Abstract
The traditional logistics vehicle scheduling method only estimates the total scheduling of batch vehicles, without considering the capacity limit of single logistics vehicle. It causes the problem of waste in vehicle transportation. Therefore, a vehicle scheduling method based on monitoring image under time constraints is proposed, using the time displayed in the monitoring image to constrain, the dynamic scheduling model is established by setting up the time window scheduling model to schedule the vehicle tasks within the time window conditions. According to the images obtained from the monitoring, combined with the need to divide several stages under the time constraints, the vehicles to ensure the logistics transportation can be scheduled according to the actual situation, make a highly optimal decision, achieve the maximum vehicle load rate, and ensure the smooth implementation of the dynamic strategy of the non full load logistics vehicle scheduling under the time constraints. Finally, the simulation test results show that the proposed method can improve the efficiency and rationality of logistics vehicle scheduling, the algorithm is stable and reliable, and has strong practicability.
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References
Yang, G.: Research on optimization method of vehicle scheduling for non-full load logistics. Comput. Simul. 34(03), 147–150 (2017)
Zheng, Z., Long, J.Y., Gao, X.Q.: Production scheduling problems of steelmaking-continuous casting process in dynamic production environment. J. Iron Steel Res. (Int.) 24(6), 586–594 (2017)
Gili, S., Marra, W.G., D’Ascenzo, F., et al.: Comparative safety and efficacy of statins for primary prevention in human immunodeficiency virus-positive patients: a systematic review and meta-analysis. Eur. Heart J. 37(48), 3600–3610 (2016)
Pan, C., Zhang, J., Qin, W.: Real-time OHT dispatching mechanism for the interbay automated material handling system with shortcuts and bypasses. Chin. J. Mech. Eng. 30(3), 663–675 (2017)
Chamana, M., Chowdhury, B.H., Jahanbakhsh, F.: Distributed control of voltage regulating devices in the presence of high PV penetration to mitigate ramp-rate issues. IEEE Trans. Smart Grid 77(99), 1 (2016)
Yu, Y.L., Li, W., Sheng, D.R., et al.: A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network. J. Zhejiang Universityence A 17(2), 101–114 (2016)
Chen, L., Ming, Y.E., Jiang, Y.E., et al.: Study on ecological operation and influence of power generation of Longtan-Yantan cascade reservoirs on Hongshui River. J. Hydroel. Eng. 35(2), 45–53 (2016)
Niu, W., Feng, Z., Cheng, C., et al.: Parallel multi-objective optimal operation of cascaded hydropower system. J. Hydraulic Eng. 48(01), 104–112 (2017)
Sediqi, M.M., et al.: An optimization approach for unit commitment of a power system integrated with renewable energy sources: a case study of Afghanistan. Energy Power Eng. Engl. Vers. 8, 528–536 (2017)
Tao, D., Lin, Z., Wang, B.: Load feedback-based resource scheduling and dynamic migration-based data locality for virtual hadoop clusters in OpenStack-based clouds. Tsinghua Sci. Technol. 22(2), 149–159 (2017)
Shi, J., Lee, W.-J., Liu, X.: Generation scheduling optimization of wind-energy storage system based on wind power output fluctuation features. IEEE Trans. Ind. Appl. 16(99), 1–7 (2017)
Gutiérrez-Mena, J.T., Gutiérrez, C.A., Luna-Rivera, J.M., et al.: A novel geometrical model for non-stationary MIMO vehicle-to-vehicle channels. IETE Tech. Rev. 7, 1–12 (2017)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Li, R., Chen, H. (2020). Research on Intelligent Scheduling Optimization of Non-Full-Load Logistics Vehicle Based on the Monitor Image. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_14
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DOI: https://doi.org/10.1007/978-3-030-51103-6_14
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