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IEEE Computational Intelligence Society /
Systems, Man & Cybernetics

Ottawa Joint Chapter

 

Next Meetings/Events

Date

Wednesday November 22nd, 2017

Time

6:30 - 6:45 PM  Pizza / drinks and networking

6:45 - 8:15 PM  Technical talk

8:15 - 8:30 PM Q&A and networking

Location

School of Electrical Engineering and Computer Science

Room 5084
University of Ottawa
800 King Edward Ave

registration is required; please register here

Title

Metaheuristic Optimization in Maritime Disruption Management: Big-Data-Enabled Multi-Objective Modeling of Vessel Scheduling Recovery Problem

Speaker

Fatemeh Cheraghchi

 

PhD Student

 

School of Electrical Engineering and Computer Science, University of Ottawa

Abstract

The international seaborne trade constitutes nearly 90% of the volume of global trade and is linked to almost every international supply chain. Thus, it has a major impact on the global economy.  Due to limited differentiation of services, the main competition between stakeholders in this industry is cost-based. Therefore, in order to be able to sustain competitiveness, efficient and optimized services should reduce the cost and present reliable services. However, such services are vulnerable to many uncertainty factors. Disruption management refers to dynamically recovering from various disruption events that prevent the original operational plan from being seamlessly executed.  

In this talk, we will introduce a multi-objective modeling of the Speed-based Vessel Schedule Recovery Problem (S-VSRP) to mitigate the impact of disruptions in a vessel’s schedule by adjusting the vessel’s speed along the route. Automatic Identification System (AIS) real-world data is brought to S-VSRP in order to turn it into a Big-Data-enabled optimization problem.  We propose meta-heuristic optimization methods to find Pareto-optimal solutions. The main decision objectives are concerned with the minimization of the total loss and delay while maximizing the compliance with historical navigational patterns. Several evolutionary multi-objective optimizers are utilized to approximate the Pareto-optimal solutions providing the vessel voyage speeds. The Pareto front gives the stakeholders the ability to inspect the tradeoff among these three conflicting objectives.


Speaker Biography

Fatemeh Cheraghchi is a Ph.D. student in Computer Science at the School of Electrical Engineering and Computer Science of the University of Ottawa. Previously, she attained her Bachelor and MSc degrees in Computer Science from the University of Tehran, Iran. She currently works as a Research Assistant at Larus Technologies Corporation, an Ottawa-based firm that specializes in high-level information fusion and decision support from a Computational Intelligence angle and for the Knowledge Discovery and Data (KDD) lab at the University of Ottawa on the NSERC project entitled “Big Data Analytics for Maritime Internet of Things”. Her research interests are in the area of data mining, machine learning, evolutionary computing, and algorithm design, especially with Big Data applications.

 

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