Stochastic scheduling: A short history of index policies and new approaches to index generation for dynamic resource allocation

Stochastic scheduling: A short history of index policies and new approaches to index generation for dynamic resource allocation
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随机调度:索引策略的简史和用于动态资源分配的索引生成新方法

DOI:
10.1007/s10951-013-0325-1
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发表时间:
2013
影响因子:
2
通讯作者:
Glazebrook K
Glazebrook K
中科院分区:
工程技术4区
文献类型:
--
作者:
Glazebrook K

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在20世纪70年代,S·约翰·吉廷斯发现了多武装强盗,这是一类重要的模型,用于在一组相互竞争的项目中动态分配单一关键资源,具有信息的最优解。在每个决策阶段,这样的政策将资源分配给具有最大Gittins指数的项目。自20世纪70年代S以来,Gittins的指标结果及其一系列的发展和重新表述构成了对随机目标排序研究有影响的一系列思想和结果。我们简要介绍了这项工作的许多最重要的贡献,并接着描述了最近如何发展指数理论来产生强性能的启发式策略,用于将可分资源动态分配给一组随机项目(或土匪)。这项工作的一个限制涉及到对结构要求的灵活性的需要,这是出了名的难以确定。我们引入了一个通用框架,用于开发动态资源分配的索引策略,从而绕过了这一困难。我们利用这个框架为两个独立兴趣的模型类生成索引策略。它们的性能在广泛的数值研究中得到了评估。
In the 1970’s John Gittins discovered thatmulti-armed bandits, an important class of models for the dynamic allocation of a single key resource among a set of competing projects, have optimal solutions ofindexform. At each decision epoch such policies allocate the resource to whichever project has the largestGittins index. Since the 1970’s, Gittins’ index result together with a range of developments and reformulations of it have constituted an influential stream of ideas and results contributing to research into the scheduling of stochastic objects. We give a brief account of many of the most important contributions to this work and proceed to describe how index theory has recently been developed to produce strongly performing heuristic policies for the dynamic allocation of a divisible resource to a collection of stochastic projects (orbandits). A limitation on this work concerns the need for the structural requirement ofindexabilitywhich is notoriously difficult to establish. We introduce a general framework for the development of index policies for dynamic resource allocation which circumvents this difficulty. We utilise this framework to generate index policies for two model classes of independent interest. Their performance is evaluated in an extensive numerical study.
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