Optimizing Shipping Container Damage Prediction and Maritime Vessel Service Time in Commercial Maritime Ports Through High Level Information Fusion

FieldValue
dc.contributor.authorPanchapakesan, Ashwin
dc.date.accessioned2019-09-09T19:54:43Z
dc.date.available2019-09-09T19:54:43Z
dc.date.issued2019-09-09
dc.identifier.urihttp://hdl.handle.net/10393/39593
dc.identifier.urihttp://dx.doi.org/10.20381/ruor-23836
dc.description.abstractThe overwhelming majority of global trade is executed over maritime infrastructure, and port-side optimization problems are significant given that commercial maritime ports are hubs at which sea trade routes and land/rail trade routes converge. Therefore, optimizing maritime operations brings the promise of improvements with global impact. Major performance bottlenecks in maritime trade process include the handling of insurance claims on shipping containers and vessel service time at port. The former has high input dimensionality and includes data pertaining to environmental and human attributes, as well as operational attributes such as the weight balance of a shipping container; and therefore lends itself to multiple classification method- ologies, many of which are explored in this work. In order to compare their performance, a first-of-its-kind dataset was developed with carefully curated attributes. The performance of these methodologies was improved by exploring metalearning techniques to improve the collective performance of a subset of these classifiers. The latter problem formulated as a schedule optimization, solved with a fuzzy system to control port-side resource deployment; whose parameters are optimized by a multi-objective evolutionary algorithm which outperforms current industry practice (as mined from real-world data). This methodology has been applied to multiple ports across the globe to demonstrate its generalizability, and improves upon current industry practice even with synthetically increased vessel traffic.
dc.language.isoen
dc.publisherUniversité d'Ottawa / University of Ottawa
dc.subjectMachine-learning
dc.subjectArtificial-intelligence
dc.subjectData-fusion
dc.subjectProcess-optimization
dc.titleOptimizing Shipping Container Damage Prediction and Maritime Vessel Service Time in Commercial Maritime Ports Through High Level Information Fusion
dc.typeThesis
dc.contributor.supervisorPetriu, Emil
dc.contributor.supervisorAbielmona, Rami Samih
thesis.degree.namePhD
thesis.degree.levelDoctoral
thesis.degree.disciplineGénie / Engineering
uottawa.departmentScience informatique et génie électrique / Electrical Engineering and Computer Science
CollectionThèses, 2011 - // Theses, 2011 -

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