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Reducing the Number of Samples in Distributed Cooperative Solution Method for Resource Supply Networks

机译:资源供应网络的分布式合作求解方法中减少样本数量

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Distributed Constraint Optimization Problems (DCOPs) are applied to resource allocation problems in resource supply networks. In previous studies, distributed cooperative solution methods based on feeder trees have been utilized. However, in most cases with resource supply networks, the size of variable's domains in the problems is very large, since the variables originally take continuous values. This is critical even if the networks are trees because it increases the number of combinations. Therefore, sampling of solutions is necessary to restrict the size of the problems. In this study, we propose methods to reduce the number of samples for resource allocation problems of resource supply networks. To maintain the feasibility with the samples, boundaries for the resource amount and cost values were introduced. With the proposed methods, the size of problems is reduced while the methods keep relatively better feasibility and quality of the solutions.
机译:分布式约束优化问题(DCOP)适用于资源供应网络中的资源分配问题。在以前的研究中,已经使用了基于馈线树的分布式协作解决方案方法。但是,在大多数情况下,使用资源供应网络时,问题中变量域的大小非常大,因为变量最初取连续值。即使网络是树,这也很重要,因为它增加了组合的数量。因此,有必要对解决方案进行抽样以限制问题的规模。在这项研究中,我们提出了减少资源供应网络中资源分配问题的样本数量的方法。为了保持样本的可行性,引入了资源数量和成本值的界限。利用所提出的方法,减小了问题的规模,同时该方法保持了相对较好的可行性和解决方案的质量。

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