Title: A ship performance and genetic algorithm-based decision support system for vessel speed optimisation of ocean route
Authors: Yanfei Zhang; Jipan Qiao
Addresses: State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute, Shanghai, China ' State Key Laboratory of Navigation and Safety Technology, Shanghai Ship and Shipping Research Institute, Shanghai, China
Abstract: The speed optimisation of operating ships contributes to reducing fuel costs and the emission of greenhouse gas (GHG). Therefore, it is a managerial problem to determine an optimal speed that helps minimise fuel consumption while considering various navigation restrictions that conform to navigation habits. Though most literary works study empirical formulas or historical navigation data to estimate fuel consumption under different weather conditions, these methods are subject to various limitations due to the unique performance of different vessels. Based on the still water model test data, this paper proposes a performance-based decision support system (DSS) for speed optimisation, which considers the impact of weather conditions and the specific fuel oil consumption (SFOC) rate value of the main engine on fuel consumption. The speed optimisation DSS applies a genetic algorithm (GA) to minimise fuel consumption and satisfy practical purposes. The ship-shore data exchange-based DSS can promptly provide an operating vessel with speed optimisation suggestions. The effectiveness of the speed optimisation DSS is validated through the implications on the operational ship, and its accuracy is tested by comparing the collected data on two target voyages.
Keywords: speed optimisation; genetic algorithm; sustainable maritime logistics; ship performance; shipping management.
DOI: 10.1504/IJSTL.2023.132650
International Journal of Shipping and Transport Logistics, 2023 Vol.17 No.1/2, pp.107 - 145
Received: 25 Apr 2021
Accepted: 05 Apr 2022
Published online: 07 Aug 2023 *