【摘要】：在當(dāng)今激烈的競(jìng)爭(zhēng)環(huán)境下,越來(lái)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(lái)改善企業(yè)整體運(yùn)營(yíng)績(jī)效,以此幫助企業(yè)在當(dāng)今激烈的產(chǎn)品競(jìng)爭(zhēng)中獲得長(zhǎng)期的競(jìng)爭(zhēng)優(yōu)勢(shì)。 本文提出的生產(chǎn)配送一體化模型主要考慮的是如何最優(yōu)的決定該問(wèn)題中生產(chǎn)環(huán)節(jié)和運(yùn)輸環(huán)節(jié)的各個(gè)相關(guān)變量,其中生產(chǎn)環(huán)節(jié)主要涉及投入變量和產(chǎn)出變量,配送環(huán)節(jié)主要涉及各個(gè)分銷中心之間的是否存在運(yùn)輸及對(duì)應(yīng)的運(yùn)輸量。 本文相對(duì)于之前的研究主要有兩點(diǎn)區(qū)別,其一是我們?cè)诳紤]生產(chǎn)計(jì)劃時(shí)不假設(shè)任何先驗(yàn)信息。特別的,本文將數(shù)據(jù)包絡(luò)分析(data envelopment analysis,DEA)引入到統(tǒng)一的生產(chǎn)運(yùn)輸問(wèn)題當(dāng)中。數(shù)據(jù)包絡(luò)分析作為一個(gè)非參數(shù)的方法,不同于之前該領(lǐng)域的用來(lái)描述生產(chǎn)關(guān)系的已有方法。在當(dāng)下,已經(jīng)有很多涉及到統(tǒng)一的生產(chǎn)和配送規(guī)劃問(wèn)題,其中有些考慮跨期操作的甚至包括了對(duì)于庫(kù)存管理的運(yùn)籌。但是這些文章對(duì)于生產(chǎn)關(guān)系的刻畫都是添加了很多外生假設(shè)的。數(shù)據(jù)包絡(luò)分析是一個(gè)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(gè)不同之處在于本文在處理生產(chǎn)關(guān)系中將生產(chǎn)關(guān)系從確定性情形中進(jìn)一步推廣到隨機(jī)性情形。事實(shí)上,不確定性是處處存在的,而不是生活中的一個(gè)特例。在本質(zhì)上生產(chǎn)過(guò)程都是具有不確定性的。例如,在勞動(dòng)力密集型產(chǎn)業(yè)中,產(chǎn)品將會(huì)受到工人情緒的影響。隨著機(jī)械化的不斷普及和提高,生產(chǎn)過(guò)程將受到機(jī)器故障、停電等其他不可控制的因素的影響。這種隨機(jī)性情形拓展了本文的應(yīng)用范圍,縮小了理論和實(shí)際的距離,能更好的反應(yīng)生產(chǎn)過(guò)程中的實(shí)際情況。 此外,本文分別針對(duì)確定型和隨機(jī)型情景下的模型進(jìn)行了最優(yōu)解存在性的討論,以及二者的最優(yōu)解的生產(chǎn)效率問(wèn)題和兩個(gè)情景下最優(yōu)解的比較。本文使用了一個(gè)統(tǒng)一的例子來(lái)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(fèi)用所在的數(shù)量級(jí)與單位運(yùn)輸費(fèi)用所在的數(shù)量級(jí)之比的比值,而在隨機(jī)型情景中分別給出了在三個(gè)置信水平下的解,用以加深對(duì)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(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：O221.4;F274

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