, Volume 8, Issue 4, pp 331–364

Makespan minimization in online scheduling with machine eligibility

  • Kangbok Lee
  • Joseph Y.-T. Leung
  • Michael L. Pinedo
Invited Survey

DOI: 10.1007/s10288-010-0149-1

Cite this article as:
Lee, K., Leung, J.YT. & Pinedo, M.L. 4OR-Q J Oper Res (2010) 8: 331. doi:10.1007/s10288-010-0149-1


In this paper we provide a survey of online scheduling in parallel machine environments with machine eligibility constraints and the makespan as objective function. We first give a brief overview of the different parallel machine environments and then survey the various types of machine eligibility constraints, including tree-hierarchical processing sets, Grade of Service processing sets, interval processing sets, and nested processing sets. We furthermore describe the relationships between the various different types of processing sets. We proceed with describing two basic online scheduling paradigms, namely online over list and online over time. For each one of the two paradigms we survey all the results that have been recorded in the literature with regard to each type of machine eligibility constraints. We obtain also several extensions in various directions. In the concluding section we describe the most important open problems in this particular area.


Parallel machine scheduling Eligibility constraint Tree-hierarchical and GoS processing sets Interval and nested processing sets Online and Semi-online scheduling Offline scheduling Makespan Competitive ratio 

MSC classification (2000)

90B35: Scheduling theory, deterministic 

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Kangbok Lee
    • 1
  • Joseph Y.-T. Leung
    • 2
  • Michael L. Pinedo
    • 3
  1. 1.Department of Supply Chain Management and Marketing SciencesRutgers Business SchoolNewarkUSA
  2. 2.Department of Computer ScienceNew Jersey Institute of TechnologyNewarkUSA
  3. 3.Department of Information, Operations and Management Sciences, Stern School of BusinessNew York UniversityNew YorkUSA

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