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

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 cha...

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Vydáno v:	Computers & electrical engineering Ročník 119; s. 109523
Hlavní autoři:	Li, Runfo, Zhang, Xinyu, Wang, Chengbo, Cui, Jinlong, Mu, Mengfeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier Ltd 01.10.2024
Témata:	Adaptive differential evolution algorithm Chance-constrained programming Machine learning Stochastic sailing times Vessel scheduling Adaptive differential evolution algorithm Chance-constrained programming Vessel scheduling Stochastic sailing times Machine learning
ISSN:	0045-7906
On-line přístup:	Získat plný text
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Popis
Shrnutí:	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.
ISSN:	0045-7906
DOI:	10.1016/j.compeleceng.2024.109523