TY - GEN
T1 - Solving dynamic vehicle routing problem via evolutionary search with learning capability
AU - Zhou, L.
AU - Feng, L.
AU - Gupta, A.
AU - Ong, Y. S.
AU - Liu, K.
AU - Chen, C.
AU - Sha, E.
AU - Yang, B.
AU - Yan, B. W.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/5
Y1 - 2017/7/5
N2 - To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.
AB - To date, dynamic vehicle routing problem (DVRP) has attracted great research attentions due to its wide range of real world applications. In contrast to traditional static vehicle routing problem, the whole routing information in DVRP is usually unknown and obtained dynamically during the routing execution process. To solve DVRP, many heuristic and metaheuristic methods have been proposed in the literature. In this paper, we present a novel evolutionary search paradigm with learning capability for solving DVRP. In particular, we propose to capture the structured knowledge from optimized routing solution in early time slot, which can be further reused to bias the customer-vehicle assignment when dynamic occurs. By extending our previous research work, the learning of useful knowledge, and the scheduling of dynamic customer requests are detailed here. Further, to evaluate the efficacy of the proposed search paradigm, comprehensive empirical studies on 21 commonly used DVRP instances with diverse properties are also reported.
UR - https://www.scopus.com/pages/publications/85028506652
U2 - 10.1109/CEC.2017.7969403
DO - 10.1109/CEC.2017.7969403
M3 - 会议稿件
AN - SCOPUS:85028506652
T3 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
SP - 890
EP - 896
BT - 2017 IEEE Congress on Evolutionary Computation, CEC 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Congress on Evolutionary Computation, CEC 2017
Y2 - 5 June 2017 through 8 June 2017
ER -