基于DEA方法的多產區(qū)生產配送一體化問題研究
發(fā)布時間：2018-09-11 13:16
【摘要】：在當今激烈的競爭環(huán)境下,越來越多的優(yōu)化模型和算法、決策支持系統(tǒng)、程序化分析工具等等方法被用來改善企業(yè)整體運營績效,以此幫助企業(yè)在當今激烈的產品競爭中獲得長期的競爭優(yōu)勢。 本文提出的生產配送一體化模型主要考慮的是如何最優(yōu)的決定該問題中生產環(huán)節(jié)和運輸環(huán)節(jié)的各個相關變量,其中生產環(huán)節(jié)主要涉及投入變量和產出變量,配送環(huán)節(jié)主要涉及各個分銷中心之間的是否存在運輸及對應的運輸量。 本文相對于之前的研究主要有兩點區(qū)別,其一是我們在考慮生產計劃時不假設任何先驗信息。特別的,本文將數(shù)據(jù)包絡分析(data envelopment analysis,DEA)引入到統(tǒng)一的生產運輸問題當中。數(shù)據(jù)包絡分析作為一個非參數(shù)的方法,不同于之前該領域的用來描述生產關系的已有方法。在當下,已經(jīng)有很多涉及到統(tǒng)一的生產和配送規(guī)劃問題,其中有些考慮跨期操作的甚至包括了對于庫存管理的運籌。但是這些文章對于生產關系的刻畫都是添加了很多外生假設的。數(shù)據(jù)包絡分析是一個提供滿意解的主要工具,也就意味著本文提出的模型是基于中央決策者所能獲得有限消息下做出的最滿意的決策。 另外一個不同之處在于本文在處理生產關系中將生產關系從確定性情形中進一步推廣到隨機性情形。事實上,不確定性是處處存在的,而不是生活中的一個特例。在本質上生產過程都是具有不確定性的。例如,在勞動力密集型產業(yè)中,產品將會受到工人情緒的影響。隨著機械化的不斷普及和提高,生產過程將受到機器故障、停電等其他不可控制的因素的影響。這種隨機性情形拓展了本文的應用范圍,縮小了理論和實際的距離,能更好的反應生產過程中的實際情況。 此外,本文分別針對確定型和隨機型情景下的模型進行了最優(yōu)解存在性的討論,以及二者的最優(yōu)解的生產效率問題和兩個情景下最優(yōu)解的比較。本文使用了一個統(tǒng)一的例子來具體展示文中的一系列模型,在確定型情景中還使用了靈敏度分析,即不斷變換單位投入費用所在的數(shù)量級與單位運輸費用所在的數(shù)量級之比的比值,而在隨機型情景中分別給出了在三個置信水平下的解,用以加深對于模型理解。
[Abstract]:In today's competitive environment, more and more optimization models and algorithms, decision support systems, program analysis tools and other methods are used to improve the overall operating performance of enterprises. In order to help enterprises in today's fierce product competition to obtain a long-term competitive advantage. The integrated model of production and distribution proposed in this paper is mainly concerned with how to determine the optimal variables of the production and transportation links, in which the production links mainly involve input variables and output variables. Distribution is mainly related to the existence of transportation between distribution centers and the corresponding traffic volume. There are two main differences between this paper and previous studies. One is that we do not assume any prior information when considering production planning. In particular, this paper introduces the data Envelopment Analysis (data envelopment analysis,DEA) into the unified production and transportation problems. As a nonparametric method, data Envelopment Analysis (DEA) is different from the existing methods used to describe the relations of production in this field. At present, there are many problems related to unified production and distribution planning, some of which consider intertemporal operations, including even inventory management. However, these articles add a lot of exogenous assumptions to the depiction of the relations of production. Data envelopment analysis is a main tool to provide satisfactory solutions, which means that the model proposed in this paper is based on the most satisfactory decision made by central decision makers under limited information. The other difference lies in the fact that the relations of production are further extended from deterministic to random in dealing with the relations of production in this paper. In fact, uncertainty exists everywhere, not as a special case in life. In essence, the production process is uncertain. In labor-intensive industries, for example, products will be affected by worker sentiment. With the popularization and improvement of mechanization, the production process will be affected by other uncontrollable factors such as machine failure, power failure and so on. This random situation expands the scope of application of this paper, reduces the distance between theory and practice, and can better reflect the actual situation in the process of production. In addition, this paper discusses the existence of optimal solutions for the models under deterministic and stochastic scenarios, and compares the production efficiency of the optimal solutions between the two scenarios and the optimal solutions under the two scenarios. In this paper, a unified example is used to show a series of models in the text, and sensitivity analysis is also used in deterministic scenarios. That is to say, the ratio of the order of magnitude of the unit input cost to the order of magnitude of the unit transportation cost is constantly changed, and the solution under the three confidence levels is given in the random scenario to deepen the understanding of the model.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：O221.4;F274
本文編號：2236787
[Abstract]:In today's competitive environment, more and more optimization models and algorithms, decision support systems, program analysis tools and other methods are used to improve the overall operating performance of enterprises. In order to help enterprises in today's fierce product competition to obtain a long-term competitive advantage. The integrated model of production and distribution proposed in this paper is mainly concerned with how to determine the optimal variables of the production and transportation links, in which the production links mainly involve input variables and output variables. Distribution is mainly related to the existence of transportation between distribution centers and the corresponding traffic volume. There are two main differences between this paper and previous studies. One is that we do not assume any prior information when considering production planning. In particular, this paper introduces the data Envelopment Analysis (data envelopment analysis,DEA) into the unified production and transportation problems. As a nonparametric method, data Envelopment Analysis (DEA) is different from the existing methods used to describe the relations of production in this field. At present, there are many problems related to unified production and distribution planning, some of which consider intertemporal operations, including even inventory management. However, these articles add a lot of exogenous assumptions to the depiction of the relations of production. Data envelopment analysis is a main tool to provide satisfactory solutions, which means that the model proposed in this paper is based on the most satisfactory decision made by central decision makers under limited information. The other difference lies in the fact that the relations of production are further extended from deterministic to random in dealing with the relations of production in this paper. In fact, uncertainty exists everywhere, not as a special case in life. In essence, the production process is uncertain. In labor-intensive industries, for example, products will be affected by worker sentiment. With the popularization and improvement of mechanization, the production process will be affected by other uncontrollable factors such as machine failure, power failure and so on. This random situation expands the scope of application of this paper, reduces the distance between theory and practice, and can better reflect the actual situation in the process of production. In addition, this paper discusses the existence of optimal solutions for the models under deterministic and stochastic scenarios, and compares the production efficiency of the optimal solutions between the two scenarios and the optimal solutions under the two scenarios. In this paper, a unified example is used to show a series of models in the text, and sensitivity analysis is also used in deterministic scenarios. That is to say, the ratio of the order of magnitude of the unit input cost to the order of magnitude of the unit transportation cost is constantly changed, and the solution under the three confidence levels is given in the random scenario to deepen the understanding of the model.
【學位授予單位】：中國科學技術大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：O221.4;F274
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