Author(s)

Kai Ma ¹, Wei Pan ¹

Affiliation(s)

¹ Institute for Big Data Research, Liaoning University of International Business and Economics, Dalian, 116052, China

Corresponding Author

Kai Ma

ABSTRACT

The geographical advantage of the Northern Sea Route (NSR) is readily quantifiable, whereas its operational reliability for scheduled container transportation remains highly uncertain. To address this challenge, this study develops an artificial intelligence (AI)-based, uncertainty-aware, multi-objective route-planning model for a container voyage from Dalian to Rotterdam. Arctic navigation is formulated as a time-dependent network in which each edge represents a dynamically evolving navigational state. The intelligent cost model integrates sailing distance, sea-ice concentration, sea state, vessel ice-class capability, fuel consumption, carbon emissions, operational cost, and navigational risk. A modified multi-label A* algorithm is developed as the core AI search mechanism. The algorithm dynamically eliminates infeasible waters, maintains non-dominated route labels, imposes a route-level risk ceiling, and automatically replans the uncompleted route when updated environmental information becomes available. This architecture enables adaptive decision-making under changing sea-ice and weather conditions and provides a computational interface for future integration with deep-learning-based sea-ice prediction and neural-network-based vessel performance models. Three Arctic alternatives and the conventional Suez route are evaluated under a controlled September 2026 scenario. Environmental and operational uncertainties are further propagated through 3,000 Monte Carlo simulations. The low-risk Arctic route covers 8,021.95 nautical miles in 19.32 days. Compared with the Suez route, it reduces distance, voyage time, and fuel consumption by 20.03%, 19.37%, and 18.41%, respectively, corresponding to a CO₂ reduction of 1,284.72 tonnes. However, its simulated on-time arrival probability is only 78.8%, compared with 94.9% for the Suez route. The results demonstrate that the proposed AI-based planning framework can generate adaptive and interpretable route alternatives while explicitly balancing efficiency, environmental performance, schedule reliability, and navigational risk.

KEYWORDS

Artificial intelligence; AI-based route planning; Multi-objective optimization; A algorithm; Arctic shipping; Northern Sea Route

CITE THIS PAPER

Kai Ma, Wei Pan. Risk-Constrained Multi-Objective Arctic Route Planning under Sea-Ice Uncertainty Using a Time-Dependent Multi-Label an Algorithm: A Dalian–Rotterdam Container Shipping Case Study. Journal of Artificial Intelligence Practice (2026). Vol. 9, No. 2, 15-24. DOI: http://dx.doi.org/10.23977/jaip.2026.090202.

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