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Computational intelligence for development of strategic decision making in port informational integration

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thesis
posted on 28.03.2022, 23:16 by Ana Ximena Halabi-Echeverry
Cooperation between nations and states can lead to their mutual growth and prosperity. One aspect of integration concerns integration of information that has been the focus of this thesis, specifically, informational integration for ports, in recognition of the important roles that ports play in the nation's and world's economy. The motivation for this thesis has been the advancement of developing countries, particularly in Latin America and specifically in Colombia, the mother country of the author. This thesis focuses on port informational integration and the use of Computational Intelligence to assist knowledge discovery and aid in the complex decision-making modelling. To aid port informational integration and associated decision-making creation of an intelligent decisionmaking support system (i-DMSS) is needed. This thesis proposes the conceptual design, development, and empirical validation of a proof-of-concept prototype of an i-DMSS for port informational integration. To guide the design and validation of the i-DMSS, various strategies and business intelligence processes are presented. Computational Intelligence is implemented to demonstrate its use with a systemic-driven, data-driven and knowledge-driven perspective of the modelling problem. The systemic-driven perspective follows a systems approach towards port´s informational decisions, mainly shaped in this thesis through consideration of port sustainability issues where knowledge from coastal (eco) system and different types of port proximities (spatial and institutional) are essential conditions in becoming port partners for informational integration. The data-driven perspective contributes to the task of assembling datasets from information residing in publicly available repositories, promoting the need for standardised formats and query processing that is increasingly becoming a priority for ICT users. Finally, the knowledge-driven perspective offers to the community and practitioners the ability to learn from the metadata and metafeatures to build intelligent models for port informational integration that support the prototype design for a port-to-port solution, that to the best of the author's knowledge, is the first time for a solution of this type to be offered. Looking to the future implications of this research, the author estimates a new view of these information systems will offer to the port decision makers an opportunity to integrate their information, and informing stakeholders on relevant issues. Benefits can be delivered through cooperation and integration of ports (despite size, capacity, spatial proximity, regulations and jurisdictions/ecosystems). Towards answering to what extent the conceptual design of the i-DMSS for integration is relevant to the port domain, this thesis employs a port cluster perspective where multiple-case studies provide empirical validation to guide future i-DMSS deployment. The multiple-cases describe: 1) an analysis of local (existing or potential) port clusters in the United States (US), b) cross-regional (existing or potential) port clusters in both US (NAFTA-Corridors East, West and Gulf Coasts) and The European Union (EU) (Rijn-Schelde delta Region), and c) institutional port proximities based on jurisdictional mechanisms which represent influential dynamics far from the port borders in the context of Latin America, and specifically in Colombia. Conclusions consider the challenge of building Business Intelligence (BI) Systems for the port domain due to the requirements that need to be met from both the decisional and engineering sides of the system.

History

Table of Contents

Introduction -- Chapter 2. Literature review -- Chapter 3. The i-DMSS methodology for port integration -- Chapter 4. A multiple case study to support empirically port information integration - Chapter 5. Conclusions - Bibliography -- Appendices.

Notes

Bibliography: pages 366-389 Empirical thesis.

Awarding Institution

Macquarie University

Degree Type

Thesis PhD

Degree

PhD, Macquarie University, Faculty of Science and Engineering, Department of Computing

Department, Centre or School

Department of Computing

Year of Award

2016

Principal Supervisor

Deborah Richards

Rights

Copyright Ana Ximena Halabi-Echeveryy 2016. Copyright disclaimer: http://mq.edu.au/library/copyright

Language

English

Extent

1 online resource (539 pages) illustrations (some colour)

Former Identifiers

mq:72373 http://hdl.handle.net/1959.14/1284195