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Bid/no-bid Decision-making Using Rough Sets and Neural Networks

机译:使用粗糙集和神经网络进行投标/不投标决策

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摘要

One of the most important decisions that have to be made by contractor firms is whether to bid or not to bid for a project, when an invitation has been received. For any construction company, being able to deal successfully with various bidding situations is of crucial importance, Especially in today's highly competitive construction market. The frame work presented in this study integrated methodology of rough set (RS) and artificial neural network (ANN) will serve as a basis for a knowledge-based system model which will guide the contracting organizations in reaching strategically correct bid/no bid and make decisions. Using rough sets, we can get reduced information table, which implies that the number of evaluation criteria such as reputation of company and risks of project is reduced with no information loss through rough set approach. And then, this reduced information is used to develop classification rules and train neural network to infer appropriate parameters. The proposed decision support system framework are of good value to contracting organizations in different construction markets.
机译:承包商公司必须做出的最重要的决定之一是,在收到邀请后是否投标项目。对于任何建筑公司而言,能够成功应对各种投标情况至关重要,尤其是在当今竞争激烈的建筑市场中。这项研究中提出的框架工作结合了粗糙集(RS)和人工神经网络(ANN)的综合方法,将为基于知识的系统模型提供基础,该模型将指导订约组织达成战略上正确的标书/不标书并做出决定。使用粗糙集,我们可以得到简化的信息表,这意味着通过粗糙集方法可以减少诸如公司声誉和项目风险等评估标准的数量,而不会造成信息损失。然后,将这些减少的信息用于开发分类规则和训练神经网络以推断适当的参数。拟议的决策支持系统框架对于不同建筑市场中的承包组织具有很好的价值。

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