Stochastic Scheduling with Variable Profile and Precedence Constraints
نویسندگان
چکیده
منابع مشابه
Stochastic Scheduling with Variable Profile and Precedence Constraints
In this paper, we consider the stochastic profile scheduling problem of a partially ordered set of tasks on uniform processors. The set of available processors varies in time. The running times of the tasks are independent random variables with exponential distributions. We obtain a sufficient condition under which a list policy stochastically minimizes the makespan within the class of preempti...
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This paper is concerned with the problem of scheduling preemptive tasks subject to precedence constraints in order to minimize the maximum lateness and the makespan. The number of available parallel processors is allowed to vary in time. It is shown that when an Earliest Due Date rst algorithm provides an optimal nonpreemptive schedule for unit-execution-time (UET) tasks, then the preemptive pr...
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In this paper, we consider the stochastic proole scheduling problem of a partially ordered set of tasks on uniform processors. The set of available processors varies in time. The running times of the tasks are independent random variables with exponential distributions. We obtain a suucient condition under which a list policy stochastically minimizes the makespan within the class of preemptive ...
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We consider parallel, identical machine scheduling problems where the jobs are subject to precedence constraints, release dates, and the processing times of jobs are governed by independent probability distributions. The objective is to minimize the expected value of the total weighted completion time ∑w j C j , where w j 0. Building upon a linear programming relaxation by Möhring, Schulz, and ...
متن کاملStochastic Online Scheduling with Precedence Constraints
We consider the preemptive and non-preemptive problems of scheduling jobs with precedence constraints on parallel machines with the objective to minimize the sum of (weighted) completion times. We investigate an online model in which the scheduler learns about a job when all its predecessors have completed. For scheduling on a single machine, we show matching lower and upper bounds of Θ(n) and ...
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ژورنال
عنوان ژورنال: SIAM Journal on Computing
سال: 1997
ISSN: 0097-5397,1095-7111
DOI: 10.1137/s0097539791218949