Heterogeneous Cargo Loading Based on Hybrid Genetic Ant Colony Algorithm
Aiming to address the issues,which heavily rely on the experience of loading workers,vehi-cle transportation,and difficult to make full ues of cargo delivery efficience,a hybrid genetic ant colo-ny packing algorithm has been proposed.This algorithm considers constraints such as the size of load-ed goods,packaging format,delivery priority sequence,and vehicle dimensions(length,width,height).It utilizes spatial heuristic algorithms to determine loading strategies,employs an improved genetic algo-rithm to generate a high-quality population for the ant colony,and optimizes the ant colony algorithm iteratively to find the best packing solution.Additionally,it uses three-dimensional digital modeling technology to create an animated sequence depicting the order of cargo placement,making the loading process visual and guiding workers in completing the loading operations.Through cargo loading simula-tion experiments,a comparison between genetic algorithms,hybrid genetic simulated annealing algorithms,and improved differential evolution algorithms revealed that the hybrid genetic ant colony algorithm had shorter operational times and higher container utilization rates,validating the feasibility and effective-ness of this algorithm.
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维普
