基于數(shù)據(jù)驅動的第三方逆向物流電子產品回收預測研究
發(fā)布時間：2018-07-20 19:06
【摘要】：第三方逆向物流服務是隨著逆向物流業(yè)的快速發(fā)展而興起的新的服務模式,由于發(fā)展時間較短,大多數(shù)的第三方逆向物流服務企業(yè)雖然具有較先進的設備和技術人員,但對逆向物流活動的管理決策能力依舊不足,使企業(yè)在實施逆向物流活動時遇到了諸多問題。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、拆卸、維修、采購、庫存和再利用的實施帶來了很大的影響。而這種影響在電子產品回收的逆向物流中表現(xiàn)更甚,因為其除了具有普通逆向物流的特征外,還具有產品生命周期短,產品種類繁多的特點。 本文以第三方逆向物流電子產品的維修退貨回收為切入點,采用了基于數(shù)據(jù)驅動的預測方法研究逆向物流的回收預測。針對GM(1,1)模型預測的不足,考慮到逆向物流的不確定性中模糊性特點,將模糊理論中的FTS模型引入對逆向物流的預測,并根據(jù)GM(1,1)模型和FTS模型各自的特點,構建了電子產品回收預測的兩階段組合預測模型。在構建組合模型過程中對于GM(1,1)模型隨期數(shù)增長預測效果下降更快的現(xiàn)象,在組合預測時賦予其一個遞減的權重,使組合預測得到了更好的效果。本文的研究成果主要包括以下幾個方面：(1)逆向物流的不確定性主要包括隨機性和模糊性,其中隨機性主要產生于產品的回收階段,模糊性主要產生于對回收產品的統(tǒng)計和分類階段,隨機性會一定程度上導致模糊性的產生；(2)GM(1,1)模型在處理逆向物流不確定性問題上具有很好的效果,但是其僅局限于對短期趨勢的把握,尤其是對最近一至兩期的預測效果較好,更長期的預測則難以達到較滿意的結果；(3)FTS模型通過對原始數(shù)據(jù)序列擾動的模糊化,能夠較好的處理逆向物流的不確定性,在逆向物流的預測中具有一定適用性；(4)FTS_GM(1,1)組合模型充分利用了每個模型的特點,在預測中能達到較之單個模型更好的效果,同時降低決策中因模型選取不當帶來的決策失誤風險。
[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.
【學位授予單位】：廈門大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F253;F713.2
本文編號：2134475
[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.
【學位授予單位】：廈門大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F253;F713.2
【參考文獻】
相關期刊論文 前10條
1 陳義華;許慶春;;基于Markov過程的逆向物流回收量預測[J];重慶理工大學學報(自然科學);2012年01期
2 唐小我,王景,曹長修;一種新的模糊自適應變權重組合預測算法[J];電子科技大學學報;1997年03期
3 羅黨,劉思峰,黨耀國;灰色模型GM(1,1)優(yōu)化[J];中國工程科學;2003年08期
4 趙鵬;雷濤;;基于灰色系統(tǒng)理論的汽車報廢量預測[J];工業(yè)技術經(jīng)濟;2011年04期
5 謝家平;趙忠;;基于GERT隨機網(wǎng)絡的廢棄回收預測模型研究[J];管理學報;2010年02期
6 洪鴻加;彭曉春;陳志良;王俊能;胡小英;;系統(tǒng)動力學模型在電子廢棄物產生量預測中的應用——以廣州市廢舊電腦為例[J];環(huán)境污染與防治;2009年10期
7 黃映輝;李冠宇;;不精確性:涵義與性質[J];計算機科學;2010年04期
8 王曉原,李軍;灰色GM(1,1)模型在區(qū)域物流規(guī)模預測中的應用[J];武漢理工大學學報(交通科學與工程版);2005年03期
9 謝家平;葛夫財;;基于Markov鏈的逆向物流回流預測[J];科技進步與對策;2007年10期
10 劉文杰;方志耕;;IGM(1,1)-TFN模型在產品回收預測的應用[J];控制理論與應用;2007年03期
相關博士學位論文 前1條
1 范文姬;不確定環(huán)境下的再制造物流系統(tǒng)庫存控制與協(xié)調研究[D];北京交通大學;2010年
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