本文選題：自適應(yīng)庫(kù)存管理 + 指數(shù)平滑法　； 參考：《青島大學(xué)》2014年碩士論文

【摘要】：庫(kù)存在生產(chǎn)系統(tǒng)乃至供應(yīng)鏈中扮演者重要角色。高效的庫(kù)存管理可以在降低成本的前提下保證生產(chǎn)銷售的正常運(yùn)行,為產(chǎn)品的流通提供可靠保障；反之,庫(kù)存管理不當(dāng)將會(huì)占用大量流動(dòng)資金并且產(chǎn)生高額的庫(kù)存管理費(fèi)用。傳統(tǒng)的EOQ模型及其擴(kuò)展模型有效解決了需求確定或者需求分布信息已知時(shí)的庫(kù)存管理問題。隨著社會(huì)的發(fā)展,顧客消費(fèi)水平的提高,產(chǎn)品生命周期不斷縮短,更新?lián)Q代速度加快,許多產(chǎn)品的需求呈現(xiàn)非平穩(wěn)特征,而且需求分布信息難以獲取,在這種背景下,如何科學(xué)有效地管理庫(kù)存問題成為庫(kù)存領(lǐng)域研究的焦點(diǎn)之一。自適應(yīng)庫(kù)存控制方法是解決這類問題的有效方法。制定有效的自適應(yīng)庫(kù)存控制策略具有重要意義。 所謂自適應(yīng)庫(kù)存控制,就是將自適應(yīng)控制理論與傳統(tǒng)庫(kù)存控制相結(jié)合。具體方法為：在面對(duì)多周期庫(kù)存控制問題時(shí)且客戶需求信息不確定的情況下,每到庫(kù)存盤點(diǎn)時(shí)刻都會(huì)檢測(cè)庫(kù)存控制的效果,并根據(jù)庫(kù)存效果與庫(kù)存控制目標(biāo)(比如：顧客服務(wù)水平、庫(kù)存成本等)的差距自動(dòng)調(diào)整參數(shù),以確保庫(kù)存控制能及時(shí)作出調(diào)整,進(jìn)而使得需求出現(xiàn)變化時(shí)可以減小不確定性對(duì)庫(kù)存的影響,進(jìn)而達(dá)到預(yù)設(shè)的庫(kù)存控制目標(biāo)。 未來顧客需求的預(yù)測(cè)在庫(kù)存控制研究中往往起著至關(guān)重要的作用,學(xué)者通常運(yùn)用指數(shù)平滑法作為對(duì)未來需求預(yù)測(cè)的模型。然而,指數(shù)平滑法中系數(shù)在選取時(shí)往往采取的利用人工經(jīng)驗(yàn)或是反復(fù)測(cè)試來完成,并沒有一種可靠而明確的方法,使得預(yù)測(cè)值與實(shí)際值偏差較大,造成指數(shù)平滑法的預(yù)測(cè)精度偏低,阻礙了指數(shù)平滑法的廣泛應(yīng)用。而將自適應(yīng)控制思想與傳統(tǒng)指數(shù)平滑法結(jié)合產(chǎn)生的自適應(yīng)指數(shù)平滑算法能夠在需求波動(dòng)時(shí)較傳統(tǒng)指數(shù)預(yù)測(cè)法更準(zhǔn)確地預(yù)測(cè)顧客需求,從而提高了預(yù)測(cè)精度,提高了庫(kù)存控制的效果。 本文目標(biāo)是探討非平穩(wěn)隨機(jī)需求環(huán)境多周期隨機(jī)庫(kù)存自適應(yīng)庫(kù)存控制方法,主要內(nèi)容包括：在探討自適應(yīng)庫(kù)存控制方法的同時(shí),結(jié)合自適應(yīng)指數(shù)預(yù)測(cè)算法并以此建立一個(gè)滿足預(yù)設(shè)服務(wù)水平的庫(kù)存模型。前三章主要介紹自適應(yīng)庫(kù)存控制基本內(nèi)容以及庫(kù)存控制的基本理論,同時(shí)介紹指數(shù)平滑法及其改進(jìn)算法；四、五兩章建立了自適應(yīng)庫(kù)存控制模型。第六章用計(jì)算機(jī)對(duì)顧客需求進(jìn)行仿真,并通過動(dòng)態(tài)地調(diào)整平滑因子和安全因子,使庫(kù)存控制能夠滿足預(yù)設(shè)的客戶服務(wù)水平。仿真結(jié)果表明,本文提出的庫(kù)存控制模型在非平穩(wěn)需求環(huán)境下能有效穩(wěn)定在預(yù)設(shè)顧客服務(wù)水平。
[Abstract]:Inventory plays an important role in the production system and even in the supply chain. Efficient inventory management can ensure the normal operation of production and sales under the premise of reducing costs, and provide reliable guarantee for the circulation of products. Conversely, improper inventory management will occupy a large number of liquidity and lead to high inventory management costs. The traditional EOQ model and its extended model effectively solve the inventory management problem when the requirement is determined or the requirement distribution information is known. With the development of society, the improvement of customer consumption level, the product life cycle is shortened, the speed of renewal is accelerated, the demand of many products presents the non-stationary characteristic, and the information of demand distribution is difficult to obtain, under this background, How to manage inventory scientifically and effectively becomes one of the focuses in inventory field. Adaptive inventory control is an effective method to solve this kind of problem. It is of great significance to establish an effective adaptive inventory control strategy. Adaptive inventory control is a combination of adaptive control theory and traditional inventory control. The specific methods are as follows: in the face of multi-cycle inventory control problem and customer demand information is uncertain, every inventory count time will check the effectiveness of inventory control, And automatically adjust the parameters according to the gap between inventory effect and inventory control objectives (such as customer service level, inventory cost, etc.) to ensure that inventory control can be adjusted in time. When the demand changes, it can reduce the impact of uncertainty on inventory, and then achieve the pre-set inventory control goal. The prediction of future customer demand often plays an important role in the research of inventory control. Scholars usually use exponential smoothing method as the model of forecasting future demand. However, in the exponential smoothing method, the coefficients are often chosen by artificial experience or repeated testing, and there is no reliable and definite method to make the predicted value deviate greatly from the actual value. The prediction accuracy of exponential smoothing method is on the low side, which hinders the wide application of exponential smoothing method. The adaptive exponential smoothing algorithm combined with the traditional exponential smoothing method can predict the customer demand more accurately than the traditional exponential forecasting method when the demand fluctuates, thus improving the prediction accuracy. Improve the effect of inventory control. The purpose of this paper is to discuss the adaptive inventory control method of multi-period stochastic inventory in the non-stationary stochastic demand environment. The main contents include: while discussing the adaptive inventory control method, Combined with adaptive exponential prediction algorithm, an inventory model satisfying the preset service level is established. The first three chapters mainly introduce the basic contents of adaptive inventory control and the basic theory of inventory control. At the same time, the exponential smoothing method and its improved algorithm are introduced. In chapter 6, the customer demand is simulated by computer, and by adjusting the smoothing factor and security factor dynamically, the inventory control can meet the preset customer service level. The simulation results show that the inventory control model proposed in this paper can effectively stabilize the customer service level in the non-stationary demand environment.
【學(xué)位授予單位】：青島大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：O227

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本文編號(hào)：2085560