Online linear optimization for job scheduling under precedence constraints

Takahiro Fujita, Kohei Hatano, Shuji Kijima, Eiji Takimoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


We consider an online job scheduling problem on a single machine with precedence constraints under uncertainty. In this problem, for each trial t = 1,..., T, the player chooses a total order (permutation) of n fixed jobs satisfying some prefixed precedence constraints. Then, the adversary determines the processing time for each job, 9 and the player incurs as loss the sum of the processing time and the waiting time. The goal of the player is to perform as well as the best fixed total order of jobs in hindsight. We formulate the problem as an online linear optimization problem over the permutahedron (the convex hull of permutation vectors) with specific linear constraints, in which the underlying decision space is written with exponentially many linear constraints. We propose a polynomial time online linear optimization algorithm; it predicts almost as well as the state-of-the-art offline approximation algorithms do in hindsight.

Original languageEnglish
Title of host publicationAlgorithmic Learning Theory - 26th International Conference, ALT 2015
EditorsClaudio Gentile, Sandra Zilles, Kamalika Chaudhuri
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783319244853
Publication statusPublished - 2015
Event26th International Conference on Algorithmic Learning Theory, ALT 2015 - Banff, Canada
Duration: Oct 4 2015Oct 6 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other26th International Conference on Algorithmic Learning Theory, ALT 2015

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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