Modeling, computing and data handling methodologies for maritime transportation / Charalampos Konstantopoulos, Grammati Pantziou, editors

Includes bibliographical references

Foreword; Preface; Contents; Contributors; Abbreviations; 1 Metaheuristic Approaches for the Minimum Cost Hybrid Berth Allocation Problem; 1.1 Introduction; 1.2 Minimum Cost Hybrid Berth Allocation Problem; 1.2.1 Problem Description; 1.2.2 Basic Definitions and Notations; 1.3 Evolutionary Algorithm for MCHBAP; 1.3.1 Representation of Individuals; 1.3.2 Generating Initial Population for EA; 1.3.3 EA Operators; 1.4 Bee Colony Optimization Algorithm for MCHBAP; 1.4.1 Implementation Details; 1.4.2 Improvement Techniques of BCO; 1.5 Variable Neighborhood Search Based Metaheuristic for MCHBAP

1.5.1 Variable Neighborhood Descent for MCHBAP1.5.2 General Variable Neighborhood Search for MCHBAP; 1.6 Experimental Results; 1.7 Conclusion; References; 2 Ship Stability Considerations in the Quay Crane Scheduling Problem; 2.1 Research in Maritime Logistics; 2.2 Ship Stability Consideration; 2.3 Outlook and Future Work; References; 3 Recent Progress Using Matheuristics for Strategic Maritime Inventory Routing; 3.1 Introduction; 3.1.1 Literature Review; 3.1.2 Contributions of this Chapter; 3.2 Problem Description and Formulations

3.2.1 A Discrete-Time Arc-Flow Mixed-Integer Linear Programming Model3.2.2 Additional Valid Inequalities: Mixed Integer Rounding Cuts; 3.3 Matheuristics Explored; 3.3.1 Construction Heuristics; 3.3.2 Improvement Heuristics; 3.3.3 Hybrid Approaches; 3.4 Computational Experiments; 3.4.1 Benchmarking Experiments with Commercial Solvers; 3.4.2 Experiments with Construction Heuristics; 3.4.3 Experiments with Improvement Heuristics; 3.4.4 Experiments with Hybrid Heuristics for Hard Instances Only; 3.4.5 Recommendations for Obtaining Good Solutions Quickly

3.5 Conclusions and Future Research Directions3.6 Nomenclature; 3.7 Appendix; References; 4 Evolutionary Computation for the Ship Routing Problem; 4.1 Introduction; 4.2 Problem Definition; 4.3 Related Work; 4.4 Customization of Evolutionary Algorithms; 4.5 Modelization Issues; 4.6 Computational Results; 4.7 Conclusion; References; 5 Decision Support Tool Employing Bayesian Risk Framework for Environmentally Safe Shipping; 5.1 Introduction; 5.2 Vessel Risk Estimation; 5.2.1 Probability Estimation; 5.2.2 Consequences Estimation; 5.3 Historical Trajectories Preprocessing and Clustering

5.4 Decision Support Tool5.4.1 Description of Tool Functionalities; 5.4.2 Technologies Used; 5.4.3 Data Analytics; 5.5 Decision Support Tool Applications; 5.5.1 Queries in Environmentally Sensitive Areas; 5.5.2 Marine Traffic Monitoring for Environmental Safety; 5.5.3 Influence of Restricted Areas in Marine Traffic; 5.6 Conclusions -- Future Plans; References; 6 A Decision Support System for the Assessment of Seaports' Security Under Fuzzy Environment; 6.1 Introduction; 6.2 Literature Review; 6.2.1 Analysis of Maritime Security Systems/Measures; 6.3 Methodology

This book is one of very few in the maritime literature that solely focus on the latest developments in information technology (IT) methodologies in this field. It provides the reader with a concise overview of how IT can truly improve the efficacy of operations in the maritime industry. It consists of seven chapters that address a range of topics related to the synergy between Computer Science and Maritime Science. Specifically, Chapters 1 and 2 explore two important problems in maritime logistics pertaining to quayside operational planning, while Chapters 3 and 4 focus on maritime routing methodologies. Chapters 5 and 6 present decision-making support systems for safe shipping and port security. Last, Chapter 7 presents simulation methodologies for modeling maritime traffic. The intended readership of the book spans both an academic audience and professionals in the areas of Operational Research, Transportation Science, and Maritime Science interested in applying IT methodologies in their areas of expertise