Submodular Returns and Greedy Heuristics for Queueing Scheduling Problems

R. Garbe and K. D. Glazebrook
Additional contact information
R. Garbe: Newcastle University, Newcastle upon Tyne, United Kingdom
K. D. Glazebrook: Newcastle University, Newcastle upon Tyne, United Kingdom

Operations Research, 1998, vol. 46, issue 3, 336-346

Abstract: We consider a range of controlled stochastic systems satisfying conservation laws together with a reducibility property that says that appropriate laws continue to hold when access to the system is restricted to a subset of all possible demand (job, customer) types. We show that for linear objectives, the optimum system-wide performance is a nondecreasing submodular (or supermodular) function of the subset chosen and that these properties are inherited from the geometry of the performance space concerned. These results are of considerable interest in their own right, but they also motivate the use of greedy heuristics for the solution of a range of job selection and scheduling problems which have hitherto seemed intractable. Computational experience suggests that such heuristics perform very well.

Keywords: Dynamic programming/optimal control; characterisation of optimal return functions; Production/scheduling; selection of jobs in advance of scheduling; Queues; Optimisation of multiclass queueing systems (search for similar items in EconPapers)
Date: 1998
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.46.3.336 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:46:y:1998:i:3:p:336-346

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().