On a “No Arrivals” Heuristic for Single Machine Stochastic Scheduling

Fay, N. A.; Glazebrook, K. D.

doi:10.1287/opre.40.1.168

Cited by 5 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…While it is true that the indices solving Red's shooting/disengagement problem will now in general depend upon the vector λ of arrival rates, the target ordering implied by the indices is rarely different from that for the equivalent closed case λ = 0. See Fay and Glazebrook (1992) who discuss precisely the closeness to optimality of a so-called "no arrivals" index heuristic. Hence choosing between the Blue targets currently present on the basis of indices of the kind described in Sections 2 and 3 will be close to optimal for Red.…”

Section: Poisson Arrivals Of Multiple Blue Typesmentioning

confidence: 99%

Index Policies for Shooting Problems

Glazebrook

Kirkbride

Mitchell

et al. 2007

Operations Research

Self Cite

View full text Add to dashboard Cite

Public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instruction, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing this burden, ABSTRACT (maximum 200 words)We consider a scenario in which a single Red wishes to shoot at a collection of Blue targets, one at a time, to maximise some measure of return obtained from Blues killed before Red's own (possible) demise. Such a situation arises in various military contexts such as the conduct of air defence by Red in the face of Blue SEAD (suppression of enemy air defences). A class of decision processes called multi-armed bandits has been previously deployed to develop optimal policies for Red in which she attaches a calibrating (Gittins) index to each Blue target and optimally shoots next at the Blue with largest index value. The current paper seeks to elucidate how a range of developments of index theory are able to accommodate features of such problems which are of practical military import. Such features include levels of risk to Red which are policy dependent, Red having imperfect information about the Blues she faces, an evolving population of Blue targets and the possibility of Red disengagement. The paper concludes with a numerical study which both compares the performance of (optimal) index policies to a range of competitors and also demonstrates the value to Red of (optimal) disengagement. NUMBER OF PAGES 2714. SUBJECT TERMS multi-armed bandits, Gitten Indices, suppression of enemy air defense PRICE CODE SECURITY CLASSIFICATION OF REPORT Unclassified SECURITY CLASSIFICATION OF THIS PAGE Unclassified SECURITY CLASSIFICATION OF ABSTRACT Unclassified LIMITATION OF ABSTRACT UL iiIndex policies for shooting problems AbstractWe consider a scenario in which a single Red wishes to shoot at a collection of Blue targets, one at a time, to maximise some measure of return obtained from Blues killed before Red's own (possible) demise. Such a situation arises in various military contexts such as the conduct of air defence by Red in the face of Blue SEAD (suppression of enemy air defences). A class of decision processes called multi-armed bandits has been previously deployed to develop optimal policies for Red in which she attaches a calibrating (Gittins) index to each Blue target and optimally shoots next at the Blue with largest index value. The current paper seeks to elucidate how a range of developments of index theory are able to accommodate features of such problems which are of practical military import. Such features include levels of risk to Red which are policy dependent, Red having imperfect information about the Blues she faces, an evolving population of Blue targets and the possibility of Red disengagement. The paper concludes with a numerical study which both c...

show abstract

Section: Poisson Arrivals Of Multiple Blue Typesmentioning

confidence: 99%

Index Policies for Shooting Problems

Glazebrook

Kirkbride

Mitchell

et al. 2007

Operations Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…As with related problems, the condition±constrained osetting can be modeled with a series of interrelated heuristics (Fay and Glazebrook, 1992;Fisher and Kedia, 1990;Hackman et al, 1989;Homann, 1992;Murty, 1995;Talbot et al, 1986). Nearly ®ve hundred (500) heuristics were needed to model the Army's apportionment, crossleveling, redesignation and reallocation processes.…”

Section: Heuristicsmentioning

confidence: 99%

Minimizing army housing deficits with a DSS‐delivered heuristic programming model

Forgionne

1999

Int Trans Operational Res

View full text Add to dashboard Cite

The ineffcient use of existing onpost, or available offpost, assets can result in insuffcient or unnecessary Army housing construction requests. Insuffcient requests can leave Army personnel inadequately housed, compromise morale and jeopardize military preparedness, while unnecessary requests can waste scarce natural resources and, in the process, alienate local residents, environmentalists and other interest groups. This paper: (a) presents a decision‐support‐system‐delivered heuristic programming model that effciently utilizes scarce housing resources; and (b) demonstrates the significant economic and management benefits that can be achieved through the application of the model. The same concepts can be applied to the housing allocation problem faced by the other armed services, federal, state, local housing authorities and to related resource allocation problems in public and private enterprises.

show abstract

“…We conjecture that Gittins index policies (suitably defined) will continue to perform well for open problems even when they cannot be shown to be strictly optimal. See, for example, Fay and Glazebrook [2]. Any analysis of the nonpreemptive case with arrivals seems difficult.…”

Section: Conclusion and Extensionsmentioning

confidence: 99%