Optimization of Route Design and Scheduling for Heterogeneous Fleets with Electric Vessel Charging Requirements

With the rapid development of all-electric ships (AESs) and the growing emphasis on sustainable shipping, there is an increasing need for effective scheduling solutions that address the unique challenges associated with AESs, such as battery limitations and charging infrastructure constraints. However, existing studies primarily focus on simplified scenarios, overlooking the complexities inherent in multi-port and multi-vessel shipping networks. To bridge this gap, this paper develops a Mixed-Integer Linear Programming (MILP) model aimed at minimizing total operational costs, specifically targeting the scheduling optimization problem in heterogeneous fleet feeder shipping networks, while explicitly considering charging requirements and time window constraints. To tackle the computational challenges posed by large-scale and strongly constrained scenarios, this study designs an optimization algorithm based on Adaptive Large Neighborhood Search (ALNS), incorporating a two-stage strategy and a destroy–repair mechanism to progressively refine solutions. Based on data from the Yangtze River feeder network, numerical experiments demonstrate the feasibility and effectiveness of the proposed model and algorithm. Additionally, a sensitivity analysis on battery capacity explores the effects of variations in key technical parameters on all-electric ship utilization and overall operational costs.