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外文資料翻譯--逆向物流運(yùn)作渠道的決策方法-展示頁(yè)

2025-05-27 04:31本頁(yè)面
  

【正文】 alytic Hierarchy Process AHP method is developed by Prof. Thomas L. Saty. AHP divides a plex problem into a hierarchy of interrelated decision elements. AHP can deal with objective as well as nontangible subjective attributes. The procedure of AHP is as follows . Model the problem as a hierarchy Develop a hierarchical structure with a goal at the top level, the criteria at the second level and alternatives at the third level. Alternatives are affected by uncertain events and are connected to all criteria. Construct a pairwise parison matrix A set of parison matrix with respect to an element of immediately higher level is constructed. The pairwise parisons capture a decision makers perception of which element dominates the other. Test the Consistency by calculating the Eigen Vectors The relative normalized weight of each attribute is determined by calculating the geometric mean of the row and then normalizing the geometric means of rows in parison matrix. A consistency ratio of or less is considered as acceptable for matrices M a consistency ratio is more than the acceptable value, inconsistency occurs, and the judgments are untrustworthy, the evaluation process needs to be improved. Consistency ratio helps to ensure decision maker reliability in determining the priorities for the criteria. TOPSIS Method Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was first established by Hwang amp。 Manufacturer Operation, Third Party Operation, Joint Operation. In this paper a hybrid methodology based on Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) under fuzzy environment is proposed for the selection and evaluation of reverse logistics operating channels. An example is included to validate the proposed method. This method helps the decision maker to select the best technology that meets the requirement. Keywords: Reverse Logistics。未來(lái)的研究包含了結(jié)合層次分析法和模糊折中排序法( VIKOR)的一種兩階段方法,并進(jìn)行敏感度的分析以確定穩(wěn)定性?;趯哟畏治龇ê湍:h(huán)境下逼近理想解排序法的混合方法被提出來(lái)解決逆向物流運(yùn)作渠道的選擇。 然而越來(lái)越多的環(huán)境上的問(wèn)題,迫使企業(yè)去選擇逆向物流。一種綜合了層次分析法和模糊環(huán)境下逼近理想解排序法的混合方法將被企業(yè)用來(lái)進(jìn)行選擇。因此選擇正確的運(yùn)營(yíng)渠道受到了企業(yè)的高度重視。該企業(yè)想擁有一套系統(tǒng)性的實(shí)施逆向物流的方法。 4 模型應(yīng)用 所提出的模型被應(yīng)用于工業(yè)上的一個(gè)問(wèn)題。語(yǔ)言變量在處理過(guò)多層面或者在沒(méi)有被很好的定義情況下,在典型的數(shù)量方面,是非常有用的。模糊集合論允許決策者將無(wú)法量化的信息,不完整的信息和不可獲得的信息和部分被忽視的信息加入決策模型中。然而主要的缺點(diǎn)是關(guān)于代表決策者意見(jiàn)清晰值得不確定性和不準(zhǔn)確性。最好的選擇將是最接近積極的理想解決方案(該方案最大化了收益標(biāo)準(zhǔn),最小化了成本標(biāo)準(zhǔn))以及遠(yuǎn) 離了消極的理想解決方案。 模糊環(huán)境下逼近理想解排序法 模糊環(huán)境下逼近理想解排序法是由 Hwang amp。 如果一致性比率等于 或者低于 ,則被視為可接受的矩陣 M過(guò)可接受值,不一致就出現(xiàn),判斷不可信,評(píng)估過(guò)程需要進(jìn)一步提高。在兩兩對(duì)比下決策者作出起到支配元素的認(rèn)知。層次分析法的步驟如下: 問(wèn)題的模型層次化 在最高水平目標(biāo)的前提下制定一個(gè)分層次的結(jié)構(gòu),第二層級(jí)是標(biāo)準(zhǔn),第三層級(jí)是可選擇的方案,可選擇方案受到不確定活動(dòng)的影響,而且與所有的標(biāo)準(zhǔn)相關(guān)聯(lián)。 層次分析法 層次分析法是由 Thomas L. Saaty 教授首先提出的,層次分析法把一個(gè)復(fù)雜的問(wèn)題分解成相關(guān)聯(lián)的決策元素的層次結(jié)構(gòu)。通過(guò)將它的混合標(biāo)準(zhǔn)與其他許多決策支持工具和方法相結(jié)合能更好地應(yīng)用于實(shí)例中。模糊環(huán)境下逼近理想解排序法將用于得到最后各種方案的排名。在本章節(jié)中,基于層次分析法( AHP)和技術(shù)模糊環(huán)境下逼近理想解排序法( TOPSIS)相結(jié)合的混合方法將會(huì)呈現(xiàn)。有著數(shù)量限制的可選擇方案條件下,多準(zhǔn)則決策( MCDM)是 離散的。逆向物流可以通過(guò)制造商自營(yíng)( MO)、聯(lián)合運(yùn)營(yíng)( JO)和第三方運(yùn)營(yíng)( TPO)三種模式進(jìn)行。汽車(chē)企業(yè)回收?qǐng)?bào)廢汽車(chē)的零部件。第二部分將提出問(wèn)題,第三部分將給出解決的方法概述,第四部分將給出一則案例作為論證,第五部分進(jìn)行總結(jié),得到研究成果。 評(píng)估和選擇逆向物流渠道被認(rèn)為是多準(zhǔn)則決策( MCDM)的重要過(guò)程,這個(gè)過(guò)程要求決策者在所存在的可選擇對(duì)象中作出最佳的選擇。物流的成本將會(huì)降低,同時(shí)訂單的滿意率將會(huì)大大提高。通過(guò)外包逆向物流活動(dòng),企業(yè)可以更加專(zhuān)注于自己核心業(yè)務(wù)的運(yùn)營(yíng),而且顧客的滿意度和評(píng)估績(jī)效就會(huì)大大提高。可以通過(guò)第三方來(lái) 減少再制
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