Better online algorithms for scheduling with machine cost

György Dósa, H. E. Yong

Research output: Contribution to journalArticle

21 Citations (Scopus)

Abstract

For most scheduling problems the set of machines is fixed initially and remains unchanged for the duration of the problem. Recently Imreh and Noga proposed adding the concept of machine cost to scheduling problems and considered the so-called list model problem. For this problem, we are given a sequence of independent jobs with positive sizes, which must be processed nonpreemptively on a machine. No machines are initially provided, and when a job is revealed the algorithm has the option to purchase new machines. The objective is to minimize the sum of the makespan and cost of machines. In this paper, we first present an online algorithm with a competitive ratio at most 1.5798, which improves the known upper bound 1.618. Then for a special case where every job size is no greater than the machine cost, we present an optimal online algorithm with a competitive ratio 4/3. Last, we present an algorithm with a competitive ratio at most 3/2 for the semionline problem with known largest size, which improves the known upper bound 1.5309.

Original languageEnglish
Pages (from-to)1035-1051
Number of pages17
JournalSIAM Journal on Computing
Volume33
Issue number5
DOIs
Publication statusPublished - Nov 22 2004

Keywords

  • Competitive analysis
  • Machine cost
  • Online algorithm
  • Parallel machine scheduling

ASJC Scopus subject areas

  • Computer Science(all)
  • Mathematics(all)

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