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
N1 - Publisher Copyright:
© 2018 Newswood Limited. All rights reserved.
PY - 2018
Y1 - 2018
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
T2 - 2018 International MultiConference of Engineers and Computer Scientists, IMECS 2018
Y2 - 14 March 2018 through 16 March 2018
ER -