Vehicle scheduling model of emergency logistics distribution based on internet of things

2018 ◽  
Vol 11 (1) ◽  
pp. 36
Author(s):  
Chang Qing
Keyword(s):  
Internet Of Things ◽  
Vehicle Scheduling ◽  
Logistics Distribution ◽  
Scheduling Model ◽  
Emergency Logistics

scholarly journals Vehicle Scheduling Model Based on Data Mining

2018 ◽  
Vol 11 (1) ◽  
pp. 104
Author(s):  
Guohua Zhang ◽  
Ting Xie ◽  
Min Liu ◽  
Yang Liu
Keyword(s):  
Data Mining ◽  
Shortest Path ◽  
Vehicle Scheduling ◽  
Path Model ◽  
Dijkstra Algorithm ◽  
Logistics Distribution ◽  
Research Status ◽  
Scheduling Model ◽  
Algorithmic Support ◽  
Distribution Process

The article presents a shortest-path model of vehicle scheduling, which based on analyzing the application of data mining in vehicle scheduling model by referring research status of data mining and describing logistics distribution process. The article also provides an algorithmic support by making the Dijkstra algorithm of the shortest path model simple and rational.

scholarly journals Analysis and Enhancement of IEEE 802.15.4e DSME Beacon Scheduling Model

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Kwang-il Hwang ◽  
Sung-wook Nam
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Ieee 802.15.4 ◽  
Design Model ◽  
Abstract Model ◽  
Modes Of Operation ◽  
Network Construction ◽  
Scheduling Model ◽  
And Performance ◽  
Ieee 802.15.4E

In order to construct a successful Internet of things (IoT), reliable network construction and maintenance in a sensor domain should be supported. However, IEEE 802.15.4, which is the most representative wireless standard for IoT, still has problems in constructing a large-scale sensor network, such as beacon collision. To overcome some problems in IEEE 802.15.4, the 15.4e task group proposed various different modes of operation. Particularly, the IEEE 802.15.4e deterministic and synchronous multichannel extension (DSME) mode presents a novel scheduling model to solve beacon collision problems. However, the DSME model specified in the 15.4e draft does not present a concrete design model but a conceptual abstract model. Therefore, in this paper we introduce a DSME beacon scheduling model and present a concrete design model. Furthermore, validity and performance of DSME are evaluated through experiments. Based on experiment results, we analyze the problems and limitations of DSME, present solutions step by step, and finally propose an enhanced DSME beacon scheduling model. Through additional experiments, we prove the performance superiority of enhanced DSME.

The Research of Emergency Logistics Routing Optimization Based on Improved Ant Colony Optimization

2012 ◽  
Vol 482-484 ◽  
pp. 2519-2523
Author(s):  
Teng Fei ◽  
Li Yi Zhang ◽  
Yun Shan Sun ◽  
Hong Wei Ren
Keyword(s):  
Mathematical Model ◽  
Ant Colony Algorithm ◽  
Model Solution ◽  
Ant Colony ◽  
Traffic Situation ◽  
Logistics System ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Emergency Logistics ◽  
Improved Ant Colony Algorithm

Emergency logistics system contains information on material reserves, emergency command and emergency distribution. In this paper, the aspect of emergency distribution only is analyzed in microscopic, mathematical model of emergency logistics distribution has been established in considering the traffic situation and shortage degree. On the aspect of model solution, improved ant colony algorithm, which can enhance the selectivity of finding the best solution in emergency logistics distribution routing, is used in solving the model.

scholarly journals Biobjective Emergency Logistics Scheduling Model Based on Uncertain Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
Youqiang Sun ◽  
Yeqing Ren ◽  
Xingjuan Cai
Keyword(s):  
Cloud Model ◽  
Traffic Volume ◽  
One Dimension ◽  
Trajectory Data ◽  
Inference Procedure ◽  
Traffic Conditions ◽  
Mutation Operators ◽  
Scheduling Model ◽  
Emergency Logistics ◽  
Proposed Model

Emergency logistics scheduling appears more and more important in modern society because of frequent occurrence of unpredictable disasters. Most of the existing studies consider a certain emergency logistics scheduling model, and most of them are based on an ideal scenario. Considering the uncertain traffic condition and the real road condition, a biobjective emergency logistics scheduling model is proposed, which includes two objectives: transportation time and transportation cost. The uncertainty of the proposed model is reflected in two aspects: the occurrence time of emergencies and the traffic volume predicted by the cloud model. The numerical characteristics of traffic information are abstracted from the spatial-temporal trajectory data by the reverse cloud model, and the inference procedure of the one-dimension cloud model further predicts the uncertain traffic volume using the numerical characteristics. In addition, the crossover and mutation operators of multiobjective evolutionary algorithms are modified to solve the model. The experimental results show that the inference procedure of one-dimension cloud model can accurately predict the traffic volume at the departure time; and the proposed model is more reasonable than the existing scheduling models; at the same time, the improved NSGA-II can also provide superior schemes in different departure times and traffic conditions for decision makers.

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