【摘要】：第三方逆向物流服務(wù)是隨著逆向物流業(yè)的快速發(fā)展而興起的新的服務(wù)模式,由于發(fā)展時(shí)間較短,大多數(shù)的第三方逆向物流服務(wù)企業(yè)雖然具有較先進(jìn)的設(shè)備和技術(shù)人員,但對(duì)逆向物流活動(dòng)的管理決策能力依舊不足,使企業(yè)在實(shí)施逆向物流活動(dòng)時(shí)遇到了諸多問(wèn)題。其中表現(xiàn)較突出的便是企業(yè)逆向物流需求的不確定,即產(chǎn)品回收數(shù)量的不確定,給逆向物流后續(xù)環(huán)節(jié)包括檢測(cè)、拆卸、維修、采購(gòu)、庫(kù)存和再利用的實(shí)施帶來(lái)了很大的影響。而這種影響在電子產(chǎn)品回收的逆向物流中表現(xiàn)更甚,因?yàn)槠涑司哂衅胀嫦蛭锪鞯奶卣魍?還具有產(chǎn)品生命周期短,產(chǎn)品種類(lèi)繁多的特點(diǎn)。 本文以第三方逆向物流電子產(chǎn)品的維修退貨回收為切入點(diǎn),采用了基于數(shù)據(jù)驅(qū)動(dòng)的預(yù)測(cè)方法研究逆向物流的回收預(yù)測(cè)。針對(duì)GM(1,1)模型預(yù)測(cè)的不足,考慮到逆向物流的不確定性中模糊性特點(diǎn),將模糊理論中的FTS模型引入對(duì)逆向物流的預(yù)測(cè),并根據(jù)GM(1,1)模型和FTS模型各自的特點(diǎn),構(gòu)建了電子產(chǎn)品回收預(yù)測(cè)的兩階段組合預(yù)測(cè)模型。在構(gòu)建組合模型過(guò)程中對(duì)于GM(1,1)模型隨期數(shù)增長(zhǎng)預(yù)測(cè)效果下降更快的現(xiàn)象,在組合預(yù)測(cè)時(shí)賦予其一個(gè)遞減的權(quán)重,使組合預(yù)測(cè)得到了更好的效果。本文的研究成果主要包括以下幾個(gè)方面：(1)逆向物流的不確定性主要包括隨機(jī)性和模糊性,其中隨機(jī)性主要產(chǎn)生于產(chǎn)品的回收階段,模糊性主要產(chǎn)生于對(duì)回收產(chǎn)品的統(tǒng)計(jì)和分類(lèi)階段,隨機(jī)性會(huì)一定程度上導(dǎo)致模糊性的產(chǎn)生；(2)GM(1,1)模型在處理逆向物流不確定性問(wèn)題上具有很好的效果,但是其僅局限于對(duì)短期趨勢(shì)的把握,尤其是對(duì)最近一至兩期的預(yù)測(cè)效果較好,更長(zhǎng)期的預(yù)測(cè)則難以達(dá)到較滿意的結(jié)果；(3)FTS模型通過(guò)對(duì)原始數(shù)據(jù)序列擾動(dòng)的模糊化,能夠較好的處理逆向物流的不確定性,在逆向物流的預(yù)測(cè)中具有一定適用性；(4)FTS_GM(1,1)組合模型充分利用了每個(gè)模型的特點(diǎn),在預(yù)測(cè)中能達(dá)到較之單個(gè)模型更好的效果,同時(shí)降低決策中因模型選取不當(dāng)帶來(lái)的決策失誤風(fēng)險(xiǎn)。
[Abstract]:Third-party reverse logistics service is a new service model rising with the rapid development of reverse logistics industry. Because of the short development time, most third-party reverse logistics service enterprises have more advanced equipment and technical personnel. However, the management decision ability of reverse logistics activities is still insufficient, which makes enterprises encounter many problems in implementing reverse logistics activities. The uncertainty of enterprise reverse logistics demand, that is, the uncertainty of the quantity of product recovery, brings great influence to the implementation of reverse logistics, including detection, disassembly, maintenance, procurement, inventory and reuse. This effect is more serious in the reverse logistics of electronic product recovery, because it has the characteristics of general reverse logistics, short product life cycle and various kinds of products. In this paper, the data driven forecasting method is used to study the recovery and prediction of reverse logistics, which is based on the maintenance and return of the third party reverse logistics electronic products as a starting point. Considering the fuzziness of uncertainty in reverse logistics, the FTS model of fuzzy theory is introduced to predict reverse logistics, and according to the characteristics of GM (1K1) model and FTS model. A two-stage combined forecasting model of electronic product recovery prediction is constructed. In the process of constructing the combination model, the GM (1 + 1) model decreases more quickly with the increase of the number of periods, and it is given a decreasing weight in the combination forecast, which makes the combination forecast get better effect. The research results of this paper mainly include the following aspects: (1) the uncertainty of reverse logistics mainly includes randomness and fuzziness, in which randomness mainly comes from the stage of product recovery. Fuzziness mainly comes from the stage of statistics and classification of recycled products, and randomness will lead to fuzziness to some extent. (2) GM (1 / 1) model has a good effect in dealing with the uncertainty of reverse logistics. However, it is limited to grasp the short term trend, especially for the most recent one or two periods, but the longer term prediction is difficult to achieve satisfactory results. (3) the FTS model is fuzzled by the disturbance of the original data series. It can deal with the uncertainty of reverse logistics well, and has certain applicability in forecasting reverse logistics. (4) FTS GM (1 / 1) composite model makes full use of the characteristics of each model, and can achieve better effect than single model in forecasting. At the same time, the risk of decision error caused by improper model selection in decision making is reduced.
【學(xué)位授予單位】：廈門(mén)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：F253;F713.2

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