Abstract
This paper mainly research on the container tuck route optimization problem with the integrated loading and unloading operation. Considered the disperse-stacking of containers in yards and the loading/unloading operations of each berth, the objective function of scheduling problem is the optimal rout of the container truck. In order to solve this problem, the hybrid swarm intelligence algorithm (PSO-ACO) is proposed, which combined the particle swarm optimization algorithm with the ant colony optimization algorithm. The hybrid swarm intelligence algorithm takes advantage of strong local search ability of ant colony optimization algorithm and the ACO’s pheromone taxis, which can avoid the particle swarm optimization algorithm fall in the local optimum during the convergence. The results show that the mathematical model and hybrid algorithm have effective, reliability and stability in solving the container truck scheduling problem.
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Acknowledgment
The project supported by the zhejiang provincial natural science foundation of China (Foundation No. LY14G010006).
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Liu, Y., Feng, M., Shahbazzade, S. (2017). The Container Truck Route Optimization Problem by the Hybrid PSO-ACO Algorithm. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10361. Springer, Cham. https://doi.org/10.1007/978-3-319-63309-1_56
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DOI: https://doi.org/10.1007/978-3-319-63309-1_56
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