Stochastic Machine Scheduling with Precedence Constraints

We consider parallel, identical machine scheduling problems, where the jobs are subject to precedence constraints and release dates, and where the processing times of jobs are governed by independent probability distributions. Our objective is to minimize the expected value of the total weighted com...

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Bibliographic Details
Published in:SIAM journal on computing Vol. 34; no. 4; pp. 788 - 802
Main Authors: Skutella, Martin, Uetz, Marc
Format: Journal Article
Language:English
Published: Philadelphia, PA Society for Industrial and Applied Mathematics 01.01.2005
Subjects:
Algorithmics. Computability. Computer arithmetics
Algorithms
Applied sciences
Approximation
Computer science; control theory; systems
Computer systems performance. Reliability
Exact sciences and technology
Expected values
Job shops
Linear programming
Mathematical programming
Operational research and scientific management
Operational research. Management science
R&D
Random variables
Release dates
Research & development
Schedules
Scheduling
Scheduling, sequencing
Software
Theoretical computing
precedence constraints
68Q25
68W40
LP relaxation
Parallel machines
Linear programming
Processing time
Stochastic scheduling
Probability distribution
Scheduling
list scheduling algorithms
68W25
90C05
approximation algorithms
Approximation algorithm
Precedence constraint
Delay
90B36
release dates
Distribution function
LP-relaxation 68M20
ISSN:0097-5397, 1095-7111
Online Access:Get full text
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Summary:We consider parallel, identical machine scheduling problems, where the jobs are subject to precedence constraints and release dates, and where the processing times of jobs are governed by independent probability distributions. Our objective is to minimize the expected value of the total weighted completion time. Building upon a linear programming relaxation by Mohring, Schulz, and Uetz [J. ACM, 46 (1999), pp. 924--942] and a delayed list scheduling algorithm by Chekuri et al. [SIAM J. Comput., 31 (2001), pp. 146--166], we derive the first constant-factor approximation algorithms for this model.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0097-5397
1095-7111
DOI:10.1137/S0097539702415007