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Fair Hitting Sequence Problem: Scheduling Activities with Varied Frequency Requirements

  • Serafino Cicerone
    ,
  • Gabriele Di Stefano
    ,
  • Leszek Gasieniec
    ,
  • Tomasz Jurdzinski
    ,
  • Alfredo Navarra
    ,
  • Tomasz Radzik(corresponding author)
*Corresponding author for this work
  • University of L'Aquila
    ,
  • University of Liverpool
    ,
  • University of Wrocław
    ,
  • University of Perugia
    ,
  • King's College London
Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution

Related Event

Title

11th International Conference on Algorithms and Complexity, CIAC 2019

Event type

Conference

Date

05/27/2019 - 05/29/2019

Location

RomeItaly

Abstract

Given a set of n elements and a family of (possibly intersecting) subsets of V, we consider a scheduling problem of perpetual monitoring (attending) these subsets. In each time step one element of V is visited, and all sets in containing v are considered to be attended during this step. That is, we assume that it is enough to visit an arbitrary element in to attend to this whole set. Each set has an urgency factor , which indicates how frequently this set should be attended relatively to other sets. Let denote the time slot when set is attended for the i-th time. The objective is to find a perpetual schedule of visiting the elements of V, so that the maximum value is minimized. The value indicates how urgent it was to attend to set at the time slot. We call this problem the Fair Hitting Sequence (FHS) problem, as it is related to the minimum hitting set problem. In fact, the uniform FHS, when all urgency factors are equal, is equivalent to the minimum hitting set problem, implying that there is a constant such that it is NP-hard to compute -approximation schedules for FHS. We demonstrate that scheduling based on one hitting set can give poor approximation ratios, even if an optimal hitting set is used. To counter this, we design a deterministic algorithm which partitions the family into sub-families and combines hitting sets of those sub-families, giving -approximate schedules. Finally, we show an LP-based lower bound on the optimal objective value of FHS and use this bound to derive a randomized algorithm which with high probability computes -approximate schedules.

Publication Information

Output type

Scholary Output:
Chapter in Book/Report/Conference proceeding
Conference contribution

Original language

English (US)

Pages from-to (Number of pages)

Pages 174-186 (13 pages)

Publication milestones

  • Published - 2019

Publication status

Published - 2019

Publisher

Springer Verlag

Publication series

  • Publication series name: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print): 0302-9743
    ISSN (Electronic): 1611-3349
    Volume: 11485 LNCS
9783030174019

Publication IDs

  • Scopus: 85066893535

Host publication title

Algorithms and Complexity - 11th International Conference, CIAC 2019, Proceedings

Host publication editors

  • Pinar Heggernes

Publication metrics

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Fractional count
1
Fractional count
0.14
Fractional count
6
Fractional count
0.86
Fractional count
1
Fractional count
1
SciVal
Author count
7
SciVal
Paper percentile
33
Scopus
citations

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Citation count
2

Funding Details

We demonstrate that scheduling based on one hitting set can give poor approximation ratios, even if an optimal hitting set is used. To counter The work has been supported in part by the European project “Geospatial based Environment for Optimisation Systems Addressing Fire Emergencies” (GEO-SAFE), contract no. H2020-691161, by the Italian National Group for Scientific Computation GNCS-INdAM, by Networks Sciences and Technologies (NeST) initiative at University of Liverpool, and by the Polish National Science Center (NCN) grant 2017/25/B/ST6/02010.
FundersFunding numbers
Italian National Group for Scientific Computation GNCS-INdAM
-
Networks Sciences and Technologies
-
Polish National Science Center NCN
-
H2020
691161
Royal Liverpool University Hospital
-
NCN
2017/25/B/ST6/02010