Paper Info
Title
Big-Data-Enabled Modelling And Optimization Of Granular Speed-Based Vessel Schedule Recovery Problem
Abstract
The Automatic Identification System (AIS) is a vessel tracking system that automatically provides updates on a vessel's movement and other relevant voyage data to vessel traffic management centres and operators. Aside from assisting in real-time tracking and monitoring marine traffic, this system is used in the analysis of historical navigation patterns. In this work, we mined and aggregated vessel speeds from AIS messages within geohashed regions at different precision levels. This granulated, real-world information was brought into the formulation of a Speed-based Vessel Schedule Recovery Problem (S-VSRP). The goal is to mitigate disruptions in vessel schedule by adjusting the speeds while also conforming to the historical navigation patterns reflected in the AIS data. We introduce a new model for vessel schedule speed recovery problem by formulating it as a multi-objective optimization (MOO) problem called the Big-Data-enabled Granular S-VSRP (G-S-VSRP) and propose meta-heuristic optimization methods to find Pareto-optimal solutions. The three objectives are: (1) minimizing the total delay between origin and destination ports, (2) minimizing total financial loss, and (3) maximizing the average speed compliance with historical speed limits. Three evolutionary multi-objective optimizers (EMOO) were investigated and utilized to approximate the Pareto-optimal solutions providing vessel voyage speeds. The Pareto front gives the ability to inspect the tradeoff among the three conflicting objectives. To the best of our knowledge, this is the first time historical AIS data has been exploited in the published literature to mitigate disruptions in vessel schedules.
Year
Venue
Keywords
2017
2017 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA)
Geohashed speed mining, Evolutionary algorithm, Multi-objective optimization, Disruption Management, Vessel Schedule Recovery Problem, Meta-heuristic, Automatic Identification System (AIS)
Field
DocType
ISSN
Data mining,Port (computer networking),Computer science,Tracking system,Real-time computing,Multi-objective optimization,Schedule,Operator (computer programming),Automatic Identification System,Big data
Conference
2639-1589
Citations 
PageRank 
References 
1
0.35
0
Authors
6
Name
Order
Citations
PageRank
Fatemeh Cheraghchi152.44
Ibrahim Y. Abualhaol2218.32
Rafael Falcon311316.51
Rami S. Abielmona45013.83
Bijan Raahemi515522.29
Emil M. Petriu696492.56