TY - GEN
T1 - A new fast large neighbourhood search for service network design with asset balance constraints
AU - Bai, Ruibin
AU - Woodward, John R.
AU - Subramanian, Nachiappan
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/2/9
Y1 - 2017/2/9
N2 - The service network design problem (SNDP) is a fundamental problem in consolidated freight transportation. It involves the determination of an efficient transportation network and the scheduling details of the corresponding services. Compared to vehicle routing problems, SNDP can model transfers and consolidations on a multi-modal freight network. The problem is often formulated as a mixed integer programming problem and is NP-Hard. In this research, we propose a new efficient large neighbourhood search function that can handle the constraints more efficiently. The effectiveness of this new neighbourhood is evaluated in a tabu search metaheuristic (TS) and a GLS guided local search (GLS) method. Experimental results based on a set of well-known benchmark instances show that the new neighbourhood performs significantly better than the previous arc-flipping neighbourhood. The neighbourhood function is also applicable in other optimisation problems with similar discrete constraints.
AB - The service network design problem (SNDP) is a fundamental problem in consolidated freight transportation. It involves the determination of an efficient transportation network and the scheduling details of the corresponding services. Compared to vehicle routing problems, SNDP can model transfers and consolidations on a multi-modal freight network. The problem is often formulated as a mixed integer programming problem and is NP-Hard. In this research, we propose a new efficient large neighbourhood search function that can handle the constraints more efficiently. The effectiveness of this new neighbourhood is evaluated in a tabu search metaheuristic (TS) and a GLS guided local search (GLS) method. Experimental results based on a set of well-known benchmark instances show that the new neighbourhood performs significantly better than the previous arc-flipping neighbourhood. The neighbourhood function is also applicable in other optimisation problems with similar discrete constraints.
UR - http://www.scopus.com/inward/record.url?scp=85016002835&partnerID=8YFLogxK
U2 - 10.1109/SSCI.2016.7850084
DO - 10.1109/SSCI.2016.7850084
M3 - Conference contribution
AN - SCOPUS:85016002835
T3 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
BT - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016
Y2 - 6 December 2016 through 9 December 2016
ER -
