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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Vydané v:SIAM journal on computing Ročník 34; číslo 4; s. 788 - 802
Hlavní autori: Skutella, Martin, Uetz, Marc
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Philadelphia, PA Society for Industrial and Applied Mathematics 01.01.2005
Predmet:
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
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Abstract 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.
AbstractList 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.
Author Uetz, Marc
Skutella, Martin
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Cites_doi 10.1016/S0167-6377(03)00047-6
10.1145/1067629.806534
10.1137/S0097539797327180
10.1007/978-3-642-78240-4
10.1287/moor.17.2.255
10.1137/S0097539796299540
10.1145/331524.331530
10.1007/BFb0029561
10.1002/j.1538-7305.1966.tb01709.x
10.2307/3212936
10.1137/0117039
10.1002/(SICI)1099-1425(199909/10)2:5<203::AID-JOS26>3.0.CO;2-5
10.1016/S0167-5060(08)70356-X
10.1007/BF01919323
10.2307/3214267
10.1145/322234.322242
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Issue 4
Keywords 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
Language English
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Uetz Marc (R21) 2002
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SubjectTerms 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
Title Stochastic Machine Scheduling with Precedence Constraints
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Volume 34
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