TY - GEN
T1 - Fair Hitting Sequence Problem
T2 - 11th International Conference on Algorithms and Complexity, CIAC 2019
AU - Cicerone, Serafino
AU - Di Stefano, Gabriele
AU - Gasieniec, Leszek
AU - Jurdzinski, Tomasz
AU - Navarra, Alfredo
AU - Radzik, Tomasz
AU - Stachowiak, Grzegorz
N1 - Funding Information:
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.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Approximation algorithms
KW - Hitting set
KW - Periodic maintenance
KW - Scheduling
UR - http://www.scopus.com/inward/record.url?scp=85066893535&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-17402-6_15
DO - 10.1007/978-3-030-17402-6_15
M3 - Conference contribution
AN - SCOPUS:85066893535
SN - 9783030174019
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 174
EP - 186
BT - Algorithms and Complexity - 11th International Conference, CIAC 2019, Proceedings
A2 - Heggernes, Pinar
PB - Springer Verlag
Y2 - 27 May 2019 through 29 May 2019
ER -