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

【摘要】：近年來(lái),隨著信息經(jīng)濟(jì)的繁榮發(fā)展,客戶需求日漸全球化,供應(yīng)鏈中各企業(yè)已無(wú)法再單純依靠自身能力來(lái)應(yīng)對(duì)經(jīng)濟(jì)全球化的挑戰(zhàn)了。市場(chǎng)不確定性的日益增加要求供應(yīng)鏈中各企業(yè)必須實(shí)現(xiàn)信息共享,共同建立完善的協(xié)同機(jī)制,以增強(qiáng)供應(yīng)鏈的核心競(jìng)爭(zhēng)力,才能在激烈的市場(chǎng)競(jìng)爭(zhēng)中占據(jù)優(yōu)勢(shì)地位。供應(yīng)鏈協(xié)同管理涉及面眾多,而庫(kù)存協(xié)同是供應(yīng)鏈協(xié)同管理的基礎(chǔ),合理有效的庫(kù)存協(xié)同機(jī)制能幫助企業(yè)在有效滿足用戶需求的基礎(chǔ)上,減少庫(kù)存成本,最大化供應(yīng)鏈?zhǔn)找妗?庫(kù)存決策受用戶市場(chǎng)的需求驅(qū)動(dòng),需求的不確定性使得庫(kù)存協(xié)同機(jī)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(lái)簡(jiǎn)化需求的影響,使得研究成果較為理想化,缺乏普遍性和實(shí)際應(yīng)用性。因此,為解決需求的不確定性問(wèn)題,本論文將采用蒙特卡洛仿真技術(shù)來(lái)對(duì)市場(chǎng)需求的不確定進(jìn)行統(tǒng)計(jì)建模,構(gòu)建通用的庫(kù)存協(xié)同決策模型。然而,蒙特卡洛仿真技術(shù)是基于概率統(tǒng)計(jì)的方法,在執(zhí)行過(guò)程中需要付出巨大的計(jì)算代價(jià),因此,它在帶來(lái)模型通用性的同時(shí)也帶來(lái)了資源消耗過(guò)大的新問(wèn)題。為此,本文將著重探索如何在保證蒙特卡洛仿真模擬結(jié)果準(zhǔn)確度的基礎(chǔ)上盡力減少計(jì)算資源的消耗,緩解結(jié)果準(zhǔn)確度和計(jì)算消耗量之間的矛盾。 在不確定需求的供應(yīng)鏈庫(kù)存協(xié)同策略優(yōu)化問(wèn)題中,本論文將首先采用蒙特卡洛仿真模擬技術(shù)建立了庫(kù)存協(xié)同策略優(yōu)化的基本模型,其次采用粒子群算法進(jìn)行庫(kù)存協(xié)同最優(yōu)策略的搜索,在粒子群算法的適應(yīng)度評(píng)價(jià)過(guò)程中同時(shí)引入了適應(yīng)度遺傳概念和自適應(yīng)采樣技術(shù),分別從粒子進(jìn)行適應(yīng)度評(píng)價(jià)的次數(shù)和粒子進(jìn)行適應(yīng)度評(píng)價(jià)過(guò)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(jì)算的資源消耗,從而達(dá)到了結(jié)果準(zhǔn)確度和資源消耗程度之間的平衡。此外,本文還將對(duì)粒子群算法的適應(yīng)度遺傳技術(shù)及其供應(yīng)鏈協(xié)同策略的自適應(yīng)采樣算法進(jìn)行多方位探討,比較多種影響因子的作用,有效提高供應(yīng)鏈庫(kù)存協(xié)同的效率,達(dá)到?jīng)Q策通用性,準(zhǔn)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(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：F274;TP18

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本文編號(hào)：1804175