首页|Findings from Dalian Maritime University Broaden Understanding of Machine Learni ng (A Machine Learning-based Adaptive Heuristic for Vessel Scheduling Problem Un der Uncertainty Via Chanceconstrained Programming)
Findings from Dalian Maritime University Broaden Understanding of Machine Learni ng (A Machine Learning-based Adaptive Heuristic for Vessel Scheduling Problem Un der Uncertainty Via Chanceconstrained Programming)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting out of Dalian, People's Republic of China,by NewsRx editors, research stated, "Efficient vessel scheduling in port is cr itical for enhancing navigational efficiency. However, it faces substantial chal lenges due to unforeseeable events." Funders for this research include National Natural Science Foundation of China ( NSFC), Dalian Science and Technology Innovation Fund. Our news journalists obtained a quote from the research from Dalian Maritime Uni versity, "In this context, this paper addresses the vessel scheduling problem wi th stochastic sailing times in port. The problem is formulated into a chance-con strained programming (CCP) model and then transformed into an equivalent determi nistic programming problem. A novel approach utilizing a machine learning-based adaptive differential evolution algorithm (MLDE) is proposed to address this mod el. In MLDE, a Kmeans clustering method is employed to generate initial populat ion, aiming to enhance the population's quality and diversity while mitigating t he impact of random interference. Throughout the mutation and crossover stages, we introduce a parameter adaption strategy based on Q-learning, which is establi shed as a Markov decision process (MDP) model. The model effectively defines the state, action, and reward functions to guide the population toward selecting th e optimal scaling factor and crossover probability parameters. Numerical experim ents based on different instance sizes are conducted at the Comprehensive port. The obtained results reveal the superior performance of the MLDE algorithm in co mparison to existing metaheuristic algorithms and traditional differential evolu tion (DE) variants."
DalianPeople's Republic of ChinaAsiaCyborgsDifferential EvolutionEmerging TechnologiesMachine LearningDali an Maritime University
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