Title: Optimising commercial port operations through high-level information fusion

Authors: Ashwin Panchapakesan; Rami Abielmona; Emil Petriu

Addresses: School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada ' Research and Engineering, Larus Technologies, Ottawa, Canada ' School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada

Abstract: In order to remain profitable, commercial maritime ports must maintain high throughput of inbound vessels. The gantry cranes that load and unload the vessels are the primary point of interaction between a vessel and the port, which cause a critical bottleneck in the process flow. Errors in this segment of the process cause cascading delays which ultimately cause vessel service backlogs, extending to logistical delays in moving shipping containers across land and rail as well. This is to the detriment of the ports, which lose popularity among shipping lines and may even be fined for causing sub-optimal delays. This work expands on prior work in using a multi-objective genetic algorithm to optimise the parameters of a fuzzy system which controls port-side resource deployment. In contrast to existing solutions, this resource deployer is able to function online and adapt to real-world faults while still maintaining superior performance as compared to industry practice. Further, proposals to expand or reduce the port-side infrastructure are computed.

Keywords: information fusion; multi-objective optimisation; genetic algorithms; fuzzy systems; data mining.

DOI: 10.1504/IJLSM.2021.118756

International Journal of Logistics Systems and Management, 2021 Vol.40 No.2, pp.242 - 265

Received: 22 May 2019
Accepted: 15 Sep 2019
Published online: 04 Nov 2021 *

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