基于大数据的最优招标方案选取算法
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摘要
传统招标方案选取算法通过建立指标属性矩阵和直觉模糊线性评价模型进行最优招标方案选取,没有考虑大数据处理上的时间性,导致选取结果效率低,准确性差。为此提出基于大数据的最优招标方案选取算法,对全部招标方案进行聚类,通过大数据抽样加快对招标方案的大数据处理进程;选用Single簇质心方位;利用均值更新方法对大数据自然簇质心的方位进行修改以确定大数据自然簇质心的实质方位,在此基础上对招标方案集进行划分聚类。实现招标方案的聚类后,构建投影寻踪模型进行最优招标方案的选取。实验结果表明,所提算法聚类误差低于7%,准确性高达93%,并且在计算速度方面具有较大优势。
The traditional bidding scheme selection algorithm makes the optimal bidding scheme selection by establishing the index attribute matrix and intuitive fuzzy linear evaluation model,and does not consider the timing of big data processing,resulting in low efficiency and poor accuracy of the selected results. Therefore,an optimal bidding scheme selection algorithm based on big data is put forward. All the bidding schemes are clustered. The big data processing process of bidding schemes is accelerated by means of big data sampling. The Single method is selected to cluster the results of big data sampling,so as to determine the centroid orientation for the natural cluster of big data. The mean value updating method is adopted to modify the centroid orientation for the natural cluster of big data,so as to determine the actual centroid orientation for the natural cluster of big data. On this basis,the classification and clustering of the bidding schemes are conducted. The projection tracing model is constructed to select the optimal bidding scheme. The experimental results show that the proposed algorithm has a clustering error of less than 7%,accuracy of as high as 93%,and a great advantage in computing speed.
The traditional bidding scheme selection algorithm makes the optimal bidding scheme selection by establishing the index attribute matrix and intuitive fuzzy linear evaluation model,and does not consider the timing of big data processing,resulting in low efficiency and poor accuracy of the selected results. Therefore,an optimal bidding scheme selection algorithm based on big data is put forward. All the bidding schemes are clustered. The big data processing process of bidding schemes is accelerated by means of big data sampling. The Single method is selected to cluster the results of big data sampling,so as to determine the centroid orientation for the natural cluster of big data. The mean value updating method is adopted to modify the centroid orientation for the natural cluster of big data,so as to determine the actual centroid orientation for the natural cluster of big data. On this basis,the classification and clustering of the bidding schemes are conducted. The projection tracing model is constructed to select the optimal bidding scheme. The experimental results show that the proposed algorithm has a clustering error of less than 7%,accuracy of as high as 93%,and a great advantage in computing speed.
引文
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[3]史金梅,夏伟.基于大数据分析的学生最优选课方案模型的设计与实现[J].现代电子技术,2017,40(14):30?32.SHI Jinmei,XIA Wei. Design and implementation of student′smost preferred course project model based on big data analysis[J]. Modern electronics technique,2017,40(14):30?32.
[4]郭磊,王军,安晓伟.基于直觉模糊集的水利工程评标办法[J].南水北调与水利科技,2016,14(5):189?193.GUO Lei,WANG Jun,AN Xiaowei. Bidding evaluation modelof water conservancy and hydropower project based on theoryof intuitionistic fuzzy set[J]. South?to?north water transfers andwater science&technology,2016,14(5):189?193.
[5]丁斅,盛昭瀚,刘慧敏.基于模糊综合分析和Gale?Shaplev理论的重大工程二阶段招投标机制研究[J].中国管理科学,2017,25(2):147?154.DING Xiao,SHENG Zhaohan,LIU Huimin. A two?stage meth?od for mega projects bidding system based on fuzzy analytic hi?erarchy process and Gale?Shapley strategy[J]. Chinese journalof management science,2017,25(2):147?154.
[6]马良,马颖亮,刘新科.基于“招标?投标”策略的舰艇编队协同反导优化[J].火力与指挥控制,2015,40(5):95?98.MA Liang,MA Yingliang,LIU Xinke. Fleet cooperative anti?missile optimization based on strategy of"invite public bidding"[J]. Fire control&command control,2015,40(5):95?98.
[7]王应权.长大铁路隧道施工通风方案选择及优化[J].地下空间与工程学报,2015,11(z1):359?366.WANG Yingquan. The selection and optimization of ventilationscheme for long railway tunnel construction[J]. Chinese journalof underground space and engineering,2015,11(S1):359?366.
[8] LEE S,JIN Y,JANG G,et al. Optimal bidding of a microgridbased on probabilistic analysis of island operation[J]. Ener?gies,2016,9(10):814.
[9] SADEGHI?MOBARAKEH A,MOHSENIAN?RAD H. Optimalbidding in performance?based regulation markets:an MPECanalysis with system dynamics[J]. IEEE transactions on powersystems,2017,32(2):1282?1292.
[10]宋俊芳,陈烽,何磊,等.运动目标最优角点选择算法[J].科学技术与工程,2016,16(12):113?119.SONG Junfang,CHEN Feng,HE Lei,et al. A new algorithmfor selecting optimal corner point of moving target[J]. Sciencetechnology and engineering,2016,16(12):113?119.