Earticle

초록

영어

This paper designs a cluster-based electric vehicle relocation scheme for carsharing systems, aiming at generating a relocation schedule within a reasonable time bound by decomposing a large problem into several smaller ones. In order to develop a genetic algorithm for clustering, a feasible plan is encoded to an integer-valued vector in which intermediary stations locate at fixed positions and negative numbers separate clusters. The vehicles in overflow clusters are moved to underflow clusters through the intermediary stations first and then finally to underflow stations. The fitness function calculates the distance of all inter-station pairs in each cluster and selects the largest of them. Genetic operators continuously reduce the cost generation by generation. The performance measurement result, obtained by a prototype implementation, shows that the proposed clustering scheme linearly increases the cost according to the addition of a station, even if it is expected to increase exponentially. Moreover, the clustering plan converges to a stable cost in the early stages of the genetic evolution. These results indicate that we can overcome the stock imbalance problem and improve the service ratio.

목차

Abstract
 1. Introduction
 2. Related work
 3. Cluster-based Relocation
  3.1. Problem Scope
  3.2. Clustering
  3.3. Local Scheduling
 4. Clustering Results
 5. Concluding Remarks
 Acknowledgment
 References

키워드

저자

• Junghoon Lee [ Dept. of Computer Science and Statistics, Jeju National University Republic of Korea ]
• Gyung-Leen Park [ Dept. of Computer Science and Statistics, Jeju National University Republic of Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Software Engineering and Its Applications

• 간기

  월간

• pISSN

  1738-9984

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605