物流管理 外文文獻 外文翻譯 英文文獻 逆向物流運作渠道的決策方法

外文出處：Senthil, S., Srirangacharyulu, B., & Ramesh, A. (2012). A decision making methodology for the selection of reverse logistics operating channels.Procedia Engineering,38, 4, 418428.附件1：外文資料翻譯譯文逆向物流運作渠道的決策方法摘要：產(chǎn)品退貨的有效管理是一項戰(zhàn)略性的問題。如今，客戶希望廠商能夠發(fā)展逆向物流系統(tǒng)，為了是返還的產(chǎn)品能夠被回收。隨著逆向物流實踐的不斷發(fā)展和進步，逆向物流渠道的選擇就顯得越來越重要。現(xiàn)在有三種基本的逆向物流運作渠道：制造商自營，第三方運營和聯(lián)合運營模式。本文基于層次分析法（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，提出了逆向物流運作渠道的選擇和評價。本文利用一個算例驗證了該方法。這種方法幫助決策者更有效的選擇能夠滿足客戶要求的最佳渠道。關(guān)鍵字：逆向物流 多目標決策 層次分析法1 引言由于有關(guān)環(huán)境的法律不斷的出臺，逆向物流逐漸引起了企業(yè)的關(guān)注。逆向物流（RL）是一個規(guī)劃、實施和控制原材料能夠高效、低成本的流動的過程，也是為了達到獲得更多的價值，對于在制品庫存、產(chǎn)成品和相關(guān)的從消費者手中回到原生產(chǎn)商的信息進行適當?shù)奶幚?。對于逆向物流的研究仍然處于探索階段。逆向物流使得企業(yè)降低成本成為可能。逆向物流定義了供應(yīng)鏈被設(shè)計為有效的管理產(chǎn)品和零部件的流動，使得它們能夠進行再制造、循環(huán)利用和流程的改進，以便更加有效的利用這些資源。逆向物流活動的執(zhí)行包括各種功能部分：產(chǎn)品質(zhì)量的把關(guān)，壓縮配置循環(huán)周期，產(chǎn)品的再制造與翻新，資產(chǎn)回收，談判，外包和客戶服務(wù)。除了產(chǎn)品的存儲和運輸，增值服務(wù)的價值如：JIT，快速反應(yīng)和問題方案的解決也都是逆向物流的重要組成部分。對于有缺陷的產(chǎn)品進行再制造，維修和回收可以創(chuàng)造巨大利潤的商業(yè)機會。作為回收產(chǎn)品的管理企業(yè)可以重新使用它們，轉(zhuǎn)售或者銷毀它們。經(jīng)銷商可能會因為季節(jié)性的原因，產(chǎn)品質(zhì)量到期或者是運輸中的損壞而將產(chǎn)品返還給企業(yè)。消費者可能會因為產(chǎn)品的質(zhì)量問題退還產(chǎn)品。進行產(chǎn)品回收的管理提高了客戶服務(wù)水平，使得企業(yè)可以保持更多的客戶數(shù)量。從采購到銷售都將產(chǎn)生一些不必要的浪費，而減少這種浪費正是重視環(huán)保實踐的主要目標。制造商將逆向物流視為修復(fù)來自于客戶的有缺陷的產(chǎn)品或者是回收可以重復(fù)使用的包裝的過程。在電子商務(wù)中，因為購買者有退貨的保證，所以逆向物流就成為了一項重要的問題。由于逆向物流需要運用跨學(xué)科的方法，這方面仍有可以研究的機會。一種關(guān)于管理零售商逆向物流運作的概念框架在本文中被提出來。在案例中，由三家調(diào)查公司進行了調(diào)查并且確定了一個企業(yè)為了獲得競爭的優(yōu)勢從而采用的逆向物流流程和戰(zhàn)略的問題。通過使用遺傳算法和粒子群優(yōu)化環(huán)境，一個集成的正向物流多級配送系統(tǒng)庫存的供應(yīng)鏈模型和基于內(nèi)部命令下的閉環(huán)多級配送系統(tǒng)被設(shè)計出來。一個基于不完整信息下的綠色供應(yīng)鏈管理得到完善。逆向物流被視為未來研究的重要領(lǐng)域，它的柔性預(yù)測計算能力使得它能夠應(yīng)對物流活動中的不確定性和準確性。一個考慮了設(shè)施定位與布置的數(shù)學(xué)模型在文中被提出，為了能夠研究逆向物流的網(wǎng)絡(luò)設(shè)計。通過使用從電腦公司搜集的數(shù)據(jù)信息，成功構(gòu)建了一個動態(tài)模型并得以使用。一個結(jié)合了制造和再制造的配送系統(tǒng)在文中被提出，提出了不同價格相對比之下的多種模型。從上述的參考文獻中，研究已經(jīng)完成了對于逆向物流網(wǎng)絡(luò)的設(shè)計和第三方物流提供商的選擇。但是層次分析法和模糊環(huán)境下逼近理想解排序法尚未被任何研究者用于逆向物流運作渠道的選擇。企業(yè)可以選擇三種運營渠道來進行逆向物流活動a）制造商自己收集可回收產(chǎn)品制造商自營（MO）。制造商應(yīng)該控制好人力上的資源，信息系統(tǒng)和相關(guān)的設(shè)備。b)經(jīng)銷商進行產(chǎn)品的回收聯(lián)合運營（JO）。C）外包給第三方第三方運營（TPO）?？梢酝ㄟ^第三方來減少再制造的花費。由于第三方物流企業(yè)可以利用它的最新的技術(shù)和資源共享優(yōu)勢，產(chǎn)品回收的不確定性可能就會被降低。通過外包逆向物流活動，企業(yè)可以更加專注于自己核心業(yè)務(wù)的運營，而且顧客的滿意度和評估績效就會大大提高。第三方逆向物流提供商將在價格，質(zhì)量和信譽等特定領(lǐng)域進行競爭。物流的成本將會降低，同時訂單的滿意率將會大大提高。每一種渠道都有各自的獨特之處，并且有適合他們的滿足服務(wù)要求的公司。評估和選擇逆向物流渠道被認為是多準則決策（MCDM）的重要過程，這個過程要求決策者在所存在的可選擇對象中作出最佳的選擇。本文的架構(gòu)組織如下。第二部分將提出問題，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，第五部分進行總結(jié)，得到研究成果。2 問題定義逆向物流可以應(yīng)用于各種行業(yè)中，像汽車、電子、化學(xué)和電腦制造等行業(yè)。汽車企業(yè)回收報廢汽車的零部件。包含有害物質(zhì)的電子產(chǎn)品可以被重新利用。逆向物流可以通過制造商自營（MO）、聯(lián)合運營（JO）和第三方運營（TPO）三種模式進行。3 提出的方法決策者發(fā)現(xiàn)了評估各種可選擇方案的問題，然后使用一套標準選擇了最好的一個。有著數(shù)量限制的可選擇方案條件下，多準則決策（MCDM）是離散的。在多準則決策中決策矩陣包含了三個主要的部分a）可選擇方案b）標準c）權(quán)重。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)合的混合方法將會呈現(xiàn)。標準的權(quán)重均采用層次分析法計算。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。因為層次分析法本身就是一種決策方法，它能夠得到精準的比例尺度的測量。通過將它的混合標準與其他許多決策支持工具和方法相結(jié)合能更好地應(yīng)用于實例中。通過三角模糊數(shù)下語言變量值參數(shù)化使得不確定性和不準確性的問題得到解決。3.1 層次分析法層次分析法是由Thomas L. Saaty教授首先提出的，層次分析法把一個復(fù)雜的問題分解成相關(guān)聯(lián)的決策元素的層次結(jié)構(gòu)。層次分析法可以處理各種目標以及無形的主觀屬性。層次分析法的步驟如下：3.1.1 問題的模型層次化在最高水平目標的前提下制定一個分層次的結(jié)構(gòu)，第二層級是標準，第三層級是可選擇的方案，可選擇方案受到不確定活動的影響，而且與所有的標準相關(guān)聯(lián)。3.1.2 構(gòu)造兩兩相比較的矩陣擁有緊鄰的較高級別的元素的一組比較矩陣進行構(gòu)建。在兩兩對比下決策者作出起到支配元素的認知。3.1.3 通過計算固定矢量檢驗一致性每個屬性相對規(guī)范化的權(quán)重是通過計算幾何平均數(shù)和歸一化該行的幾何平均數(shù)來決定的，在比較矩陣中。如果一致性比率等于0.1或者低于0.1，則被視為可接受的矩陣M>5.如果一致性比率超過可接受值，不一致就出現(xiàn)，判斷不可信，評估過程需要進一步提高。一致性比率有助于確保決策者在決定條件優(yōu)先次序下做出更可靠的決策。3.2 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 &Yoon首先提出的。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準，最小化了成本標準）以及遠離了消極的理想解決方案。在傳統(tǒng)的TOPSIS方法中，這些標準的權(quán)值是精確的已知的，清晰值被用于評估過程中。然而主要的缺點是關(guān)于代表決策者意見清晰值得不確定性和不準確性。因此，模糊TOPSIS法被提出。模糊集合論允許決策者將無法量化的信息，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。模糊集合論是為了處理在模糊和不精確條件下，來自于大量信息的抽象的主要可行效果。語言變量在處理過多層面或者在沒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。在模型中應(yīng)用的語言變量對于每一個標準可以表示為三角模糊數(shù)。4 模型應(yīng)用所提出的模型被應(yīng)用于工業(yè)上的一個問題。一個坐落于印度南部的一家印刷廠被選作案例。該企業(yè)想擁有一套系統(tǒng)性的實施逆向物流的方法。逆向物流在制造過程中產(chǎn)生了顯著的效果，正確的決策決定將會為企業(yè)帶來競爭優(yōu)勢。因此選擇正確的運營渠道受到了企業(yè)的高度重視。為了從客戶手中收回用過的報紙和雜志，企業(yè)可能會選擇MO,TPO和JO三種中一種。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)用來進行選擇。5 結(jié)論由于財務(wù)和運營條件的進入，逆向物流的實施可能對于企業(yè)是一項不安全的工作。然而越來越多的環(huán)境上的問題，迫使企業(yè)去選擇逆向物流。問題是企業(yè)究竟通過何種運營渠道來進行退貨的回收?；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解決逆向物流運作渠道的選擇。這個問題已經(jīng)被描述為不確定條件下多準則決策方法，提醒決策者作出在不確定條件下的判斷的要求。未來的研究包含了結(jié)合層次分析法和模糊折中排序法（VIKOR）的一種兩階段方法，并進行敏感度的分析以確定穩(wěn)定性。附件2：外文原文A Decision Making Methodology For The Selection Of Reverse Logistics Operating ChannelsAuthor：S.Senthil，B.Srirangacharyulu, A.Ramesh.Procedia Engineering.2012Abstract An efficient management of product returns is a strategic issue. Nowadays, customer expect manufacturer to develop a reverse logistics system so that the returned products can be recovered. With the development and advancement of reverse logistics practice, the selection of reverse logistics operating channels becomes more important. There are three operating channels of reverse logistics; 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; Multicriteria Decision Making; AHP Method1. Introduction Due to the growing environmental legislations, more attention is given to Reverse Logistics. Reverse Logistics (RL) is the process of planning, implementing and controlling the efficient, cost effective flow of raw materials, in process inventory, finished goods and related information from the point of consumption to the point of origin for the purpose of recapturing value, or proper disposal .The study of reverse logistics is in exploration stage. Cost reduction is possible in reverse logistics. A reverse logistics defines a supply chain that is redesigned to efficient manage the flow of products or parts designed for remanufacturing, recycling or disposal and to effectively utilize resources .The various functions executed through RL activities include gatekeeping, compacting disposition cycle times, remanufacturing and refurbishment, asset recovery, negotiation, outsourcing and customer service .In addition to disposition and transportation, value added services such as JIT, quick response and program solutions are also important functions in reverse logistics. Recovery of products for remanufacturing, repair and recycling can create profitable business opportunities . For managing the returns, the companies can reuse them, resell or destroy them. Retailers may return the goods due to seasonality, expiry or because of transit damage. Customers may return the goods due to poor quality. Managing the product returns increases the customer service level and retention level. Each activity from procurement to distribution generates waste and reduction of this waste is a major goal of environmentally conscious business practices. Manufactures see reverse logistics as a process of recovering defective products or reusable containers back from the user. In the e-commerce since buyers need assurance for refund, reverse logistics is an important issue. Owing to RL approach, this area present an opportunity for research. A conceptual framework for managing retail reverse logistics operation is presented in .In the case study conducted by , three companies were visited and identified reverse logistics process flow and the strategic issues a firm may use for competitive advantage. An integrated forward logistics multi echelon distribution inventory supply chain model and closed loop multi echelon distribution for the built to order environment was designed using genetic algorithm and particle swarm optimization .A model for green supply chain management with incomplete information was developed .Reverse logistics was suggested as an area for future research and the advantages of soft computing is its capability to tolerate imprecision, uncertainty .A mathematical model for the design of Reverse Logistics network design was proposed considering the location and allocation of facilities. A dynamic model was constructed and validated the same using the data collected from the computer company. A distribution system which uses a combination of manufacturing and remanufacturing was proposed and the models were compared with respect to the various prices. From the above references, studies have been done for the RL network design and the selection of third party logistics provider. But AHP and Fuzzy TOPSIS has not been used by any researcher for selection of RL operating channels selection. The companies can choose three operating channels for performing the RL activities a) Manufacturer collecting the used products-Manufacturer Operation (MO). The manufacturer should control human resources, information systems and related equipment. b) Retailer will collect the used products- Joint Operation (JO). c) Outsourcing to third party-Third Party Operation (TPO). Remanufacturing costs may be reduced by third party. Since the third party logistics is using his latest technology and resource sharing advantages, uncertainty of recovery may be reduced. By outsourcing reverse logistics activities, the organizations can concentrate on their core business operation, but customer satisfaction and delivery performance may be improved. Third party reverse logistics provider will compete with each other in specific areas like price, quality and credit. Logistics costs will be reduced and order fill rate will be improved. Each channel has its distinct characters and suitable for companies with their sole service requirements. Evaluating and selecting reverse logistics channels is regarded as Multi criteria decision making (MCDM) process in which a decision maker chooses the best option among the existing alternatives. This paper is organized as follows. Section 2 describes the problem and in section 3 an overview of method is given. Application of the model to a case study is given in section 4.Section 5 concludes the study and summarizes its findings. 2. Problem Definition Reverse Logistics can be applied to wide variety of industries like automobile, electronic, chemical and computer manufacturers. Automobile companies recover the end of life auto parts. Electronic products that contain hazardous materials are disposed. Reverse Logistics may take place through Manufacturer Operation (MO), Third Party Operation (TPO), or Joint Operation (JO).3. Proposed Methodology Decision makers find the problem of assessing the variety of alternatives and then selecting the best one using a set of criteria. Multiple criteria decision making methods (MCDM) are discrete with a restricted number of alternatives. A decision matrix in MCDM consists of three main parts a) Alternatives b) Criteria c) Weights of Relative importance. 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 presented. The weights of criteria are calculated by applying the AHP method. The Fuzzy TOPSIS method is applied to get the final ranking results. Although AHP is a decision-making methodology in itself. its ability to get accurate ratio scale measurements and combine them across multiple criteria has led to AHP applications in conjunction with many other decisions support tool and methodologies. Uncertainty and imprecision is handled with linguistic values parameterized by the triangular fuzzy number.3.1 Analytic Hierarchy ProcessAHP method is developed by Prof. Thomas L. Saty. AHP divides a complex problem into a hierarchy of interrelated decision elements. AHP can deal with objective as well as non-tangible subjective attributes. The procedure of AHP is as follows3.1.1. Model the problem as a hierarchyDevelop 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.3.1.2 Construct a pair-wise comparison matrixA set of comparison matrix with respect to an element of immediately higher level is constructed. The pair-wise comparisons capture a decision makers perception of which element dominates the other.3.1.3 Test the Consistency by calculating the Eigen VectorsThe 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 comparison matrix.A consistency ratio of 0.1 or less is considered as acceptable for matrices M> 5.If 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.3.2Fuzzy TOPSIS MethodTechnique for Order Preference by Similarity to Ideal Solution (TOPSIS) was first established by Hwang &Yoon. The best alternative would be the one that is nearest to the positive ideal solution (the solution that maximizes the benefit criteria and minimizes the cost criteria) and farthest away from the negative ideal solution. In the traditional TOPSIS method, the weights of the criteria are known precisely and crisp values are used in the evaluation procedure. However the major drawback is that the uncertainty and imprecision related with representing decision makers observations to crisp values. Therefore, the fuzzy TOPSIS method is proposed. Fuzzy set theory allows the decision maker to incorporate unquantifiable information, incomplete information and non-obtainable information and partially ignorant facts into the decision model .The fuzzy set theory is intended to deal with the abstraction of the main viable effect from an array of information that is expressed in vague and imprecise terms. Linguistic variable is very useful in dealing with circumstances, which are too multifaceted or not well defined to be reasonably described in typical quantitative terms. The linguistic variables that are applied in the model can be expressed in triangular fuzzy Numbers for each criterion.4. Numerical Illustration Proposed model is applied to a real problem in industry. A printing industry located in the Southern part of India is selected. The industry wanted a systematic way to implement the reverse logistics operations. Reverse Logistics brings significant improvements in the manufacturing process and the correct decisions made brings the industry competitive advantage. Therefore selecting the most important operating channels is of great importance for the industry. To collect the used papers and magazines from the customers, the industry may choose MO, TPO, JO. A hybrid methodology combing AHP and TOPSIS under fuzzy environment is utilized for the selection. Schematic diagram of the proposed model is presented in figure 1.First a team comprising two engineers and one manager was formed. The data required for selection are obtained through direct questions.5. Conclusions Since financial and operational attributes are involved, the implementation of reverse logistics may be an unsafe task for the industry. However growing environmental concerns have forced the industries to opt for reverse logistics. The question is by which channel the industries will be able to collect the returned product. A methodology based on AHP and TOPSIS under fuzzy environment is proposed for the selection of RL operating channels. The problem has been described as a multi-criteria decision making method under uncertainty, prompting the need for the method to handle imprecise judgments from decision makers. Future research includes incorporating a two phase methodology combining AHP and Fuzzy VIKOR and carrying out sensitivity analysis to confirm the robustness.