Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
8-2019
Abstract
Maritime navigational safety is of utmost importance to prevent vessel collisions in heavily trafficked ports, and avoid environmental costs. In case of a likely near miss among vessels, port traffic controllers provide assistance for safely navigating the waters, often at very short lead times. A better strategy is to avoid such situations from even happening. To achieve this, we a) formalize the decision model for traffic hotspot mitigation including realistic maritime navigational features and constraints through consultations with domain experts; and b) develop a constraint programming based scheduling approach to mitigate hotspots. We model the problem as a variant of the resource constrained project scheduling problem to adjust vessel movement schedules such that the average delay is minimized and navigational safety constraints are also satisfied. We conduct a thorough evaluation on key performance indicators using real world data, and demonstrate the effectiveness of our approach in mitigating high-risk situations.
Discipline
Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence 2019: Macau, August 10-16
First Page
5794
Last Page
5800
Identifier
10.24963/ijcai.2019/803
Publisher
IJCAI
City or Country
Macau
Citation
BHATNAGAR, Saumya; KUMAR, Akshat; and LAU, Hoong Chuin.
Decision making for improving maritime traffic safety using constraint programming. (2019). Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence 2019: Macau, August 10-16. 5794-5800.
Available at: https://ink.library.smu.edu.sg/sis_research/4683
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.24963/ijcai.2019/803
Included in
Computer Sciences Commons, Operations Research, Systems Engineering and Industrial Engineering Commons