本文選題：層次分析 + BP神經(jīng)網(wǎng)絡(luò)　； 參考：《浙江工商大學(xué)》2014年碩士論文

【摘要】：對(duì)于服裝行業(yè)來講,企業(yè)運(yùn)營效率的提高主要依托于兩個(gè)要素,一個(gè)是服裝銷售預(yù)測的精度,另一個(gè)是供應(yīng)鏈的反應(yīng)速度,即供應(yīng)鏈的效率。如果預(yù)測的精度高,則即便供應(yīng)鏈的反應(yīng)速度不夠快,也能夠?qū)崿F(xiàn)庫存與資金的高周轉(zhuǎn),可見服裝銷售預(yù)測的準(zhǔn)確度對(duì)于公司上層做決策起到重要的作用。 服裝產(chǎn)業(yè)是一個(gè)復(fù)雜的、銷售渠道多樣的、快速變化的時(shí)尚產(chǎn)業(yè),因此短周期預(yù)測對(duì)服裝產(chǎn)業(yè)更為合適。針對(duì)這一特點(diǎn),本文除常規(guī)的季節(jié)、周期、節(jié)假日外,增加了競爭對(duì)手、流行走向等因子,全面提取了影響服裝銷售的影響因素,從而使銷售預(yù)測在更大層度上逼近真實(shí)的銷售量。 本文將層次分析法(Analytical Hierarchy Process,簡稱AHP)與BP (Back Propagation)人工神經(jīng)網(wǎng)絡(luò)相結(jié)合,提出了一套A-BP銷售預(yù)測模型,將定性預(yù)測算法與定量預(yù)測算法緊密的結(jié)合了起來。該模型的構(gòu)建過程是：首先提出了多個(gè)影響銷售量的因子,然后將這些影響因子通過層次分析建立三級(jí)層次結(jié)構(gòu),通過兩兩比較確定層次中因子的相對(duì)重要性,輸出各個(gè)因子對(duì)銷售量的權(quán)重,最后將權(quán)重值排在前幾位的影響因子納入BP神經(jīng)網(wǎng)絡(luò)中進(jìn)行計(jì)算,得出預(yù)測銷售量。 最后,本文實(shí)現(xiàn)了一個(gè)基于A-BP模型的銷售預(yù)測系統(tǒng),通過實(shí)驗(yàn)驗(yàn)證了A-BP銷售預(yù)測模型比BP神經(jīng)網(wǎng)絡(luò)模型預(yù)測準(zhǔn)確更高的結(jié)論。
[Abstract]:For the garment industry, the improvement of enterprise operation efficiency mainly depends on two factors, one is the precision of clothing sales prediction, the other is the response speed of supply chain, that is, the efficiency of supply chain. If the prediction accuracy is high, even if the supply chain reaction speed is not fast enough, it can also realize the high turnover of inventory and capital. It is clear that the accuracy of clothing sales prediction plays an important role in the upper layer of the company to make decisions. The garment industry is a complicated, diversified and rapidly changing fashion industry, so short period prediction is more suitable for garment industry. In view of this characteristic, in addition to the normal season, cycle, holiday, this article has added the competition, the popular trend and so on factor, has completely extracted the influence factor which affects the clothing sale, So that the sales forecast in a larger level approach to the real sales volume. In this paper, the Analytical Hierarchy process (Hierarchy) and BP back Propagation (BP) artificial neural network are combined, and a set of A-BP sales forecasting model is proposed, which combines the qualitative prediction algorithm and the quantitative prediction algorithm closely. The construction process of the model is as follows: firstly, several factors affecting sales volume are put forward, then the three-level hierarchical structure is established by hierarchical analysis, and the relative importance of the factors in the hierarchy is determined by comparison. The weight of each factor to the sales volume is outputted. Finally, the influence factors which rank the weight in the first place are incorporated into the BP neural network to calculate the predicted sales volume. Finally, a sales forecasting system based on A-BP model is implemented in this paper. The experimental results show that the A-BP model is more accurate than the BP neural network model.
【學(xué)位授予單位】：浙江工商大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP311.52;TP183

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