Service-Oriented Robust Ship Fleet Planning Under Uncertainty

In the fiercely competitive and unstable shipping market, liner shipping companies always face the dual pressure of controlling costs and maintaining service reliability. To address the management challenges brought about by this pressure, this study investigates the service-oriented robust ship fleet planning problem under uncertain container transportation demand, and adopts the filling rate as the service level indicator. This problem is formulated as a two-stage robust optimization model, with the objective of minimizing total costs while ensuring high service levels for shippers. The extended risk-bounded model makes decisions more aligned with business practice. Based on the problem characteristics, the model is decomposed into a master problem and a subproblem, and an enhanced column-and-constraint generation (EC&CG) algorithm is developed to efficiently solve this model. The applicability of the model and the effectiveness of the algorithm is verified through computational experiments. Sensitivity analysis reveals the impact of factors such as service level, uncertainty parameters, penalty cost, and sailing speed on total cost. The obtained solution exhibits strong performance in both cost management and service level consistency, offering actionable insights and reliable decision support for ship fleet planning in uncertain environments.