Voyage optimization algorithms for ship safety and energy-efficiency
Licentiatavhandling, 2018

Currently, over 90% of the world’s trade is transported by sea. The environmental impacts from shipping and societal challenges of human and property losses caused by ship accidents are pressuring the shipping industry for more energy efficiency and enhanced safety. In the maritime community, voyage optimization systems are recognized as one of the most effective measures that can contribute to the sustainability of the maritime sector. A voyage optimization system can provide an optimized ship route for an expected time of arrival (ETA) with well-planned waypoints and sailing speeds for a specific voyage, minimizing fuel consumption and structural damage due to vibrations. The optimization procedure accounts for reliable meteorological and oceanographic (MetOcean) forecasts and exploits accurate ship’s performance models, which can describe the ship’s speed-power relationship, motion and structural response, etc. in terms of various MetOcean and operational conditions.


Various optimization algorithms are available to provide route planning services in the shipping market. However, these algorithms often contain large uncertainties, leading to a large scatter of the recommended optimal route solutions. Furthermore, these algorithms focus on simple voyage optimization problems, e.g., maintaining a fixed ship speed during the entire voyage, and their results may be impractical for actual ship operation. Moreover, most of the algorithms focus on single-objective optimization. Therefore, the main goals of this thesis are to 1) study the uncertainties and sensitivities of various conventional routing optimization algorithms, 2) analyse the benefits of these algorithms for ship safety and fuel consumption, and 3) propose a more sophisticated voyage optimization algorithm to provide a globally optimal ship route plan to guide actual ship operation.


In this study, five conventional voyage optimization algorithms, categorized as either dynamic grid based methods or static grid based methods, are benchmarked to identify their advantages and disadvantages and their relationships. The benefits of using various optimization algorithms to reduce crack propagation in ship structures are investigated. It is concluded that crack propagation can be reduced more than 60% by applying a voyage optimization algorithm. Finally, a hybrid of Dijkstra’s algorithm and a genetic optimization algorithm is proposed to search for globally optimum solutions for routes in a 3D graph. It was found that the hybrid algorithm can solve multi-objective route optimizations, helping ships avoid multiple severe sea conditions. The algorithm can additionally provide optimum route suggestions, allowing for both voluntary ship speed and power variation along a route. It is concluded that the algorithm can design optimum ship routes with the lowest fuel costs while ensuring an accurate expected time of arrival.

Energy efficiency

Dijkstra’s algorithm

Voyage optimization algorithms

Genetic algorithm

Ship safety

Expected time of arrival

Alfa hall, Hörselgången4, Chalmers
Opponent: Prof. Krzysztof Podgórski, Department of Statistics, Lund University, Sweden


Helong Wang

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Voyage optimization for mitigating ship structural failure due to crack propagation

Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability,; Vol. 233(2019)p. 5-17

Artikel i vetenskaplig tidskrift

Wang, H., Mao, W., and Eriksson, L. E. ,Benchmark study of five optimization algorithms for weather routing (2017)

Wang, H., Mao, W., and Eriksson, L. E. ,MetOcean data drived voyage optimization using genetic algorithm (2018)



Marin teknik




Alfa hall, Hörselgången4, Chalmers

Opponent: Prof. Krzysztof Podgórski, Department of Statistics, Lund University, Sweden

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