A parallel local search framework for the Fixed-Charge Multicommodity Network Flow problem

Lluís Miquel Munguía, Shabbir Ahmed, David A. Bader, George L. Nemhauser, Vikas Goel, Yufen Shao

Research output: Contribution to journalArticlepeer-review

Abstract

We present a parallel local search approach for obtaining high quality solutions to the Fixed Charge Multicommodity Network Flow problem (FCMNF). The approach proceeds by improving a given feasible solution by solving restricted instances of the problem where flows of certain commodities are fixed to those in the solution while the other commodities are locally optimized. We derive multiple independent local search neighborhoods from an arc-based mixed integer programming (MIP) formulation of the problem which are explored in parallel. Our scalable parallel implementation takes advantage of the hybrid memory architecture in modern platforms and the effectiveness of MIP solvers in solving small problems instances. Computational experiments on FCMNF instances from the literature demonstrate the competitiveness of our approach against state of the art MIP solvers and other heuristic methods.

Original languageAmerican English
Pages (from-to)44-57
Number of pages14
JournalComputers and Operations Research
Volume77
DOIs
StatePublished - Jan 1 2017
Externally publishedYes

ASJC Scopus subject areas

  • General Computer Science
  • Modeling and Simulation
  • Management Science and Operations Research

Keywords

  • Discrete optimization
  • FCMNF
  • Multicommodity capacitated network design
  • Parallel computing
  • Primal heuristics

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