Multi-objective evolutionary algorithm for a ship routing problem in maritime logistics collaboration

Abstract

This paper proposes a multi-objective evolutionary algorithm in maritime logistics collaboration of two liner shipping companies in joint-routing network design. The model is called the ship routing problem and two objectives being minimised are total cost and deviation in fair cost proportion. The method combines NSGA-II and the principles of effective genetic algorithms from the literature, and an example of application with data background from the Indonesian archipelago is demonstrated. Both the method and its application in real-life problems have never been encountered in academic publication, therefore this research has significant contribution and practical values on those fronts. Three dispersal mechanisms are tested with two different mutation probabilities and the results suggest that different rate supports different mechanism. Running times are longer in higher mutation rate, but in general the DV(1) mechanism is faster than both DL mechanisms. Non-dominated solutions are found and translated to joint routings of both carriers.

A Predictive Analytics Method for the Avoidance of Ship Grounding in Real Operational Conditions

“…They are based on ship route planning algorithms. Examples are evolutionary theory-based methods (e.g., Wibisono et al, 2017;Szlapczynska et al, 2019); A* algorithms (e.g., ; Ant Colony algorithms (e.g., Mohan et al, 2012;Liang et al, 2020) and Fast Marching algorithms (e.g., Yan et al, 2020;Chen et al, 2020). Lately, Gil (2021) and Montewka et al (2022) presented the CADCA approach to determine the safe manoeuvring distance to shallow waters for ship grounding avoidance.…”

A Set Covering Model for a Green Ship Routing and Scheduling Problem with Berth Time-Window Constraints for Use in the Bulk Cargo Industry

“…They developed a mixed-integer mathematical programming model for a multi-objective sustainable load planning problem and solved the proposed model using a compromise programming approach. Wibisono and Jittamai [33] proposed a multi-objective evolutionary algorithm to solve a ship routing problem and generated Pareto optimal solutions while jointly minimizing total cost and deviation in fair cost proportion. Martínez-López et al [34] identified the optimal sizing and the most adequate propulsion plant for a fleet of feeder vessels to minimize the total cost, the transportation time, and the environmental costs.…”

“…The Pareto approach provides decision-makers (maritime regulators) with several alternative solutions associated with objective functions. It provides more choices for the decision-maker to consider and find the best balance among objectives based on their needs [33,37].…”

“…This method is more realistic, particularly when decision-makers (maritime regulators) are undecided about the weights or when a trade-off relation among objective functions is not established a priori [33].…”