Journal of Heuristics

, Volume 17, Issue 6, pp 637–658

Matching based very large-scale neighborhoods for parallel machine scheduling

Authors

  • Tobias Brueggemann
    • Department of Applied MathematicsUniversity of Twente
    • Department of Applied MathematicsUniversity of Twente
Open AccessArticle

DOI: 10.1007/s10732-010-9149-8

Cite this article as:
Brueggemann, T. & Hurink, J.L. J Heuristics (2011) 17: 637. doi:10.1007/s10732-010-9149-8

Abstract

In this paper we study very large-scale neighborhoods for the minimum total weighted completion time problem on parallel machines, which is known to be strongly \(\mathcal{NP}\)-hard. We develop two different ideas leading to very large-scale neighborhoods in which the best improving neighbor can be determined by calculating a weighted matching. The first neighborhood is introduced in a general fashion using combined operations of a basic neighborhood. Several examples for basic neighborhoods are given. The second approach is based on a partitioning of the job sets on the machines and a reassignment of them. In a computational study we evaluate the possibilities and the limitations of the presented very large-scale neighborhoods.

Keywords

SchedulingParallel machinesTotal weighted completion timeVery large-scale neighborhoodsLocal search
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Copyright information

© The Author(s) 2010