article

On Scheduling Unit-Length Jobs with Multiple Release Time/Deadline Intervals

Authors:

Barbara Simons,

Michael SipserAuthors Info & Claims

Operations Research, Volume 32, Issue 1

Pages 80 - 88

https://doi.org/10.1287/opre.32.1.80

Published: 01 February 1984 Publication History

Abstract

We consider the problem of scheduling n unit-time jobs with multiple release time/deadline intervals, which are intervals of time in which the job can be scheduled. A feasible schedule is one in which each job is run in its entirety within one of its release time/deadline intervals. We show that the general problem is NP-complete, but that for a special case one can determine in polynomial time if a feasible schedule exists. If there is a feasible schedule for the special case, then it is possible to obtain both a schedule that minimizes the maximum completion time and also one that minimizes the sum of the completion times of all jobs. If, on the other hand, there is no feasible schedule, then one can determine the minimum number of jobs that must be removed to make the problem instance feasible.

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Kis TSzögi E(2024)Scheduling jobs to minimize a convex function of resource usageComputers and Operations Research10.1016/j.cor.2024.106748169:COnline publication date: 1-Sep-2024
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Bender MBunde DLeung VMcCauley SPhillips CBlelloch GVöcking B(2013)Efficient scheduling to minimize calibrationsProceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures10.1145/2486159.2486193(280-287)Online publication date: 23-Jul-2013
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Sugihara RGupta R(2011)Path Planning of Data Mules in Sensor NetworksACM Transactions on Sensor Networks10.1145/1993042.19930438:1(1-27)Online publication date: 1-Aug-2011
https://dl.acm.org/doi/10.1145/1993042.1993043
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On Scheduling Unit-Length Jobs with Multiple Release Time/Deadline Intervals
1. Applied computing
2. Theory of computation
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    1. Approximation algorithms analysis
    2. Online algorithms
      1. Online learning algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning

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Published In

Operations Research Volume 32, Issue 1

February 1984

228 pages

ISSN:0030-364X

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INFORMS

Linthicum, MD, United States

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Published: 01 February 1984

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Kis TSzögi E(2024)Scheduling jobs to minimize a convex function of resource usageComputers and Operations Research10.1016/j.cor.2024.106748169:COnline publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1016/j.cor.2024.106748
Bender MBunde DLeung VMcCauley SPhillips CBlelloch GVöcking B(2013)Efficient scheduling to minimize calibrationsProceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures10.1145/2486159.2486193(280-287)Online publication date: 23-Jul-2013
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Sugihara RGupta R(2011)Path Planning of Data Mules in Sensor NetworksACM Transactions on Sensor Networks10.1145/1993042.19930438:1(1-27)Online publication date: 1-Aug-2011
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Kravchenko SWerner F(2011)Parallel machine problems with equal processing timesJournal of Scheduling10.1007/s10951-011-0231-314:5(435-444)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1007/s10951-011-0231-3
Sugihara RGupta R(2010)Speed control and scheduling of data mules in sensor networksACM Transactions on Sensor Networks10.1145/1806895.18068997:1(1-29)Online publication date: 20-Aug-2010
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Mosheiov GSarig A(2010)Scheduling with a common due-windowInformation Sciences: an International Journal10.1016/j.ins.2009.11.042180:8(1492-1505)Online publication date: 1-Apr-2010
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Federgruen AGroenevelt H(1986)Preemptive Scheduling of Uniform Machines by Ordinary Network Flow TechniquesManagement Science10.5555/2912935.291294732:3(341-349)Online publication date: 1-Mar-1986
https://dl.acm.org/doi/10.5555/2912935.2912947

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