Hybrid genetic algorithm and Q-learning-based solution for the time-variant berth and quay crane allocation problem
Year: 2025
Type: article
Abstract: Introduction This study addresses the joint scheduling optimization of continuous berths and quay cranes by proposing a time-variant quay crane allocation method. Methods A coordinated optimization model is constructed that considers the temporal dimension of quay crane scheduling and equipment collision factors to reduce overall port operational costs. A hybrid intelligent algorithm integrating Q-learning is innovatively designed, using a genetic algorithm as the main framework while embedding a quay crane allocation module and dynamically selecting genetic operators through Q-learning to achieve adaptive optimization of the evolutionary mechanism. Results The module with Q-learning optimization is compared to the module without Q-learning optimization, demonstrating that the Q-learning module can accelerate the convergence of the algorithm and has a better ability to find the optimal solution in large-scale cases, proving the effectiveness of the module. Discussion The results show that the proposed algorithm and CPLEX perform similarly in small-scale cases, while the solution speed and capability are better than the genetic algorithm in large-scale problems and superior to the CPLEX algorithm with time constraints in some cases, proving the effectiveness and superiority of the proposed algorithm.
Authors: Chengji Liang, Dong Tang, Rui Zhao, Yu Wang
Institution: Shanghai Maritime University
Language: English
Topic: Maritime Ports and Logistics
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,353
OpenAlex ID: W4408217996