Preemptive scheduling on uniformly related machines: minimizing the sum of the largest pair of job completion times

Leah Epstein () and Ido Yatsiv ()
Additional contact information
Leah Epstein: University of Haifa
Ido Yatsiv: University of Haifa

Journal of Scheduling, 2017, vol. 20, issue 2, No 1, 115-127

Abstract: Abstract We revisit the classic problem of preemptive scheduling on m uniformly related machines. In this problem, jobs can be arbitrarily split into parts, under the constraint that every job is processed completely, and that the parts of a job are not assigned to run in parallel on different machines. We study a new objective which is motivated by fairness, where the goal is to minimize the sum of the two maximal job completion times. We design a polynomial time algorithm for computing an optimal solution. The algorithm can act on any set of machine speeds and any set of input jobs. The algorithm has several cases, many of which are very different from algorithms for makespan minimization (algorithms that minimize the maximum completion time of any job), and from algorithms that minimize the total completion time of all jobs.

Keywords: Uniformly related machines; Preemptive scheduling; Makespan completion times (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10951-016-0476-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jsched:v:20:y:2017:i:2:d:10.1007_s10951-016-0476-y

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10951

DOI: 10.1007/s10951-016-0476-y

Access Statistics for this article

Journal of Scheduling is currently edited by Edmund Burke and Michael Pinedo

More articles in Journal of Scheduling from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().