Observations and Conclusions Research ContributionsFuture Research Scheduling and Routing of the Vehicles Economic and Optimization Model Other Possible Extension Appendix: Information on the Web References Index