Abstract
To get global solution in multi-depots vehicle scheduling problem (MDVSP), MDVSP models are established. Two-phase particle swarm optimization (TPPSO) is established to solve MDVSP. The optimization course are as follow: first phase, set up goods number dimension particle position vector, vector’s every column corresponds to goods, vector elements are random vehicle serial number, thus we can assign goods to vehicles. Second phase, particle position matrix is set up, matrix’s column number equal to vehicle freight goods number, every column corresponds to a goods, and matrix has two row, the first row correspond to goods start depot, second row correspond to end depot, matrix elements are random number between 0 and 1, matrix elements are sort ascending according to sort rules, we can get single vehicle route. Then evaluate and filtrate particles by optimization aim, circulate until meet terminate qualification. TPPSO can assign all freights to all vehicles and easy to get optimized solution.
This work is partially supported by National Scientific and Technical Supporting Programs Funded by Ministry of Science & Technology of China (NO.2006BAH02A09), National Natural Science Foundation of China (70431003), Hebei Procce Technical Research and Develop Instruct Programs (072135214), Shenzhen-Hong Kong Innovative Circle project (Grant no.SG200810220137A) and Project 801-000021 supported by SZU R/D Fund.
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Wang, S. et al. (2009). Study on Multi-Depots Vehicle Scheduling Problem and Its Two-Phase Particle Swarm Optimization. In: Huang, DS., Jo, KH., Lee, HH., Kang, HJ., Bevilacqua, V. (eds) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. ICIC 2009. Lecture Notes in Computer Science(), vol 5755. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04020-7_80
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DOI: https://doi.org/10.1007/978-3-642-04020-7_80
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