本文關(guān)鍵詞：非同質(zhì)和多流程數(shù)據(jù)包絡(luò)分析研究　出處：《中國(guó)科學(xué)技術(shù)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 數(shù)據(jù)包絡(luò)分析(DEA) 兩階段網(wǎng)絡(luò)DEA 非同質(zhì)DMUs 多流程DEA 雙性變量 副產(chǎn)品

【摘要】：如何公平評(píng)價(jià)決策單元(Decision Making Units,DMUs)相對(duì)于同行的效率,一直是近幾十年來(lái)研究的一個(gè)焦點(diǎn)。三十多年前誕生的數(shù)據(jù)包絡(luò)分析(Data Envelopment Analysis,DEA),是一種有效的評(píng)價(jià)多輸入和多輸出決策單元的相對(duì)效率的工具,廣泛應(yīng)用于DMUs的評(píng)價(jià)和改進(jìn)相對(duì)效率。許多研究者一直致力于利用DEA進(jìn)行研究,成千上萬(wàn)的雜志文章和書籍面世。最近研究的一個(gè)特定領(lǐng)域是利用網(wǎng)絡(luò)DEA測(cè)量DMUs的效率。兩階段DEA是網(wǎng)絡(luò)DEA研究的一個(gè)重要的子集,眾多的研究有不同形式的兩階段的分析。其中一種是封閉的串行流程,第一階段所有輸出作為中間變量進(jìn)人第二階段,成為唯一輸入,沒有任何元素離開此系統(tǒng)。研究的問題是如何將DMUs多階段的效率拆分成單個(gè)階段的效率值。一項(xiàng)重要的研究成果是利用博弈論和DEA理論結(jié)合去確定DMUs綜合效率,進(jìn)而確定單個(gè)階段的平均效率。隨后的重要科研成果之一是利用單個(gè)階段效率幾何平均和算術(shù)平均方法去確定DMUs總體效率。近年來(lái)另一個(gè)重要的研究方向是非同質(zhì)DMUs效率,非同質(zhì)現(xiàn)象有多種表現(xiàn)形式。例如：DMUs不同的輸出組合和不同的輸入組合。非同質(zhì)DMUs綜合效率第一步驟是DMUs通過內(nèi)部結(jié)構(gòu)劃分為一套互斥的子決策單元,子決策單元應(yīng)用和決策單元相同輸入和輸出指標(biāo),數(shù)據(jù)規(guī)模按分開的比例縮小。第二步驟是通過標(biāo)準(zhǔn)CCR模型評(píng)估子決策單元效率。第三步驟是通過子決策單元效率加權(quán)平均得出決策單元綜合效率。本論文的研究主題是非同質(zhì)DMUs和多流程的DEA效率評(píng)估。本論文創(chuàng)新性研究成果表現(xiàn)在以下四個(gè)方面：(1)處理非期望輸出的兩階段網(wǎng)絡(luò)DEA模型研究。近年來(lái),霧霾氣候的增多,人們?cè)絹?lái)越關(guān)注中國(guó)環(huán)境問題,不僅從經(jīng)濟(jì)發(fā)展的角度來(lái)看,也從人類生存的角度考慮。中國(guó)經(jīng)濟(jì)要達(dá)到可持續(xù)發(fā)展,管理者應(yīng)該有興趣從環(huán)境監(jiān)管和經(jīng)濟(jì)繁榮之間找平衡點(diǎn)。本研究提出應(yīng)用數(shù)據(jù)包絡(luò)分析(DEA)測(cè)量在中國(guó)不同地區(qū)生態(tài)系統(tǒng)的效率,有利于管理者發(fā)現(xiàn)借鑒高效率的省份的成功經(jīng)驗(yàn)。該方法不同于以往的生態(tài)系統(tǒng)模型,我們視生態(tài)系統(tǒng)作為兩階段網(wǎng)絡(luò)流程,第一階段是生態(tài)系統(tǒng)本身,第二階段是凈化系統(tǒng),第一階段的非期望輸出,部分經(jīng)過人工凈化系統(tǒng)凈化。凈化后水回收作為一個(gè)反饋的流程。我們分開污染氣體和消耗的水(被排出的污染的水)各自成為兩部分,一部分是處理過的,而另一部分是未經(jīng)處理直接丟棄排放到生態(tài)系統(tǒng)中又不能被生態(tài)系統(tǒng)循環(huán)和利用的污染氣體和污水。第一階段模型考慮兩個(gè)期望輸出,即人口和地區(qū)生產(chǎn)總值(Gross Region Product, GRP);以及非期望的輸出變量,消耗的水和某些污染氣體,例如：氮氧化物、二氧化硫和煙塵。同時(shí),第一階段這些非期望輸出到第二階段凈化階段作為輸入。凈化后的水反饋到生態(tài)系統(tǒng)第一階段。因此,中間變量如消耗的水(排出的污染的水)和排放廢氣同時(shí)扮演既是輸出又是輸入雙性變量角色。(2)部分輸入組合在多流程中產(chǎn)生指定輸出的DEA效率模型研究。數(shù)據(jù)包絡(luò)分析是一種用來(lái)測(cè)量相關(guān)同質(zhì)決策單元的效率方法。在最初模型中,假設(shè)DMUs是相同多輸入、多輸出指標(biāo),所有輸入指標(biāo)影響整體的輸出指標(biāo)。然而,在許多情況下這一假設(shè)并不成立。例如,在一個(gè)生產(chǎn)廠,包裝資源(一種輸入指標(biāo))只影響到需要包裝的這些產(chǎn)品。這推斷是部分的輸入指標(biāo)對(duì)特定輸出的影響,DEA模型是基于視一個(gè)DMU內(nèi)部劃分為一組獨(dú)立的子單元,這樣DMUs的效率可以被定義為一組子單元效率加權(quán)平均。當(dāng)前提出的擴(kuò)展方法以允許效率測(cè)量在多個(gè)流程生成指定地輸出。該模型應(yīng)用于評(píng)估一組鋼鐵制造廠的效率。(3)非同質(zhì)DEA模型關(guān)于不同的輸入組合DMUs研究。在早期的研究中,學(xué)者們研究輸出非同質(zhì)DMUs。具體地說,不是所有的DMUs都具有相同的輸出。早期的非同質(zhì)DMUs,具有相同的輸入指標(biāo),細(xì)分后的輸出指標(biāo)不同(生產(chǎn)產(chǎn)品不同)。在本章中,我們研究輸入組合非同質(zhì)DMUs的效率評(píng)估。以制造工廠為例,當(dāng)輸出組合可以使用生產(chǎn)機(jī)器,機(jī)器人和勞動(dòng)者,因此,一個(gè)DMU輸入組合不同于另一個(gè)DMU的輸入組合。作為實(shí)際應(yīng)用這一現(xiàn)象,本章分析中國(guó)各個(gè)省份的經(jīng)濟(jì)環(huán)境效率。所有省份都有期望輸出GRP,人口和非期望輸出,非期望輸出例如二氧化氮,二氧化硫和空氣中的粉塵等。然而,在輸入端這種共性是缺失的。所有省份有水資源、資本投資和其他自然資源,后者的(其他自然資源)有幾種不同的形式,即煤炭、天然氣和石油。并不是所有的省份都有相同的這些資源組合,也沒有明確的換算比率在這些不同資源間進(jìn)行換算,但是可替換輸入指標(biāo)。這意味著,不能直接應(yīng)用傳統(tǒng)DEA方法。這就引出一個(gè)問題,如何公正評(píng)估輸入組合不同的DMUs效率。為了解決這個(gè)問題,我們觀察各個(gè)省份的不同自然資源的組合在多流程中產(chǎn)生輸出。我們提出具有不同輸入組合的非同質(zhì)DEA模型,第一步驟是將DMU同質(zhì)的子決策單元?dú)w為一組,對(duì)同組里的決策單元進(jìn)行多流程劃分,得出子單元輸入和輸出數(shù)據(jù)：第二步驟是利用CCR模型對(duì)同質(zhì)子決策單元進(jìn)行評(píng)估,得出子決策單元的效率,第三步驟通過子決策單元效率加權(quán)平均得出決策單元綜合效率。通過以上三個(gè)步驟得出的DMUs綜合效率,使得在評(píng)價(jià)非同質(zhì)輸入資源的DMU效率更為公平可信。(4)產(chǎn)品與副產(chǎn)品關(guān)聯(lián)條件效率模型研究。本研究涉及部分輸入輸出的影響以及多流程DEA模型。早期工作沒有考慮副產(chǎn)品在輸出中的出現(xiàn),由此產(chǎn)生的產(chǎn)品與副產(chǎn)品關(guān)聯(lián)現(xiàn)象。模型的復(fù)雜化一方面在于產(chǎn)品假定兩個(gè)不同的角色,作為輸入指標(biāo)影響副產(chǎn)品產(chǎn)生,同時(shí)作為主要輸出指標(biāo)。另一個(gè)復(fù)雜的方面在于多個(gè)流程出現(xiàn),副產(chǎn)品經(jīng)常出現(xiàn)在這些流程的一個(gè)子集中。我們開發(fā)一類基于DEA的方法模型處理在多流程中產(chǎn)生產(chǎn)品和副產(chǎn)品關(guān)聯(lián)條件。
[Abstract]:How fair evaluation of decision making units (Decision Making Units, DMUs) relative to the peer efficiency, has been a focus of research in recent decades. Data envelopment analysis was born more than 30 years ago (Data Envelopment, Analysis, DEA) is a kind of effective evaluation of multi input and multi output relative efficiency of decision making units tools. The relative efficiency evaluation and improvement are widely used in DMUs. Many researchers have been committed to the use of DEA research, the tens of thousands of magazine articles and books published. A specific area in recent research is the utilization efficiency of network DEA measurement of DMUs. The two stage DEA is an important subset of the DEA research network, there are many studies analysis of two different stages form. One is the serial process closed, the first stage of all output as intermediate variables in the second stage, as the only input, without any elements left Study on this system. The problem is how to split the efficiency of multi stage DMUs into a single stage efficiency value. An important research achievement is combined to determine the comprehensive efficiency of DMUs by using game theory and DEA theory, and then determine the average efficiency of single stage. Then one of the important achievements in scientific research is the use of single stage efficiency geometric mean and the arithmetic average method to determine the overall efficiency of DMUs. In recent years, another important research direction is non homogeneous DMUs efficiency, non homogeneity phenomenon has a variety of forms. For example: DMUs output of different combinations and different input combinations. The first step is the comprehensive efficiency of DMUs decision-making unit through the internal structure is divided into DMUs a set of mutually exclusive, decision-making unit application and decision-making units of the same input and output indicators, the data size separated by proportion. Second steps through the standard CCR assessment model Efficiency of decision making units. Third steps through the decision-making unit efficiency derived weighted average comprehensive efficiency of decision making units. The theme of this paper is non homogeneous DMUs and multi process DEA efficiency evaluation. This thesis innovative research results in the following four aspects: (1) research on the two stage DEA network model of non expected outputs processing. In recent years, the increase in haze weather, people pay more and more attention to China environmental problems, not only from the perspective of economic development, but also from the human point of view. Chinese economy to achieve sustainable development, managers should be interested in between environmental regulation and economic prosperity to find the balance point. This study proposes the application of data analysis the envelope (DEA) measurement in the efficiency of different ecosystem China, is conducive to find out successful experience of high efficiency provinces. This method is different from the previous model of ecological system, We consider the ecological system as the two stage of the network flow, the first stage is the ecological system itself, the second stage is the purification system, non expected output of the first stage, in part through artificial purification purification system. Purified water recycling as a feedback process. We separate the polluted gas and water consumption (by the discharge polluted water two) each become part of a part is processed, and the other part is untreated discarded directly discharged into the ecosystem can not be polluted gas and sewage ecosystem circulation and utilization. The first stage model considering two output, population and GDP (Gross Region, Product, GRP); and non expected output variables, the consumption of water and some polluting gases, such as nitrogen oxides, sulfur dioxide and smoke. At the same time, the first stage of these undesirable outputs to the second stage purification stage As the input. The purified water back to the first stage of ecological system. Therefore, the intermediate variables such as consumption of water (water pollution) emissions and output is also play both input variables. The role of bisexual (2) of DEA efficiency model are combined to produce a specified portion of the input output in the process of data envelopment. The analysis is a measure of the efficiency of decision making units related to homogeneous method. In the first model, assuming that DMUs is the same as multi input, multi output index, the output index overall all input index. However, this assumption does not hold in many situations. For example, in a factory for the production of packaging resources (a input indicators) only affect the need for packaging of these products. This inference is the input index influence on the part of the specific output, the DEA model is a DMU based on the partition for a group of independent sub units, so the efficiency of DMUs Can be defined as a group of sub unit efficiency. The weighted average method is proposed to allow the extension of the current generation of specified output efficiency measurement in multiple processes. This model was applied to evaluate the efficiency of a group of iron and steel factory. (3) non homogeneous DEA model on input combinations of DMUs. In the early studies, the scholars study output non homogeneous DMUs. specifically, not all DMUs have the same output. Early non homogeneous DMUs, with the same input indicators, output indicators after the breakdown of the different (the production of different products). In this chapter, we study the input combination evaluation efficiency of non homogeneous DMUs. Manufacturing plant for example, when the output you can use a combination of production machines, robots and workers, therefore, a combination of input DMU input combination is different from another DMU. As an application of this phenomenon, this chapter analyzes the various provinces by Chinese Economic and environmental efficiency. All provinces have the expected output GRP, population and non expected output, non expected outputs such as nitrogen dioxide, sulfur dioxide and dust in the air. However, at the input end of the common deletion. All provinces have water resources, capital investment and other natural resources, the latter (natural resources) there are several different forms, namely coal, gas and oil. Not all provinces have the same combination of resources, there is no explicit conversion ratio conversion in these different resources, but can replace the input index. This means that the traditional DEA method can not be applied directly. This leads to a problem how, impartial assessment of the different input combinations of DMUs efficiency. In order to solve this problem, a combination of different natural resources we observe various provinces to generate output in many processes. We present a different input group Non homogeneous DEA model, the first step is the decision-making unit of DMU homogeneous are classified as a group, multi process classification for decision making units in the same group, the sub unit of input and output data: the second step is to evaluate the same proton decision unit using the CCR model, the efficiency of decision-making unit, third steps through the decision-making unit efficiency of decision making units that the weighted average comprehensive efficiency. Through the comprehensive efficiency of DMUs above three steps that make it more credible in the evaluation of non homogeneous input resource efficiency. DMU (4) contact conditions study efficiency model of products and by-products. This research involves part of input and output and the influence the process model of DEA. Early work did not consider the products appear in the output, resulting in the phenomenon of associated products and by-products. A complex model is a product of two different assumptions The role, produced as input indicators affect products, also as the main output index. Another aspect is a complex process, products often appear in a subset of these processes. We developed a method based on the DEA model in multi process produces products and by-products related conditions.

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2016
【分類號(hào)】：F224

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