Abstract
In this paper, we focus on multi-vehicle and multiple types of dynamic vehicle routing problems. The introduction of dynamic traveling salesman problem (TSP) is to consider user’s needs in many aspects. This paper uses the Hopfield neural network for solving the vehicle routing problem of “advanced request” to shorten the delivery path length and reduce the logistics cost. For “immediate request,” we build the analytic hierarchy process model to analyze the final delivery order under a number of factors; use multi-type corresponds to multi-vehicles mixed queuing system model to obtain service indicators of the system, so as to improve the system efficiency compared with the single-delivery vehicle system. The combination of AHP and the Hopfield neural network algorithm is superior to the application of BP neural network classification and the Hopfield neural network.
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Acknowledgement
This research is supported by the Funding Project for Academic Human Resources Development in Institutions of Higher Learning Under the Jurisdiction of Beijing Municipality PHR 201106133.
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Zhang, Y., Zhao, G. (2015). Research on Multi-service Demand Path Planning Based on Continuous Hopfield Neural Network. In: Proceedings of China Modern Logistics Engineering. Lecture Notes in Electrical Engineering, vol 286. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44674-4_39
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DOI: https://doi.org/10.1007/978-3-662-44674-4_39
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