本文選題：供應鏈庫存協(xié)同 + 不確定需求��； 參考：《北京郵電大學》2014年碩士論文

【摘要】：近年來,隨著信息經(jīng)濟的繁榮發(fā)展,客戶需求日漸全球化,供應鏈中各企業(yè)已無法再單純依靠自身能力來應對經(jīng)濟全球化的挑戰(zhàn)了。市場不確定性的日益增加要求供應鏈中各企業(yè)必須實現(xiàn)信息共享,共同建立完善的協(xié)同機制,以增強供應鏈的核心競爭力,才能在激烈的市場競爭中占據(jù)優(yōu)勢地位。供應鏈協(xié)同管理涉及面眾多,而庫存協(xié)同是供應鏈協(xié)同管理的基礎,合理有效的庫存協(xié)同機制能幫助企業(yè)在有效滿足用戶需求的基礎上,減少庫存成本,最大化供應鏈收益。 庫存決策受用戶市場的需求驅動,需求的不確定性使得庫存協(xié)同機制的研究極為困難,已有研究大都采用平均需求或者假定需求服從某一特定的概率分布類型的方法來簡化需求的影響,使得研究成果較為理想化,缺乏普遍性和實際應用性。因此,為解決需求的不確定性問題,本論文將采用蒙特卡洛仿真技術來對市場需求的不確定進行統(tǒng)計建模,構建通用的庫存協(xié)同決策模型。然而,蒙特卡洛仿真技術是基于概率統(tǒng)計的方法,在執(zhí)行過程中需要付出巨大的計算代價,因此,它在帶來模型通用性的同時也帶來了資源消耗過大的新問題。為此,本文將著重探索如何在保證蒙特卡洛仿真模擬結果準確度的基礎上盡力減少計算資源的消耗,緩解結果準確度和計算消耗量之間的矛盾。 在不確定需求的供應鏈庫存協(xié)同策略優(yōu)化問題中,本論文將首先采用蒙特卡洛仿真模擬技術建立了庫存協(xié)同策略優(yōu)化的基本模型,其次采用粒子群算法進行庫存協(xié)同最優(yōu)策略的搜索,在粒子群算法的適應度評價過程中同時引入了適應度遺傳概念和自適應采樣技術,分別從粒子進行適應度評價的次數(shù)和粒子進行適應度評價過程中的采樣次數(shù)兩方面入手,大力減少了蒙特卡洛模擬計算的資源消耗,從而達到了結果準確度和資源消耗程度之間的平衡。此外,本文還將對粒子群算法的適應度遺傳技術及其供應鏈協(xié)同策略的自適應采樣算法進行多方位探討,比較多種影響因子的作用,有效提高供應鏈庫存協(xié)同的效率,達到?jīng)Q策通用性,準確性和高效性的統(tǒng)一。
[Abstract]:In recent years, with the prosperous development of information economy and the increasing globalization of customer demand, the enterprises in supply chain can no longer rely solely on their own ability to meet the challenge of economic globalization. With the increasing uncertainty of the market, the enterprises in the supply chain must share information and establish a perfect coordination mechanism to enhance the core competitiveness of the supply chain, so as to occupy the dominant position in the fierce market competition. Supply chain collaborative management involves many aspects, and inventory coordination is the basis of supply chain collaborative management. Reasonable and effective inventory coordination mechanism can help enterprises to reduce inventory costs and maximize supply chain benefits on the basis of effectively meeting the needs of users. Inventory decision is driven by the demand of the user market, and the uncertainty of the demand makes the research of inventory coordination mechanism very difficult. Most of the previous studies have used the method of average demand or assuming demand service from a particular probability distribution type to simplify the influence of demand, which makes the research results more idealized, lacking of universality and practical application. Therefore, in order to solve the uncertainty of demand, this paper uses Monte Carlo simulation technology to model the uncertainty of market demand and construct a general inventory collaborative decision-making model. However, Monte Carlo simulation technology is based on probability and statistics, and it has to pay a huge computational cost in the process of execution. Therefore, it brings not only the generality of the model, but also the new problem of excessive resource consumption. Therefore, this paper will focus on how to reduce the consumption of computing resources on the basis of ensuring the accuracy of Monte Carlo simulation results, and to alleviate the contradiction between the accuracy of the results and the computational consumption. In the inventory coordination strategy optimization problem of supply chain with uncertain demand, this paper first uses Monte Carlo simulation technology to establish the basic model of inventory coordination policy optimization. Secondly, Particle Swarm Optimization (PSO) algorithm is used to search inventory cooperative optimal strategy, and the fitness genetic concept and adaptive sampling technique are introduced in the process of PSO fitness evaluation. Starting from two aspects: the number of particle fitness evaluation and the sampling number in the process of particle fitness evaluation, the resource consumption of Monte Carlo simulation calculation is greatly reduced. Thus, the balance between the accuracy of the results and the degree of resource consumption is achieved. In addition, in this paper, the fitness genetic technology of PSO and the adaptive sampling algorithm of supply chain coordination strategy are discussed in order to improve the efficiency of inventory coordination in supply chain. Achieve the unity of decision generality, accuracy and efficiency.
【學位授予單位】：北京郵電大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F274;TP18

【參考文獻】

相關期刊論文 前4條

1 程錫禮,張延林,崔新生;蒙特卡洛仿真在工程項目進度管理中的應用[J];工業(yè)工程;2004年03期

2 涂歆;;自適應采樣免疫算法及其對隨機優(yōu)化問題的應用[J];貴州大學學報(自然科學版);2009年05期

3 要趁紅;王民;宋文博;;一種自適應采樣方法在網(wǎng)絡管理中的研究[J];信息通信;2012年03期

4 林君曉;姜鵬飛;謝玉萍;;蒙特卡洛模擬技術在污水處理項目風險分析中的應用[J];科技管理研究;2006年12期

，

本文編號：1804175