TY - GEN
T1 - A hybrid genetic algorithm for vehicle routing problems with dynamic requests
AU - Yi, Ruikang
AU - Luo, Wenjian
AU - Bu, Chenyang
AU - Lin, Xin
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/1
Y1 - 2017/7/1
N2 - In this paper, a hybrid Genetic Algorithm (GA) is proposed to solve the Vehicle Routing Problems with Dynamic Requests (VRPDR). The proposed hybrid GA primarily involves the following parts. First, a greedy split is introduced, which is fit for the VRPDR. Second, the adjacent-exchange-based local search is proposed. Third, a novel mutation operator, named the insert mutation, is proposed. The experimental results show that the proposed hybrid GA is effective.
AB - In this paper, a hybrid Genetic Algorithm (GA) is proposed to solve the Vehicle Routing Problems with Dynamic Requests (VRPDR). The proposed hybrid GA primarily involves the following parts. First, a greedy split is introduced, which is fit for the VRPDR. Second, the adjacent-exchange-based local search is proposed. Third, a novel mutation operator, named the insert mutation, is proposed. The experimental results show that the proposed hybrid GA is effective.
KW - Dynamic Vehicle Routing
KW - Genetic Algorithm
KW - Local Search
KW - Split Algorithm
UR - https://www.scopus.com/pages/publications/85046095893
U2 - 10.1109/SSCI.2017.8285301
DO - 10.1109/SSCI.2017.8285301
M3 - 会议稿件
AN - SCOPUS:85046095893
T3 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
SP - 1
EP - 8
BT - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Y2 - 27 November 2017 through 1 December 2017
ER -