NDLI: A simulation of tourists and vehicles scheduling in scenic and its decision-making strategy

Content Provider	IEEE Xplore Digital Library
Author	Jian-Hua Ran Kai Zeng Hong Duan Kun She
Copyright Year	2010
Description	Author affiliation: Jiuzhai Valley Guan Li Ju, China (Jian-Hua Ran) \|\| School of Computer Science & Engineering, University of Electronic Science and Technology of China, Chengdu, China (Kai Zeng; Hong Duan; Kun She)
Abstract	Today, more and more people like to take kinds of vehicles to travel, there are so many tourists and vehicles at every scenic, as a result, how to effectively solve problems between ecological environment protection and tourist development, how to wisely handle abnormal events of the scenes, such as quick response for incidences, safety of tourists, people/vehicles evacuation and so on. In this paper, we propose a multi-agent visual platform, which is used to simulate, show, evaluate and solve the most probably encountered abnormal events of the scenes. Based on this platform, we put up a model of the vehicle scheduling problem (VSP) about abnormal events of the scenes, and the data mining and decision tree algorithms are made and replaced randomly for a better and better decision solution about how to dispatch the vehicles, people. Abnormal events of the scenes are divided into three different situations: full load, not full load or have a time window. Finally, we build a complete model to verification our solution based on the platform.
Starting Page	474
Ending Page	477
File Size	440565
Page Count	4
File Format	PDF
ISBN	9781424480258
e-ISBN	9781424480265
DOI	10.1109/ICACIA.2010.5709944
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-12-17
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	decision tree VSP Biological system modeling Decision making Adaptation model data mining Security Decision trees multi-agent platform Vehicles Load modeling
Content Type	Text
Resource Type	Article

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