Research On Chinese-style Bidding Optimization Based On Clustering Analysis

Due to changing market conditions and personalized demands, bidding has become a common transaction mode of Chinese construction and government procurement projects. Because all procurement activities are carried out in accordance with the tender documents which will directly affect the quality and progress of bidding, tender document has become the most instructive instrument of all of the files during the bidding process of government and medium-sized enterprises. Moreover, in the bidding cost structure, tender fees, expert assessment fees, taxes and other associated costs will be reduced while reducing the number of tenders, so as to achieve the purpose of bidding cost savings. Therefore, how to minimize the workload in case of meeting the procurement requirements, namely how to achieve the minimum bids and bid conferences, while also saving the bidding costs and improving efficiency is the key to the bidding optimization problem for those purchasers.This article is intended for government and medium-sized enterprises’bidding process, which aims at searching for the optimal bidding schemes based on collection of procurement materials/candidate suppliers/materials offered by candidate suppliers are known. It presented an optimization model with the goal of minimize the number of tenders based on material supply matrix by contrasted with Western reverse auction process, and then solved the problem with the help of cluster analysis algorithm and GA_TSP algorithm respectively. After that, compared the bidding process optimized results of both algorithms. The results showed that both clustering algorithms and GA_TSP algorithms are able to effectively classify the materials and obtain the optimal combination of tenders. Among them, the clustering algorithm runs faster and is more suitable for small samples on the bidding process optimization; while GA_TSP algorithm runs slower, but can get better operating results, which means that GA_TSP algorithm is more suitable for large sample bidding process optimization. Research findings have curtain guiding role to the actual bidding roles of government and medium-sized enterprises.