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    Title: 廢棄電子資訊物品逆物流回收之需求分析與預測研究 : 以臺灣地區為例
    Other Titles: Demand analysis and forecasting for reverse logistics and recycling of end-of-life electrical and electronic equipment : a case study of Taiwan area
    Authors: 郭育孟;Kuo, Yu-meng
    Contributors: 淡江大學運輸管理學系碩士班
    溫裕弘;Wen, Yuh-horng
    Keywords: 廢棄電子資訊物品逆物流回收;逆物流回收需求分析;逆物流回收貨運量預測;類神經網路模式;Reverse Logistics and Recycling of End-of-Life Electrical and Electronic Equipment;Demand Analysis for Reverse Logistics and Recycling;Forecasting for Reverse Logistics and Recycling;Artificial Neural Network
    Date: 2009
    Issue Date: 2010-01-11 04:34:41 (UTC+8)
    Abstract: 環境的汙染促使永續環保議題逐漸受到重視,為因應相關環保法規的制定、綠色供應鏈風潮與企業永續發展,逆物流回收議題漸成為當務之急的研究方向。如何有效的了解最終消費者對廢資訊物品逆物流回收之服務需求以及對逆物流回收貨運量之預測與掌握,則為重要課題之ㄧ。透過逆物流回收需求分析,可作為決策者提高產品回收率之參考依據,而針對逆物流回收量進行預測,則可提供相關第三方逆物流回收運送業者之運輸規劃基礎。然而,過去研究有關逆物流回收之需求分析與逆物流回收貨運量預測之文獻闕如,故發展一套整合逆物流回收需求分析與逆物流回收貨運量預測模式,對於學術上與實務上均具有研究之價值。
    本研究第一部份進行廢資訊物品逆物流回收需求調查與分析,並利用探索性因素分析,萃取出生命終期廢棄資訊物品從產生逆物流到實際進行逆物流回收活動過程之關鍵因素。並藉由廢資訊物品逆物流回收考量因素之因素分數與回收處理方式,應用二元羅吉斯迴歸模式建構回收機率函數。此外,假設資訊物品使用年限呈常態分配,再利用資訊物品出貨量、使用年限機率與回收機率函數進行未來潛在回收貨運量之推估。但此推估之潛在回收貨運量與實際回收貨運量仍有差距,且實際回收貨運量具有不規則、不確定等特性,因此,本研究第二部份應用類神經網路之適應性學習功能,建構逆物流回收貨運量預測模式,以修正潛在回收貨運量與實際回收貨運量之誤差與降低其不確定性。最後,進行實證範例分析,結果顯示本研究所建構之逆物流回收貨運量預測模式之預測能力均較機率推估型預測模式之使用年限法、時間數列型預測模式之ARIMA與GM(1,1) 、整合機率推估型與時間數列型預測模式之二元迴歸及GM(1,N)模式佳,驗證本研究模式可行且具有較佳之預測能力與解釋能力。
    本研究首次嘗試將逆物流回收需求分析整合於逆物流回收貨運量預測模式中,研究成果不僅在學術上可作為逆物流回收需求分析與貨運量預測模式相關研究之參考,所發展之模式亦可提供實務上進行逆物流回收預測模組開發之模式基礎。
    With the global eco-awareness, the European Union has claimed several regulations, such as the Directive on Waste Electrical and Electronic Equipment (WEEE) to regulate recycling items for end-of-life(EOL) electrical and electronic equipment. Under the trends in the responsibility of end-of-life product recycling, the reverse logistics management has become a topic of great interest for many academicians and planners, and an essential element of company strategy for others. However, the management of waste EOL reverse logistics is even more complex than the traditional logistics, due to the uncertainty surrounding the process of reverse logistics and EOL recycling. Demand analysis and forecasting for waste recycling is a critical foundation in the reverse logistics management of EOL electrical and electronic equipment, that is prerequisite to recycling management for regulators and administrators, and reverse logistics network design and transportation planning for reverse logistics service providers. However, few literatures have been paid to the demand analysis and forecasting models for reverse logistics and recycling of waste EOLs. This study attempts to develop a series of models to analyze the demand factors, and to predict the return quantity for waste EOL electrical and electronic equipment.
    The first part of this study conducted a demand survey and analysis of reverse logistics and recycling on EOL electrical and electronic equipment. This study applies exploratory factor analysis to identify key demand for EOL electrical and electronic equipment recycling. This study proposes a binary logistic regression model to determine the return probability. In the second part of the study, this study combines probability estimation and time-series forecasting model to propose a hybrid forecasting model for return quantity forecasting. Considering useful life of electrical and electronic equipment, the production shipment volume, and the return probabilities, the potential return quantity is estimated. Furthermore, a neural network model is developed to improve the forecasting accuracy and eliminate the uncertainty and randomness surrounding the input data. Finally, a case study with a reverse logistics and recycling of EOL electrical and electronic equipment data was provided to illustrate the results and the application of the model’s is shown to be more accurate prediction results than useful life, ARIMA, GM(1,1), binary regression and GM(1,N) models. The results verified that the proposed model is practicable, and provide a better prediction and explanation ability.
    Appears in Collections:[Graduate Institute & Department of Transportation Management] Thesis

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