Skip to main content

Research Repository

Advanced Search

Fuzzy C-means-based scenario bundling for stochastic service network design

Jiang, Xiaoping; Bai, Ruibin; Landa-Silva, Dario; Aickelin, Uwe

Fuzzy C-means-based scenario bundling for stochastic service network design Thumbnail


Authors

Xiaoping Jiang

Ruibin Bai

Profile Image

DARIO LANDA SILVA DARIO.LANDASILVA@NOTTINGHAM.AC.UK
Professor of Computational Optimisation

Uwe Aickelin



Abstract

Stochastic service network designs with uncertain demand represented by a set of scenarios can be modelled as a large-scale two-stage stochastic mixed-integer program (SMIP). The progressive hedging algorithm (PHA) is a decomposition method for solving the resulting SMIP. The computational performance of the PHA can be greatly enhanced by decomposing according to scenario bundles instead of individual scenarios. At the heart of bundle-based decomposition is the method for grouping the scenarios into bundles. In this paper, we present a fuzzy c-means-based scenario bundling method to address this problem. Rather than full membership of a bundle, which is typically the case in existing scenario bundling strategies such as k-means, a scenario has partial membership in each of the bundles and can be assigned to more than one bundle in our method. Since the multiple bundle membership of a scenario induces overlap between the bundles, we empirically investigate whether and how the amount of overlap controlled by a fuzzy exponent would affect the performance of the PHA. Experimental results for a less-than-truckload transportation network optimization problem show that the number of iterations required by the PHA to achieve convergence reduces dramatically with large fuzzy exponents, whereas the computation time increases significantly. Experimental studies were conducted to find out a good fuzzy exponent to strike a trade-off between the solution quality and the computational time.

Conference Name 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017
Conference Location Honolulu, HI, USA
Start Date Nov 27, 2017
End Date Nov 1, 2017
Acceptance Date Sep 1, 2017
Online Publication Date Feb 5, 2018
Publication Date Feb 2, 2018
Deposit Date Dec 8, 2017
Publicly Available Date Feb 2, 2018
Peer Reviewed Peer Reviewed
Pages 1-8
Book Title 2017 IEEE Symposium Series on Computational Intelligence, SSCI 2017 - Proceedings
ISBN 978-1-5386-2727-3
DOI https://doi.org/10.1109/SSCI.2017.8280905
Public URL https://nottingham-repository.worktribe.com/output/897017
Publisher URL http://ieeexplore.ieee.org/document/8280905/
Additional Information doi:10.1109/SSCI.2017.8280905

Files





You might also like



Downloadable Citations