Online cardinality constrained scheduling

Leah Epstein; Alexandra Lassota; Asaf Levin; Marten Maack; Lars Rohwedder

doi:10.1016/j.orl.2023.08.003

Online cardinality constrained scheduling

Leah Epstein, Alexandra Lassota, Asaf Levin, Marten Maack, Lars Rohwedder

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

In online load balancing problems, jobs arrive over a list. Upon arrival of a job, the algorithm is required to assign it immediately and irrevocably to a machine. We consider such a makespan minimization problem with an additional cardinality constraint, i.e., at most k jobs may be assigned to each machine, where k is a parameter of the problem. We present both upper and lower bounds on the competitive ratio of online algorithms for this problem with identical machines.

Original language	English
Pages (from-to)	533-539
Number of pages	7
Journal	Operations Research Letters
Volume	51
Issue number	5
DOIs	https://doi.org/10.1016/j.orl.2023.08.003
State	Published - Sep 2023

Bibliographical note

Publisher Copyright:
© 2023 Elsevier B.V.

Keywords

Competitive analysis
Load balancing
Online algorithms
Scheduling

ASJC Scopus subject areas

Software
Management Science and Operations Research
Industrial and Manufacturing Engineering
Applied Mathematics

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10.1016/j.orl.2023.08.003

Cite this

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AB - In online load balancing problems, jobs arrive over a list. Upon arrival of a job, the algorithm is required to assign it immediately and irrevocably to a machine. We consider such a makespan minimization problem with an additional cardinality constraint, i.e., at most k jobs may be assigned to each machine, where k is a parameter of the problem. We present both upper and lower bounds on the competitive ratio of online algorithms for this problem with identical machines.

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KW - Scheduling

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Online cardinality constrained scheduling

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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