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基于層次分析法的同行評議專家反評估模型研究

發(fā)布時間:2018-06-21 06:57

  本文選題:反評估 + 評審專家; 參考:《河北工業(yè)大學(xué)》2015年碩士論文


【摘要】:科技項目在現(xiàn)代社會的發(fā)展中占據(jù)著舉足輕重的地位,同時科技項目活動很復(fù)雜,影響因素眾多?萍柬椖砍袚(dān)一定風(fēng)險,本文需要對同行評議專家進(jìn)行反評估,從而為科技項目評審活動篩選高水平的專家,保證評審結(jié)果公平公正,使科研資金得以高效利用。通過使用同行評議專家反評估模型可以對參與科技項目評審的專家進(jìn)行反評價,找出存在的問題并改進(jìn),從而應(yīng)用到科技項目評審專家遴選工作中,因此無論是從實踐還是理論上都迫切需要建立同行評議專家反評估模型。本文首先綜合分析專家反評估的課題來源、研究意義、研究現(xiàn)狀,層次分析法的研究現(xiàn)狀及目前存在的問題。在研究層次分析法現(xiàn)存問題的基礎(chǔ)上,整理層次分析法改進(jìn)的思路方法。通過上述工作,對層次分析法有了更深層次的理解并開始著手將層次分析法的改進(jìn)運(yùn)用到本課題的研究背景中?萍柬椖吭u審的同行評議體系最重要因素是指標(biāo)體系。指標(biāo)體系是建立層次結(jié)構(gòu)模型的基礎(chǔ),也是所求權(quán)重的核心。目前反評估的指標(biāo)數(shù)量非常多,本文結(jié)合研究背景對現(xiàn)有指標(biāo)體系進(jìn)行了調(diào)整,建立了適合本文研究背景的指標(biāo)體系。下一步工作是確定指標(biāo)體系的權(quán)重:在判斷矩陣的建立過程中,同時考慮科技項目評審總負(fù)責(zé)人、科技項目評審專家以及科技項目管理人員三方面專家的意見,使用matlab實現(xiàn)改進(jìn)的層次分析法。建立同行評議專家反評估模型,將反評估指標(biāo)體系與權(quán)重指標(biāo)相結(jié)合計算得到評審專家的綜合得分。最后進(jìn)行驗證,應(yīng)用反評估模型對專家進(jìn)行反評估。第一,根據(jù)課題背景整理實驗數(shù)據(jù);第二,編寫功能模塊,該模塊實現(xiàn)功能:輸入實驗數(shù)據(jù),輸出專家的量化評分,并進(jìn)行排序;第三,根據(jù)往年的評審工作總結(jié)驗證專家量化評分結(jié)果的科學(xué)合理性。實證分析表明,同行評議專家反評估模型可以有效提高科技項目評審專家遴選質(zhì)量,從而提高科技項目評審工作質(zhì)量。
[Abstract]:Science and technology projects play an important role in the development of modern society. Science and technology project bears certain risk, this paper needs to carry on the counter-evaluation to the peer review expert, so as to screen the high level experts for the science and technology project review activity, guarantee the evaluation result fair and just, and make the scientific research fund be used efficiently. Through the use of peer review expert counter-evaluation model, experts involved in the review of science and technology projects can be re-evaluated, problems identified and improved, so as to apply to the selection of experts in science and technology project review. Therefore, both in practice and in theory, it is urgent to establish peer-review expert anti-evaluation model. In this paper, the origin, significance, research status, analytic hierarchy process (AHP) and existing problems of expert backassessment are analyzed. On the basis of studying the existing problems of Analytic hierarchy process (AHP), the improved method of AHP is sorted out. Through the above work, the analytic hierarchy process has a deeper understanding and begins to apply the improvement of the analytic hierarchy process to the research background of this subject. The most important factor of peer review system is the index system. Index system is the foundation of building hierarchy model and the core of weight. At present, there are a lot of indexes in the counter-evaluation. This paper adjusts the existing index system with the background of the research, and establishes the index system suitable for the research background of this paper. The next step is to determine the weight of the indicator system: in the process of establishing the judgment matrix, the opinions of the chief executive of the science and technology project review, the science and technology project review expert, and the science and technology project manager are taken into account. Using matlab to implement the improved AHP. The evaluation experts' comprehensive score is obtained by combining the evaluation index system with the weight index. At last, the back-evaluation model is used to evaluate the experts. First, collate the experimental data according to the background of the subject; second, write the function module, this module realizes the function: input the experimental data, output the expert's quantitative score, and carry on the sort; third, According to the review work in previous years, the scientific rationality of the expert quantitative evaluation results is verified. The empirical analysis shows that the peer-reviewed expert counter-evaluation model can effectively improve the selection quality of science and technology project review experts and thus improve the quality of science and technology project review.
【學(xué)位授予單位】:河北工業(yè)大學(xué)
【學(xué)位級別】:碩士
【學(xué)位授予年份】:2015
【分類號】:TP182


本文編號:2047670

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