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研究生:邱俊智
研究生(外文):Chun-Chih Chiu
論文名稱:不確定需求下多階供應鏈訂單分配策略之研究
論文名稱(外文):A Study of Order Allocation Strategies for Multi-Stage Supply Chain under Uncertainty
指導教授:王文派王文派引用關係
指導教授(外文):Wen-Pai Wang
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:工業工程與管理系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:中文
論文頁數:111
中文關鍵詞:多階供應鏈需求預測類神經模糊系統原物料訂單分配非等效平行機台生產排程系統模擬基因演算法
外文關鍵詞:Multi-Stage Supply ChainDemand ForecastsNeuro-fuzzy systemOrder AllocationScheduling Unrelated Parallel MachinesSimulationGenetic Algorithms
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在需求不確定,產品多樣化與交期縮短的經營環境下,製造商正面臨極為嚴厲的挑戰;亦因全球化市場競爭激烈、資訊科技蓬勃發展,且產品生命週期縮短,客製化已成為生產系統之主流,導致顧客需求的不確定與多變性驟增,造成廠商在預測顧客需求方面,已無法應用傳統的預測模式來進行準確的預測。本研究結合模糊理論與類神經網路,建置類神經模糊系統,由實際工廠訂單資料中歸納出訂單屬性之隸屬函數與模糊規則,使需求預測之模糊決策規則庫能適應環境變遷而調整;所得結果並與傳統迴歸分析、倒傳遞類神經網路、灰色預測方法比較,驗證所提預測模式之正確性。且值此全球景氣正從因金融海嘯所造成歷來最嚴峻的全球經濟大衰退的陰霾谷底逐漸翻升之際,企業的經營管理更是面臨前所未有的挑戰。本研究即以模擬分析建構多階供應鏈訂單分配模式,使以利企業面臨此不確定環境下時,可得以擬定適切之採購及生產訂單分配策略;並針對當作業/整備時間與效能不同之平行機台,在考慮不同訂單類型、緊急訂單發放及存量管制等因素之排程問題下,以最小總完工時間為目標下,結合基因演算法與反應曲面法,決定訂單在非等效平行機台加工時之最適加工順序與最佳參數水準組合,建構廠內合宜之排單系統,期使提升排程規劃的績效,進而改善並克服平行機台應用於實務中所面對的各項挑戰。本研究所提不確定需求下多階供應鏈控管系統,可提供廠商瞭解其供應鏈管理之影響因素、各種成本的衡量,並作為供應鏈管理決策之參考,研擬合適的顧客需求管理、供應商訂單分配管理及生產排程管理之策略。
Because of demand uncertainty, product variety and due-date shortening, manufacturers are confronted with great challenges in conditions of business. Moreover, due to tough competition in the globalization, and fast changes in technology, and short life cycle of products, customization has become a tendency of production systems in today’s business environment. Suchlike phenomena lead to increase uncertainty and variety of demands abruptly, and make traditional methods incapable of proceeding accurate forecast. This paper therefore integrates fuzzy theory and neural network into a neuro-fuzzy system in which it is based on ANFIS as the foundation. The approach generalized the membership function and fuzzy rules from the attributes of historical orders. It enabled the fuzzy decision rule database of demand forecasts to possess adaptability, and is compared with regression analysis, back-propagation network and grey forecasting to verify its efficiency and effectiveness using real order data of two manufacturing companies in Taiwan. On the occasion of fierce worldwide recession, the paper develops a multi-stage supply chain order allocation model using simulation to assist enterprises in determining suitable purchasing and production allocation strategies. Afterward the paper considered diverse operation/setup times, order types, rush order released and inventory control factors to enhance scheduling performance, applying response surface method and genetic algorithms with the goal of minimal total completion time to determine the optimal factor combination and processing sequence of orders on unrelated parallel machines under uncertainty for increasing profits and enhancing service levels. It is be beneficial to raise believability and acceptance generated from the results of the proposed system. Ultimately the goal of enhancing administration performance and satisfying customer needs can be achieved.
中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VI
表目錄 VII
第一章、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究範圍 5
1.4 研究架構 6
第二章、文獻探討 8
2.1 供應鏈 8
2.1.1 供應鏈管理 9
2.1.2 多階供應鏈 9
2.2 需求預測方法 10
2.2.1 模糊理論 12
2.2.2 類神經網路 13
2.1.3類神經模糊系統 14
2.1.4 灰色系統理論 15
2.3 系統模擬應用於供應鏈訂單分配 16
2.4 平行機台生產排程 19
2.4.1 基因演算法 20
2.4.2 實驗設計與反應曲面法 21
第三章、研究方法 23
3.1 以ANFIS建置顧客需求預測模型 23
3.1.1 ANFIS模式限制 23
3.1.2 ANFIS模式應用程序 26
3.2 倒傳遞類神經網路 27
3.2.1 倒傳遞類神經網路架構 28
3.2.2 倒傳遞類神經網路演算法 28
3.2.3 BPN模式應用程序 30
3.3 灰色模型建構 30
3.3.1 灰預測模型應用程序 30
3.4 系統模擬建構供應商之訂單分配 31
3.4.1 供應商訂單分配模式建構 34
3.4.2 供應商訂單分配模式績效衡量 35
3.5 基因演算法應用於平行生產訂單排程分配 35
3.5.1 基因演算法參數設定 36
第四章、供應鏈訂單分配策略模式建置 40
4.1 需求預測模式建構 41
4.1.1 應用ANFIS預測顧客訂購數量 41
4.1.2 應用倒傳遞類神經網路預測訂購數量 42
4.1.3應用灰預測分析預測訂購數量 43
4.1.4 應用迴歸方法分析預測訂購數量 43
4.1.5 預測結果分析與討論 44
4.2 供應商訂單分配模式建構 47
4.2.1 模式1建構(預測式原物料訂單分配系統) 47
4.2.2 模式2建構(考慮緊急訂單發放) 48
4.2.3 模式3建構(考慮存量管制系統) 49
4.2.4 訂單分配模擬建構步驟 50
4.2.5原物料訂單分配各模式比較與分析 52
4.3 非等效平行機台生產排程模式建構 53
4.3.1 非等效平行機台基因演算法建構步驟 53
4.3.2 基因演算法排單程式介面 54
4.3.3 基因演算法參數實驗設計 57
4.3.4 實驗設計求解參數結果 58
4.3.5 反應曲面法求解參數結果 60
4.3.6 基因參數實驗設計與反應曲面法求解品質比較 63
第五章、結論與未來研究方向 64
5.1結論 64
5.2 未來建議 66
參考文獻 67
附 錄1 各產品訂單數量資料 73
附 錄2 原物料訂單分配輸入資料統計分配 74
附 錄3 基因演算法程式碼 82
附 錄4 實驗設計與反應曲面法實驗配置表 96

圖目錄
圖1. 論文架構圖 7
圖2. 供應鏈產品加工配送流程圖 8
圖3. 多產品多階段供應鏈流程圖 10
圖4. 緊急訂單的成因及因應之道 14
圖5. ANFIS系統架構 23
圖6. Sugeno模糊推論模式 26
圖7. ANFIS輸出值與誤差 27
圖8. 倒傳遞類神經網路架構 28
圖9. 供應鏈之原物料採購架構 32
圖10. 基因演算法流程圖 36
圖11. 染色體編碼示意圖 37
圖12. 研究計劃整體架構與流程 40

圖13. 某產品ANFIS網路架構 41

圖14. MATLAB倒傳遞神類神經網路架構 42

圖15. 灰預測誤差比較圖形 43

圖16. MINITAB14預測迴歸模式建構 43

圖17. 原物料訂單分配模式1建構 48

圖18. 原物料訂單分配模式2建構 49

圖19. 原物料訂單分配模式3建構 50

圖20. 適應值計算示意圖 53

圖21. 交配示意圖 54
圖22. 突變示意圖 54

圖23. 程式介面之訂單資訊 55

圖24. 程式介面之機台資訊 55

圖25. 程式介面之每代最佳解 56

圖26. 程式介面之最終輸出解 56

圖27. 主效用圖 58
圖28. 實驗設計殘差常態機率圖 59

圖29. 實驗設計(初始母體)變異數相等假設 59
圖30. 實驗設計(交配率)變異數相等假設 59

圖31. 反應曲面法常態機率圖 61

圖32. 反應曲面法最佳化參數 62

圖33. 反應曲面法(初始母體)變異數相等假設 62

圖34. 反應曲面法(交配率)變異數相等假設 62
表目錄
表1. 模糊理論研究應用文獻整理 12
表2. 平行機台依特性分類表 19
表3. 倒傳遞類神經網路參數設定 42

表4. 各方法、類型誤差比較總表 44

表5. ANFIS、迴歸分析預測差異比較 45

表6. ANFIS、倒傳遞類神經網路預測差異比較 45

表7. ANFIS訓練資料期數不同比較 46

表8. 類型2各方法預測誤差比較 46

表10. ANFIS與灰預測Case1誤差比較 47

表11. 原物料訂單分配各預測方法存、缺貨比較表 51

表12. 模式1及模式2各成本與利潤比較 51

表14. 實驗配置表 57
表15. 實驗設計ANOVA表 57
表16. 反應曲面法缺適性檢定 60
表17. 反應曲面法ANOVA表 61
表18. 兩種方法不同張數訂單測試比較 63


[1]. 李允中,王小璠,蘇木春,2002,模糊理論及其應用,台北:全華圖書股份有限公司。
[2]. 李鉦慶,2006,以模擬為基礎求解多產品多供應商訂單分配問題 – 以IC設計公司為例,碩士論文,國立成功大學製造工程系。
[3]. 周湘蘭, 2002,類神經網路在多重產品需求預測上之應用,碩士論文,元智大學工業工程與管理系。
[4]. 林虹谷,2002,製造業即時排程與重排程基礎架構,國立高雄第一科技大學資訊管理系。
[5]. 林隆儀、羅文坤、鄭英傑,新產品行銷策略,超越企管顧問股份有限公司,1991。
[6]. 邱昌盛,2007,多廠商供應鏈環境下的訂單分配,碩士論文,國立交通大學。
[7]. 張玉鈍,曾毓文,2000,容許批量分割之非相關平行機器排程,科技與管理學術研討會,pp.93-102。
[8]. 陳彥良,凌俊青,許秉瑜, 2001,在包裹式資料庫中挖掘數量關聯規則,資訊管理學報,第七卷第二期,215-229。
[9]. 陳美棟,1998,訂單式生產系統之緊急訂單評估模式, 碩士論文,義守大學管理科學系。
[10]. 陸維仁,2009,半導體產業訂單分配模式之績效評估,碩士論文,國立高雄第一科技大學運籌管理系。
[11]. 馮正民、陳其華、趙珮君、王一帆、顏子揚、蔡于婷、張芸綾(2005)。筆記型電腦廠商全球供應鏈系統物流設施區位評選多目標最適規畫(行政院國家科學委員會專題研究計畫成果報告)。
[12]. 曾治瑋,2009,應用適應性模糊類神經系統於台灣地區汽車銷售預測,碩士論文,國立臺灣科技大學工業工程與管理系。
[13]. 溫坤禮、張簡士琨、葉鎮愷、王建文、林慧珊,2007,MATLAB 在灰色系統理論的應用,台北市,全華。
[14]. 葉怡成,2000,類神經網路模式應用與實作,台北市:儒林。
[15]. 葉麗芬,2001,雙目標非等效平行機台排程問題之探討,碩士論文,元智大學工業工程與管理學系。
[16]. 鄧聚龍,1992,灰色系統理論教程,華中理工大學出版社。
[17]. 謝文凱, 2007,應用類神經網路於造紙業研究型生產之研究,碩士論文,國立台灣科技大學自動化及控制系。
[18]. 羅強華,2005,類神經網路-MATLAB的應用,台北市:高立。

[19]. Allahverdi, A. & Mittenthal, J. (1994). Scheduling on M Parallel Machines Subject to RANDom Breakdowns to Minimize Expected Mean Flow Time. Naval Research Logistics, 41(5), 677-682.
[20]. Anderson, D. L., Frank F. B. & Donavon J. F., (1996), The Seven Principles of Supply Chain Management. Supply Chain Management , 19-29.
[21]. Bersini, H. & Bontempi, G., (1997), Now comes the time to defuzzify neuro- fuzzy models. Fuzzy Sets & Systems, 90(2), 161-170.
[22]. Chandra P. & Fisher M. L. (1994), Coordination of production & distribution planning. European Journal of Operational Research, 74(3), 503-517.
[23]. Chang, P. C., Wang, Y. W. & Liu, C. H., (2007), The Development of A Weighted Evolving Fuzzy Neural Network for PCB Sales Forecasting. Expert Systems with Applications, 32(1), 86-96.
[24]. Chen, C. M., (2003), Rush order evaluation & capacity planning in a multi-site manufacturing environment, Master Thesis, Institute of Industrial Engineering & Management, YUAN_ZE University, Taiwan.
[25]. Chen, D. W., Zhang, J. P., (2005), Time series prediction based on ensemble ANFIS, International Conference on Machine Learning & Cybernetics, 6, 3552-3556.
[26]. Christopher, M., (1992), Logistics & supply chain management: strategies for reducing costs & improving service. London, Pitman.
[27]. Chu, P.C., Beasley, J.E., (1997), A genetic algorithm for the generalized assignment problem. Computers & Operations Research, 24, 17–23.
[28]. Dahel, N.E., (2003), Vendor selection & order quality allocation in volume discount environments. Supply Chain Management, vol. 8, pp. 335-342.
[29]. Davis, T., (1993), Effective Supply Chain Management. Slone Management Review, Vol. 34, pp. 35-46.
[30]. De Boer, M., Bosch, F.A.J. & Volberda, H.W. (1999), Managing organizational knowledge integration in the emerging multimedia complex. Journal of Management Studies, 36(3), 379-398.
[31]. Ehteshami, B., Petrakian, R.G., & Shabe, P.M. (1992), Trade-off in cycle time management: Hot lots. IEEE Transactions on Semiconductor Manufacturing, 5(2), 101–105.
[32]. Fleisch, E., Tellkamp C., (2005), Inventory Inaccuracy & Supply Chain Performance: A Simulation Study of a Retail Supply Chain. International Journal of Production Economics, 95(3), pp.373-385.
[33]. Grefenstette, J., (1986), Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on System, Man & Cybernetics, 16, 122-128.
[34]. Gürsel A. Süer, Francisco Pico & Aidsa Santiago (1997), Identical machine scheduling to minimize the number of tardy jobs when lot-splitting is allowed. Computers & Industrial Engineering, 33(1-2), 277-280.
[35]. Hanada, A. & Ohnishi, K., (1993), Near Optimal Job-shop Scheduling Using Neural Network Parallel Computing. Industrial Electronics, Control, & Instrumentation, 1, 315-320.
[36]. Haq, A.N., Vrat, P., & Kanda, A., (1991), An integrated production-inventory-distribution model for manufacture of urea: a case. International Journal of Production Economics, 25(1), 39-49.
[37]. Hemant, Kumar, N. S. & Srinivasan, G., (1996), A genetic algorithm for job shop scheduling-A case study, Computers in Industry, 31, 155-160.
[38]. Hinojosa, Y., Puerto, J. & Fernandez, F. R., (2000), A multiperiod two-echelon multi commodity capacitated plant location problem. European Journal of Operational Research, 123(2), 271-291.
[39]. Hong, T. P., Lin, K. Y., Wang, S. L., (2003), Fuzzy data mining for interesting generalized association rules, Fuzzy Sets & Systems, 138(2), 255-269 .
[40]. Horng, H.C. & Cochran, J. K., (2001), Project surface regions: a decision support methodology for multitasking workers assignment in JIT systems. Computers & Industrial Engineering, 39, 159-171.
[41]. Horowitz, E. & Sahni, S., (1976), Exact & approximate algorithm for scheduling nonidentical processors. Journal of the Association for Computing Machinery, 23(2), 317-327.
[42]. Hsieh, J.C., Chang, P.C., & Hsu, L.C. (2003), Scheduling of drilling operations in printed circuit board factory. Computers & Industrial Engineering, 44(3), 461–473.8
[43]. Hu, Y.C., Chen, R.S., Tzeng, G. H., (2003), Finding fuzzy classification rules using data mining techniques. Pattern Recognition Letters, 24(1-3), 509-519.
[44]. Huang, Y.F., Zheng, M. C. & Wu, C. H. (2004), Comparison of various different approaches to tourist demand forecasting, Journal of grey system, 7(1), 21-27.
[45]. Jang, J.S.R., Sun, C.T. & Mizutani, E. (1997), Neuro-Fuzzy & Soft Computing, Prentice- Hall.
[46]. Jou, C., (2005), A genetic algorithm with sub-indexed partitioning genes & its application to production scheduling of parallel machines. Computers & Industrial Engineering, 48, 39-54.
[47]. Kawtummachai, B. & Hop, N.V., (2005), Order Allocation in a Multiple-Supplier Environment, International Journal of Production Economics, 93-94, 231-238.
[48]. Kelton, W.D., Sadowski, R.P., & Sturrock, D.T., (2006), Simulation with Arena, 4th edition, McGraw-Hill, New York.
[49]. Kim, D.W., Kim, K.H., Jang, W., & Chen, F.F. (2002), Unrelated parallel machine scheduling with setup times using simulated annealing. Robotics & Computer Integrated Manufacturing, 18, 223–231.10
[50]. Kuo, Y., Chang, I. & Yang, T., (2003), A hybrid genetic algorithms & simulation method in solving a parallel machine dispatch problem, Seventh International Conference on Automation Technology, Taiwan.
[51]. Lau, J.S.K., Huang, G.Q., Mark, K.L., (2004), Impact of Information Sharing on Inventory Replenishment in Divergent Supply Chain, International Journal of Production Research, 42(5), pp.919-941.
[52]. Lee, Z. J., Lee, C. Y., (2005), A hybrid search algorithm with heuristics for resource allocation problem. Information Sciences, 173, 155–167.
[53]. Li, Y. H. & Pinedo, M. (1997), Scheduling jobs on parallel machines with sequence- dependent setup times, European Journal of Operational Research, 100, 464-474.
[54]. Lin, C.C. & Wang, T. H., (2007), The Supply Chain Design With Manufacturing Postponement under Uncertain Demands, INFORMS international Conference, Rio Grande, Puerto Rico.
[55]. Liou, J.C., Huang, Y. F., (2006), The Research of Mother Board New Product Sales Forecast Using Grey Theory, The 2006(11th) National Conference on Grey System Theory & Applications, 157-162.
[56]. Man, K. F., Tang, K. S. & Kwong, S., (1996), Genetic Algorithms: Concepts & Applications, IEEE Transactions On Industrial Electronics, 43(5), pp. 519-533.
[57]. Man, K.F., Tang, K.S., Kwong, S., (1999), Genetic Algorithms. Springer-Verlag London Limited, London.
[58]. Min, L., Cheng, W., (1999), A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines, Artificial Intelligence in Engineering, 13, 399-403.
[59]. Min, L., Cheng, W., (2006), Genetic algorithms for the optimal common due date assignment & the optimal scheduling policy in parallel machine earliness/ tardiness scheduling problems. Robotics & Computer-Integrated Manufacturing, 22, 279-287.
[60]. Monma, C.L. & Potts, C.N. (1993), Analysis of heuristics for preemptive parallel machine scheduling with batch setup times, Operations Research, 41(5), 981-993.
[61]. Montgomery, D. C., (1997), Design & Analysis of Experiments, 4th ed., John Wiley & Sons, New York.
[62]. Oguz, C., Ercan, M.F., (2005), A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks. Journal of Scheduling, 8, 323–351.
[63]. Pan, W. Wang, S. Zhang, J. Hua, G. Fang, Y., (2008), Fuzzy Multi-Objective Order Allocation Model for Risk Management in a Supply Chain. International Conference on Modelling & Simulation, 771-776.
[64]. Pinedo, M., (1995), Scheduling, Prentice Hall.
[65]. Pinedo, M., (2002), Scheduling: theory, algorithms, & systems, Prentice-Hall Englewood Cliffs, New Jersey.
[66]. Schaffer, J.D., Caruana, R.A., Eshelman, L.J., & Das, R., (1989), A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization, Proceedings of the third international conference on Genetic algorithms, 51-60.
[67]. Shang, J.S. & Tadikamalla, P.R., (1998), Multicruteria design & control of cellular manufacturing system through simulation & optimization. International Journal of Production Research, 36, 1515-1528.
[68]. Shieh, H. M., & May, M. D., (2001), Solving the Capacitated Clustering Problem with Genetic Algorithms, Journal of the Chinese Institute of Industrial Engineers, 18, 1-12.
[69]. Silva, C., & Magalhaes, J.M. (2006), Heuristic lot size scheduling on unrelated parallel machines with applications in the textile industry. Computers & Industrial Engineering, 50, 76–89.
[70]. Somendra, P., Rajesh, S., & Madan, B. (2003), Making sense of the e-supply chain landscape: An implementation framework. International Journal of Information, 201-221.
[71]. Stefan, M. (2003), Multiple-supplier inventory models in supply chain management: A review. International Journal of Production Economics, 265-279.
[72]. Sugeno, M. & K. Tanaka, (1991), Successive Identification of a Fuzzy Model & its Applications to Predictions of Complex Systems. Fuzzy Sets & Systems, 42(3), 315-334.
[73]. Sugeno, M. & T. Yasukawa, (1993), A Fuzzy- Logic-Based Approach to Qualitative Modeling. IEEE Transactions on Fuzzy System, 1(1), 7-31.
[74]. Thiesing, F. M., Vornberger, O. (1997), Sales Forecasting Using Neural Networks. International Conference on Neural Networks, 4, 2125-2128.
[75]. Thomassey, S., M. Happiette & J.M. Castelain, (2005), A Short & Mean-term Automatic Forecasting System–Application to Textile Logistics. European Journal of Operational Research, 161(1),275-284.
[76]. Tu, K. Y., Liao, C. S. (2007), Application of ANFIS for Frequency Syntonization Using GPS Carrier-Phase Measurements, Frequency Control Symposium. 2007 Joint with the 21st European Frequency & Time Forum, IEEE International, 933-936.
[77]. Turban, Mclean & J. Wetherbe, (2002), Information Technology for Management, 3th ed., Wiley, New York.
[78]. Van der Velde, S.L., (1993), Duality based algorithm for scheduling unrelated parallel machines. ORSA Journal on Computing, 5(2), 192-205.
[79]. Wang, W. P., Chen, Z. (2008), A neuro-fuzzy based forecasting approach for rush order control applications. Expert Systems with Applications, 35(1-2), 223-234.
[80]. Wang, Y. H., (2007), Using Learning Curve Model to Construct an Order Distribution Decision Supply System of Global Logistics for Apparel Industry Master Thesis, Institute of Commerce Automation & Management, National Taipei University of Technology, Taiwan.
[81]. Watson, K. & Polito, T., (2003), Comparison of DRP & TOC Financial Performance within a Multi-product, Multi-echelon Physical Distribution Environment. International Journal of Production Research, 41(4), 741-765.
[82]. Wu, M. C. & Chen, S. Y., (1996), Cost Model for Justifying the Acceptance of Rush Orders, International Journal of Production Research, 34(7), 1963-1974.
[83]. Yang, T. & Olmen, R.V., (2004), Robust design for a multilayer ceramic capacitor screen-printing process case study. Journal of Engineering Design, 15( 5), 447-457.
[84]. Yu, A. Q. & Gu, X. S. (2008), A Coupled Transiently Chaotic Neural Network Approach for Identical Parallel Machine Scheduling. Acta Automatica Sinica, 34(6), 697-701.
[85]. Yu, L., Helosia, M.S., Pefund, M., Carlyle, W. M., & Fowler, J. W. (2002), Scheduling of unrelated parallel machines: an application to PWB manufacturing. IIE Transactions, 34(11), 921-931.
[86]. Yu, L., Shih, H.M., Pfund, M., Carlyle, W.M., & Fowler, J.W. (2002), Scheduling of unrelated parallel machines: an application to PWB manufacturing. IIE Transactions, 34, 921–931.30
[87]. Zarandi, F.M.H., & Soroosh, S., (2003), A Comprehensive Fuzzy Multi-Objective Model for Supplier Selection Process. IEEE International Conference on Fuzzy System, 1, 368-373.
[88]. Zhao, X., Xie, J. & Lau, R.S.M., (2001), Improving the Supply Chain Performance: Use of Forecasting Models Versus Early Order Commitments, International Journal of Production Research, 39(17), 3923-3939.

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