Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm

Tingying Song, Tomohiro Murata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the aggravation of environment pollution, increase of the number of private cars and the development of shared economic, one-way car sharing system is a substitute for traditional two-way car system. And free-floating car sharing system is most dynamic, in which users can rent and return vehicles in different stations just by smart phone. Therefore, no reservation information and imbalance between available cars and user demand of each station is a serious problem that lead to low user satisfaction and car working rate. In this paper, we produce a scheduling method generates short-term forecasting and relocation. In forecasting, we use Markov chain model (MCM) to forecast the number of available cars in future three time-period to catch the gap between it and demand. Then make relocate scheduling based on Genetic algorithm(GA) to minimize the gap. We apply this method to the data generated by simulator, randomly generating running condition, and we can find improvement in user satisfaction rate and car working rate.

Original languageEnglish
Title of host publicationProceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018
EditorsOscar Castillo, David Dagan Feng, A.M. Korsunsky, Craig Douglas, S. I. Ao
PublisherNewswood Limited
ISBN (Electronic)9789881404886
Publication statusPublished - 2018 Jan 1
Event2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018 - Hong Kong, Hong Kong
Duration: 2018 Mar 142018 Mar 16

Publication series

NameLecture Notes in Engineering and Computer Science
Volume2
ISSN (Print)2078-0958

Other

Other2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018
CountryHong Kong
CityHong Kong
Period18/3/1418/3/16

Fingerprint

Relocation
Markov processes
Railroad cars
Genetic algorithms
Scheduling
Multicarrier modulation
Pollution
Simulators
Economics

ASJC Scopus subject areas

  • Computer Science (miscellaneous)

Cite this

Song, T., & Murata, T. (2018). Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm. In O. Castillo, D. D. Feng, A. M. Korsunsky, C. Douglas, & S. I. Ao (Eds.), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018 (Lecture Notes in Engineering and Computer Science; Vol. 2). Newswood Limited.

Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm. / Song, Tingying; Murata, Tomohiro.

Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. ed. / Oscar Castillo; David Dagan Feng; A.M. Korsunsky; Craig Douglas; S. I. Ao. Newswood Limited, 2018. (Lecture Notes in Engineering and Computer Science; Vol. 2).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Song, T & Murata, T 2018, Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm. in O Castillo, DD Feng, AM Korsunsky, C Douglas & SI Ao (eds), Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. Lecture Notes in Engineering and Computer Science, vol. 2, Newswood Limited, 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018, Hong Kong, Hong Kong, 18/3/14.
Song T, Murata T. Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm. In Castillo O, Feng DD, Korsunsky AM, Douglas C, Ao SI, editors, Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. Newswood Limited. 2018. (Lecture Notes in Engineering and Computer Science).
Song, Tingying ; Murata, Tomohiro. / Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm. Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018. editor / Oscar Castillo ; David Dagan Feng ; A.M. Korsunsky ; Craig Douglas ; S. I. Ao. Newswood Limited, 2018. (Lecture Notes in Engineering and Computer Science).
@inproceedings{da0da287e330406b9162cc114acd28ee,
title = "Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm",
abstract = "As the aggravation of environment pollution, increase of the number of private cars and the development of shared economic, one-way car sharing system is a substitute for traditional two-way car system. And free-floating car sharing system is most dynamic, in which users can rent and return vehicles in different stations just by smart phone. Therefore, no reservation information and imbalance between available cars and user demand of each station is a serious problem that lead to low user satisfaction and car working rate. In this paper, we produce a scheduling method generates short-term forecasting and relocation. In forecasting, we use Markov chain model (MCM) to forecast the number of available cars in future three time-period to catch the gap between it and demand. Then make relocate scheduling based on Genetic algorithm(GA) to minimize the gap. We apply this method to the data generated by simulator, randomly generating running condition, and we can find improvement in user satisfaction rate and car working rate.",
author = "Tingying Song and Tomohiro Murata",
year = "2018",
month = "1",
day = "1",
language = "English",
series = "Lecture Notes in Engineering and Computer Science",
publisher = "Newswood Limited",
editor = "Oscar Castillo and Feng, {David Dagan} and A.M. Korsunsky and Craig Douglas and Ao, {S. I.}",
booktitle = "Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018",

}

TY - GEN

T1 - Vehicle relocation scheduling method for car sharing service system based on Markov chain and genetic algorithm

AU - Song, Tingying

AU - Murata, Tomohiro

PY - 2018/1/1

Y1 - 2018/1/1

N2 - As the aggravation of environment pollution, increase of the number of private cars and the development of shared economic, one-way car sharing system is a substitute for traditional two-way car system. And free-floating car sharing system is most dynamic, in which users can rent and return vehicles in different stations just by smart phone. Therefore, no reservation information and imbalance between available cars and user demand of each station is a serious problem that lead to low user satisfaction and car working rate. In this paper, we produce a scheduling method generates short-term forecasting and relocation. In forecasting, we use Markov chain model (MCM) to forecast the number of available cars in future three time-period to catch the gap between it and demand. Then make relocate scheduling based on Genetic algorithm(GA) to minimize the gap. We apply this method to the data generated by simulator, randomly generating running condition, and we can find improvement in user satisfaction rate and car working rate.

AB - As the aggravation of environment pollution, increase of the number of private cars and the development of shared economic, one-way car sharing system is a substitute for traditional two-way car system. And free-floating car sharing system is most dynamic, in which users can rent and return vehicles in different stations just by smart phone. Therefore, no reservation information and imbalance between available cars and user demand of each station is a serious problem that lead to low user satisfaction and car working rate. In this paper, we produce a scheduling method generates short-term forecasting and relocation. In forecasting, we use Markov chain model (MCM) to forecast the number of available cars in future three time-period to catch the gap between it and demand. Then make relocate scheduling based on Genetic algorithm(GA) to minimize the gap. We apply this method to the data generated by simulator, randomly generating running condition, and we can find improvement in user satisfaction rate and car working rate.

UR - http://www.scopus.com/inward/record.url?scp=85062629778&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062629778&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:85062629778

T3 - Lecture Notes in Engineering and Computer Science

BT - Proceedings of the International MultiConference of Engineers and Computer Scientists 2018, IMECS 2018

A2 - Castillo, Oscar

A2 - Feng, David Dagan

A2 - Korsunsky, A.M.

A2 - Douglas, Craig

A2 - Ao, S. I.

PB - Newswood Limited

ER -