本文關鍵詞： 不確定性 魯棒優(yōu)化 綜合生產(chǎn)計劃 LINGO　出處：《南京理工大學》2014年碩士論文　論文類型：學位論文

【摘要】：在當今市場經(jīng)濟條件下,產(chǎn)品的生命周期越發(fā)短暫,市場競爭異常激烈,客戶需求變化不定,加之供應鏈的網(wǎng)狀結構等,這些因素使得供應鏈中存在諸多不確定性。供應鏈中的不確定性會給供應鏈帶來一定的風險,嚴重時甚至摧毀整個供應鏈。因此需要運用不確定性優(yōu)化方法來降低不確定性給供應鏈帶來的風險,將不確定性因素降至最低,從而提高供應鏈中企業(yè)的競爭力。在生產(chǎn)管理中,綜合生產(chǎn)計劃是一項重要的技術水平規(guī)劃；在規(guī)劃領域,它是介于具有寬泛決策的長期規(guī)劃和具有詳細決策的短期規(guī)劃之間的。因此,研究供應鏈的綜合生產(chǎn)計劃具有重要意義。 本文研究的是這樣一個供應鏈綜合生產(chǎn)計劃：供應鏈中有一個供應商,多個制造廠區(qū)和多個客戶區(qū)。其中制造廠商所需的所有原材料都由這個供應商供應,而每個客戶區(qū)所需的產(chǎn)品可以由任意一個或多個制造廠區(qū)供應。期間,每個制造廠區(qū)可以通過雇傭和解雇的方式調節(jié)勞動力,通過外包和加班的形式來改變生產(chǎn)力。本文首先對供應鏈綜合生產(chǎn)計劃這一概念進行界定,接著對供應鏈綜合生產(chǎn)計劃中存在的不確定性按照不同分類方式進行了歸納總結,在整個不確定性環(huán)境中,本文選取需求和成本這兩種不確定性來進行研究,使用情景分析法對成本和需求不確定性進行描述,并選取魯棒優(yōu)化方法對供應鏈綜合生產(chǎn)計劃進行建模,分別為最小化損失和最大化市場需求滿足率,并構造了合適的罰函數(shù)。該魯棒優(yōu)化模型不僅包含了損失最小化目標,還引進了市場滿足率最大化這一目標。對于多目標求解,文中給出了處理方式,即采用歸一化的手法,將多目標模型轉化成單目標模型進行求解。為了驗證模型的有效性和可行性,本文進行了算例設計,基于LINGO軟件對所設計的算例求解。文章的最后對基于LINGO軟件的求解結果進行了分析,不僅分析了目標函數(shù)隨著風險系數(shù)變化的變化趨勢,還分析了目標函數(shù)隨權重系數(shù)變化的變化趨勢。求解結果顯示,本文構建的魯棒優(yōu)化模型具有很好的魯棒性能。為了驗證罰函數(shù)的重要性,文中還給出了不加罰函數(shù)的模型求解結果圖形,并和加了罰函數(shù)的模型求解結果進行了對比。對比結果顯示,加了罰函數(shù)的魯棒優(yōu)化模型魯棒性能更好。最后本文給出了在目標函數(shù)1和目標函數(shù)2之間權衡的曲線圖,決策者可以根據(jù)企業(yè)的實際情況,決定最小化損失和最大化市場滿足率之間的權重。 本文構建了綜合生產(chǎn)計劃的多目標魯棒優(yōu)化模型,通過算例求解,不僅驗證了模型具有可行性和有效性,還給出了目標函數(shù)之間的權衡曲線圖,對企業(yè)具有一定的參考價值。
[Abstract]:In today's market economy, the life cycle of products is more and more short, the market competition is extremely fierce, the customer demand is changeable, and the supply chain network structure, etc. These factors lead to a lot of uncertainties in the supply chain. The uncertainty in the supply chain will bring some risks to the supply chain. Therefore, it is necessary to use uncertainty optimization method to reduce the risk of the supply chain caused by uncertainty, and to minimize the uncertainty factors. So as to improve the competitiveness of enterprises in the supply chain. In production management, integrated production planning is an important technical level planning; in the field of planning, It is between the long-term planning with broad decision and the short-term planning with detailed decision. Therefore, it is of great significance to study the integrated production planning of supply chain. This paper studies such a supply chain integrated production plan: the supply chain has a supplier, multiple manufacturing areas and multiple customer areas, in which all the raw materials required by the manufacturer are supplied by this supplier. And the products needed for each customer area can be supplied by any one or more manufacturing areas. During this period, each manufacturing area can regulate the labor force by hiring and firing, Firstly, this paper defines the concept of supply chain integrated production plan, and then summarizes the uncertainty in supply chain integrated production plan according to different classification methods. In the whole uncertain environment, this paper chooses the uncertainty of demand and cost to study, and uses scenario analysis to describe the uncertainty of cost and demand. The robust optimization method is selected to model the integrated production planning of the supply chain, which is to minimize the loss and maximize the market demand satisfaction rate, respectively, and construct the appropriate penalty function. The robust optimization model not only contains the loss minimization objective. The goal of maximization of market satisfaction rate is also introduced. In order to verify the validity and feasibility of the model, a numerical example is designed. At the end of this paper, the solution results based on LINGO software are analyzed, not only the change trend of objective function with risk coefficient, but also the change trend of objective function with risk coefficient are analyzed. The change trend of objective function with weight coefficient is also analyzed. The results show that the robust optimization model constructed in this paper has good robustness. In order to verify the importance of penalty function, The result graph of the model without penalty function is given, and the result is compared with that of the model with penalty function. The robust optimization model with penalty function is more robust. Finally, the graph of tradeoff between objective function 1 and objective function 2 is given. Determine the weight between minimizing losses and maximizing market satisfaction. In this paper, a multi-objective robust optimization model of integrated production planning is constructed. The model is not only proved to be feasible and effective, but also the tradeoff curve between objective functions is given, which has some reference value for enterprises.
【學位授予單位】：南京理工大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP13;F274

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