A machine learning-based adaptive heuristic for vessel scheduling problem under uncertainty via chance-constrained programming

Runfo Li, Xinyu Zhang, Chengbo Wang, Jinlong Cui, Mengfeng Mu

Abstract: Efficient vessel scheduling in port is critical for enhancing navigational efficiency. However, it faces substantial challenges due to unforeseeable events. In this context, this paper addresses the vessel scheduling problem with stochastic sailing times in port. The problem is formulated into a chance-constrained programming (CCP) model and then transformed into an equivalent deterministic programming problem. A novel approach utilizing a machine learning-based adaptive differential evolution algorithm (MLDE) is proposed to address this model. In MLDE, a K-means clustering method is employed to generate initial population, aiming to enhance the population's quality and diversity while mitigating the impact of random interference. Throughout the mutation and crossover stages, we introduce a parameter adaption strategy based on Q-learning, which is established as a Markov decision process (MDP) model. The model effectively defines the state, action, and reward functions to guide the population toward selecting the optimal scaling factor and crossover probability parameters. Numerical experiments based on different instance sizes are conducted at the Comprehensive port. The obtained results reveal the superior performance of the MLDE algorithm in comparison to existing metaheuristic algorithms and traditional differential evolution (DE) variants. A statistical analysis experiment is also conducted to further confirm the superiority of MLDE.