系統識別號 | U0002-1502201913192800 |
---|---|
DOI | 10.6846/TKU.2019.00364 |
論文名稱(中文) | 基於價值距離衡量之動態多準則決策方法及其應用 |
論文名稱(英文) | A dynamic multi-attributes decision making method based on value distance and its applications |
第三語言論文名稱 | |
校院名稱 | 淡江大學 |
系所名稱(中文) | 管理科學學系博士班 |
系所名稱(英文) | Doctoral Program, Department of Management Sciences |
外國學位學校名稱 | |
外國學位學院名稱 | |
外國學位研究所名稱 | |
學年度 | 107 |
學期 | 1 |
出版年 | 108 |
研究生(中文) | 尹亮 |
研究生(英文) | Liang Yin |
學號 | 899620099 |
學位類別 | 博士 |
語言別 | 英文 |
第二語言別 | |
口試日期 | 2019-01-16 |
論文頁數 | 108頁 |
口試委員 |
指導教授
-
徐煥智(shyur@mail.im.tku.edu.tw)
共同指導教授 - 鄭啟斌(cbcheng@mail.tku.edu.tw) 委員 - 郭人介(rjkuo@mail.ntust.edu.tw) 委員 - 伍台國(w134644932@gmail.com) 委員 - 范書愷(morrisfan@ntut.edu.tw) 委員 - 黃承龍(clhuang@nkust.edu.tw) 委員 - 時序時(hshih@mail.tku.edu.tw) 委員 - 徐煥智(shyur@mail.im.tku.edu.tw) 委員 - 曹銳勤(rctsaur@mail.tku.edu.tw) |
關鍵字(中) |
多屬性決策 展望理論 動態決策 群體決策 |
關鍵字(英) |
MADM Prospect theory Group decision Dynamic decision making |
第三語言關鍵字 | |
學科別分類 | |
中文摘要 |
為取得最令人滿意之決策結果,決策者的風險態度而不僅僅是待選項目的效用價值應被納入考量中。本研究使用S形曲線價值函數來取代傳統多屬性決策方法中的期望效用函數以反映決策者的風險趨避和風險追求行為。在此基礎上,為了進一步減輕使用者在標量參考點上遇到的困難,本研究使用價值函數和權重加總方法來定義每個待選項目相對於極端可行解的心理價值距離以衡量它們的總體展望價值。該方法的效能在比較分析和敏感度分析中得到了驗證,證明其能夠幫助減少多屬性決策中的常見的問題例如排序顛倒問題.之後,該方法被擴展到群體決策領域,將多位決策者的偏好加總後得出公正的解決方案。實驗證明了該方法是適當且穩定的。最後,為了處理現實社會中存在的動態多階段決策場景.本研究將此方法發展為動態多階段決策方法並應用在一個挑選海量資料服務建構商的實際標案過程中,前一輪決策結果被以回饋機制帶入到下一輪的決策過程中。使用者接受了最終結果並認為該決策過程是易用且有幫助的。 |
英文摘要 |
To achieve the most satisfying decision results, not only the utility value of the alternatives but also the risk attitudes of the decision makers need to be considered. In this proposed model, the s-shape value function is adopted to replace the expected utility function that is often used in traditional MADM methods to reflect the risk-averse and risk-seeking behavior of decision makers. On top of that, to further reduce the user burden of identifying the reference points, the psychological value distance is defined to measure the overall prospect values of each alternative reference to extreme feasible solutions using the value function and the additive weighting method. Performance of the proposed algorithms is comparatively analyzed and sensitivity analysis is conducted, to prove that this mechanism can help reduce issues like rank reversal. After that, the method is extended to a group decision setting, and the preferences of multiple decision makers are aggregated to produce a fair result. The experiments show that it is an appropriate and robust MADM method. Finally, considering the real world dynamic decision making scenario, the model is further developed to be dynamic (can handle more than one rounds of decision making, as defined in another research of a dynamic multiple-criteria decision making framework) (Campanella and Ribeiro, 2011), and then was applied in a big data service provider selection bidding case and the results from previous decision making process were carried to the following round using a feedback mechanism. The users accepted the final results and were satisfied with the easy and helpful decision making process. |
第三語言摘要 | |
論文目次 |
CONTENTS Chapter 1 Introduction 1 1.1 Research motivation 1 1.2 Research objectives 4 1.3 Research methodology 5 1.4 Research limitations 8 1.5 Dissertation structure 9 Chapter 2 Literature review 10 2.1 MADM 10 2.2 The Prospect Theory 13 2.3 The usage of the Prospect Theory in MADM 16 2.4 Dynamic decision process studies 19 Chapter 3 The proposed approach for static scenario 22 3.1 Election based on relative value distances 22 3.2 Election based on value distances 34 3.3 EBVD used in group decision scenario 38 Chapter 4 Evaluations and experiments 46 4.1 Evaluations for ERVD 46 4.2 Evaluations for EBVD 59 4.3 Comparison of ERVD and EBVD 70 Chapter 5 The proposed approach in dynamic scenarios 74 5.1 The framework of the dynamic EBVD 74 5.2 Detailed steps of the dynamic EBVD 77 Chapter 6 Illustrative example for dynamic scenario 83 6.1 Project background 83 6.2 Assessment procedures 86 6.3 The second round of the bidding process 89 Chapter 7 Conclusion and discussions 92 7.1 Advantages of the proposed methods 92 7.2 Scenarios to apply the proposed methods 95 7.3 Management implications and future studies 96 I. Appendix I: call center support system evaluation form – first round 98 II. Appendix II: call center support system evaluation form – second round 99 References 100 LIST OF TABLES Table 2 1 Recent MADM methods based on prospect theory 19 Table 2 2 Dynamic MADM methods 21 Table 3 1 Decision matrix of human resource selection (Opricovic, 1998) 29 Table 3 2 Weights on criteria (Opricovic, 1998) 30 Table 3 3 Normalized decision matrix 31 Table 3 4 Normalized value based decision matrix 32 Table 3 5 The relative closeness and rank by ERVD 33 Table 3 6 Decision matrix defined by decision maker 1 (Shih et al., 2007) 42 Table 3 7 Separation measures 44 Table 3 8 The aggregated relative closeness and rank by group EBVD 45 Table 4 1 The decision matrix and results obtained by TOPSIS and ERVD of Experiment 1 48 Table 4 2 The decision matrix and results obtained by TOPSIS and ERVD of Experiment 2 50 Table 4 3 Results obtained by ERVD with parameter α and β varied 52 Table 4 4 Results obtained by ERVD with parameter λ varied 56 Table 4 5 Results obtained by ERVD with reference point of C2 varied 58 Table 4 6 The decision matrix results obtained by TOPSIS and EBVD of Example 1 60 Table 4 7 The final values and rank obtained by TODIM 62 Table 4 8 Decision matrix of Example 2 (García-Cascales and Lamata, 2012) 63 Table 4 9 Results obtained by TOPSIS and EBVD of Example 2 63 Table 4 10 Results obtained by TOPSIS and EBVD of Example 2 with the incorporation of a new alternative 64 Table 4 11 Results obtained by EBVD with parameter α = β varied 66 Table 4 12 Results obtained by EBVD with attenuation factor of the losses varied 70 Table 4 13 EBVD and ERVD comparison using 4.1 experiment 1 71 Table 4 14 Subsection 4.2 Example 2 original scenario 72 Table 4 15 Subsection 4.2 Example 2 add new alternative 72 Table 6 1 The sample call-in phone call counts for short (<10 seconds) and longer (10-50 seconds) calls 84 Table 6 2 Sample report 85 Table 6 3 Assessment criteria for the first round of decision-making 87 Table 6 4 The parameters for the first round decision 88 Table 6 5 The decision matrix for the first round 89 Table 6 6 Ranking results for the first round 89 Table 6 7 Parameters used in the second round of decision-making 91 Table 6 8 Decision matrix for the second round 91 Table 6 9 Results for the second round 91 LIST OF FIGURES Figure 2 1 Value Function (Kahneman and Tversky, 1979) 14 Figure 4 1 Experiment on the range of α = β 51 Figure 4 2 Experiment on the range of α 53 Figure 4 3 Experiment on the range of β 54 Figure 4 4 Experiment on the range of λ (0.25 - 7) 55 Figure 4 5 Experiment on the range of λ (2.0 - 2.55) 57 Figure 4 6 The values according to various reference points. 59 Figure 4 7 Experiment on the range of α = β 65 Figure 4 8 Experiment on the range of α 67 Figure 4 9 Experiment on the range of β 68 Figure 4 10 Experiment on the range of λ (0.25 - 7) 69 Figure 5 1 The framework for a Dynamic EBVD 76 Figure 7 1 Different scenario to apply the corresponding proposed methods 95 Figure I 1 Call center support system evaluation form round 1 98 Figure II 1 Call center support system evaluation form round 2 99 |
參考文獻 |
References 1. Abdellaoui, M. (2000). Parameter-free elicitation of utilities and probability weighting functions. Management Science, 46, 1497–1512. 2. Abdellaoui, M., Bleichrodt, H., & Paraschiv, C. (2007). Measuring loss aversion under prospect theory: A parameter-free approach. Management Science, 53(10), 1659–1674. 3. Alanazi, H. O., Abdullah, A. H., & Larbani, M. (2013). Dynamic weighted sum multi-criteria decision making: Mathematical model. International Journal of Mathematics and Statistics Invention, 1(2), 16. 4. Baky, I.A. and Abo-Sinna, M.A. 2013. TOPSIS for bi-Level MODM problems. Applied Mathematical Modelling, 37: 1004–1015. 5. Brans, J. P., Mareschal, B. & Vincke, Ph. (1984). PROMETHEE – a new family of outranking methods in multicriteria analysis. Operational Research ’84, North-Holland, New York, 477-490. 6. Campanella, G., & Ribeiro, R. A. (2011). A framework for dynamic multiple-criteria decision making. Decision Support Systems, 52(1), 52. 7. Chen, C. L. P., & Zhang, C. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on big data. Information Sciences, 275, 314. 8. Cheng, S., Chan, C. W., & Huang, G. H. (2002). Using multiple criteria decision analysis for supporting decision of solid waste management. Journal of Environmental Science and Health, part A, 975-990. 9. Deng, H., Yeh, C., & Willis, R. J. (2000). Inter-company comparison using modified TOPSIS with objective weights. Computers & Operations Research, 27(10), 963. 10. Edwards, K. D. (1996). Prospect theory: A literature review. International Review of Financial Analysis, 5(1), 19. 11. Edwards, W. (1977). How to use multiattribute utility measurement for social decision making. IEEE Transactions on Systems, Man and Cybernetics. SMV-7, 326-340. 12. Fan, Z., Zhang, X., Chen, F., & Liu, Y. (2013a). Multiple attribute decision making considering aspiration-levels: A method based on prospect theory. Computers & Industrial Engineering, 65(2), 341-350. 13. Fan, Z., Zhang, X., Chen, F., & Liu, Y. (2013b). Extended TODIM method for hybrid multiple attribute decision making problems. Knowledge-Based Systems, 42, 40-48. 14. Fang, H., Li, J., & Song, W. (2018). Sustainable site selection for photovoltaic power plant: An integrated approach based on prospect theory. Energy Conversion and Management, 174, 755-768. 15. García-Cascales, M. S., & Lamata, M. T. (2012). On rank reversal and TOPSIS method. Mathematical and Computer Modelling, 56(5-6), 123-132. 16. Gomes, L. F. A. M., & Lima, M. (1992a). From modeling individual preferences to multicriteria ranking of discrete alternatives: A look at prospect theory and the additive difference model. Foundations of Computing and Decision Sciences, 17(3), 171-184. 17. Gomes, L. F. A. M., & Lima, M. M. P. P. (1992b). TODIM: Basics and application to multicriteria ranking of projects with environmental impacts. Foundations of Computing and Decision Sciences, 16(4), 113-127. 18. Gomes, L. F. A. M., & Rangel, L. (2009). An application of the TODIM method to the multicriteria rental evaluation of residential properties. European Journal of Operational Research, 193(1), 204-211. 19. Gomes, Luiz Flavio Autran Monteiro, Machado, M. A. S., & Rangel, L. A. D. (2013). Behavioral multi-criteria decision analysis: The TODIM method with criteria interactions. Annals of Operations Research, 211(1), 531. 20. Gurevich, G., Kliger, D., & Levy, O. (2009). Decision-making under uncertainty – A field study of cumulative prospect theory. Journal of Banking & Finance, 33(7), 1221. 21. Hu, J., Chen, P., & Yang, L. (2013). Dynamic stochastic multi-criteria decision making method based on prospect theory and conjoint analysis. Management Science and Engineering, 8, 65-71. 22. Hwang, C., & Yoon, K. (1981). Multiple attribute decision making. In Lecture Notes in Economics and Mathematical Systems, 186. 23. Jackson, M., Crouch, S., & Baxter, R. (2011) Software evaluation: criteria-based assessment. Software Evaluation Guide. Retrieved from https://www.software.ac.uk/resources/guides-everything/software-evaluation-guide 24. Jassbi, J. J., Ribeiro, R. A., & Varela, L. R. (2014). Dynamic MCDM with future knowledge for supplier selection. Journal of Decision Systems, 23(3), 232. 25. Ju, Y., & Wang, A. (2013). Extension of VIKOR method for multi-criteria group decision making problem with linguistic information. Applied Mathematical Modelling, 37(5), 3112. 26. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under risk. Econometrica, 47, 263-292. 27. Kahraman, C., Onar, S.C., and Oztaysi, B. 2015. Fuzzy multicriteria decision-making: A literature review. International Journal of Computational Intelligence Systems, 8(4), 637-666. 28. Kano, N., Nobuhiku, S., Fumio, T., & Shinichi, T. (1984). Attractive quality and must-be quality. Journal of the Japanese Society for Quality Control (in Japanese), 14(2), 39-48. 29. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E., Turskis, Z., & Antucheviciene, J. (2018). A dynamic fuzzy approach based on the EDAS method for multi-criteria subcontractor evaluation. Information, 9(3), 68. 30. Khamseh, A. A., & Mahmoodi, M. (2014). A new fuzzy TOPSIS-TODIM hybrid method for green supplier selection using fuzzy time function. Advances in Fuzzy Systems, 2014, 1. 31. Kim, K. H. S., Relkin, N. R., Lee, K., & Hirsch, J. (1997). Distinct cortical areas associated with native and second languages. Nature, 388, 171-174. 32. Kruskal, J. B. (1963). Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika, 29(1), 1-27. 33. Liang, H., Xiong, W., & Dong, Y. (2018). A prospect theory-based method for fusing the individual preference-approval structures in group decision making. Computers & Industrial Engineering, 117, 237-248. 34. Liao, H., Wu, D., Huang, Y., Ren, P., Xu, Z., & Verma, M. (2018). Green logistic provider selection with a hesitant fuzzy linguistic thermodynamic method integrating cumulative prospect theory and PROMETHEE. Sustainability, 10(4), 1291. 35. Lin, Y., Lee, P., & Ting, H. (2008). Dynamic multi-attribute decision making model with grey number evaluations. Expert Systems with Applications, 35(4), 1638-1644. 36. Liu, P., Jin, F., Zhang, X., Su, Y., & Wang, M. (2011). Research on the multi-attribute decision-making under risk with interval probability based on prospect theory and the uncertain linguistic variables. Knowledge-Based Systems, 24(4), 554-561. 37. Lourenzutti, R., & Krohling, R. A. (2014). The hellinger distance in multicriteria decision making: An illustration to the TOPSIS and TODIM methods. Expert Systems with Applications, 41(9), 4414-4421. 38. Mishra, S., Datta, S., & Mahapatra, S. S. (2013). Grey-based and fuzzy TOPSIS decision-making approach for agility evaluation of mass customization systems. Benchmarking: An International Journal, 20(4), 440–462. 39. Opricovic, S. (1998). Multicriteria optimization of civil engineering systems. Faculty of Civil Engineering, Belgrade. 40. Opricovic, S., & Tzeng, G. (2004). Compromise solution by MCDM methods: A comparative analysis of VIKOR and TOPSIS. European Journal of Operational Research, 156(2), 445. 41. Qin, J., Liu, X., & Pedrycz, W. (2015). An extended VIKOR method based on prospect theory for multiple attribute decision making under interval type-2 fuzzy environment. Knowledge-Based Systems, 86, 116-130. 42. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49. 43. Rezaei, J. (2016). Best-worst multi-criteria decision-making method: Some properties and a linear model. Omega, 64, 126. 44. Roy, B. (1968). Classement et choix en présence de points de vue multiples (la méthode ELECTRE). La Revue d'Informatique Et De Recherche Opérationelle (RIRO), (8), 57-75. 45. Saaty, T. L. (1980). The analytic hierarchy process. New York: McGraw-Hill. 46. Shih, H., Shyur, H., & Lee, E. S. (2007). An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45(7-8), 801. 47. Shyur, H., Yin, L., Shih, H., & Cheng, C. (2015). A multiple criteria decision making method based on relative value distances. Foundations of Computing and Decision Sciences, 40(4), 299-315. 48. Trope, Y., & Liberman, N. (2010). Construal-level theory of psychological distance. Psychological Review, 117(2), 440-463. 49. Tseng, M., Zhu, Q., Sarkis, J., & Chiu, A. S. F. (2018). Responsible consumption and production (RCP) in corporate decision-making models using soft computation. Industrial Management & Data Systems, 118(2), 322. 50. Tversky, A., & Kahneman, D. (1992). Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4), 297. 51. Wang , L., Zhang , Z., & Wang, Y. (2015). A prospect theory-based interval dynamic reference point method for emergency decision making. Expert Systems with Applications, 42(23), 9379-9388. 52. Wang, J. Q., Wu, J. T., Wang, J., Zhang, H. U., & Chen, X. H. (2016a). Multi-criteria decision-making methods based on the hausdorff distance of hesitant fuzzy linguistic numbers. Soft Computing, 20(4), 1621-1633. 53. Wang, J., Wang, J., & Zhang, H. (2016b). A likelihood-based TODIM approach based on multi-hesitant fuzzy linguistic information for evaluation in logistics outsourcing. Computers & Industrial Engineering, 99, 287. 54. Wang, S., & Liu, J. (2017). Extension of the TODIM method to intuitionistic linguistic multiple attribute decision making. Symmetry, 9(6), 95. 55. Wang, Y., & Luo, Y. (2009). On rank reversal in decision analysis. Mathematical and Computer Modelling, 49(5-6), 1221. 56. Wei, C., Ren, Z., & Rodríguez, R. M. (2015). A hesitant fuzzy linguistic TODIM method based on a score function. International Journal of Computational Intelligence Systems, 8(4), 701. 57. Yin, L., & Shyur, H. J. (2017). A robust group multiple attributes decision-making method based on risk preferences of the decision makers. International Journal of Applied Science and Engineering, 15(1), 33-46. 58. Ying, C., Li, Y., Chin, K., Yang, H., & Xu, J. (2018). A new product development concept selection approach based on cumulative prospect theory and hybrid-information MADM. Computers & Industrial Engineering, 122, 251. 59. Yoon, K., & Hwang, C. (1985). Manufacturing plant location analysis by multiple attribute decision making: Part I—single-plant strategy. International Journal of Production Research, 23(2), 345. 60. Yu, P., & Chen, Y. (2012). Dynamic multiple criteria decision making in changeable spaces: From habitual domains to innovation dynamics. Annals of Operations Research, 197(1), 201. 61. Yue, Z. 2011. An extended TOPSIS for determining weights of decision makers with interval numbers. Knowledge-Based Systems, 24: 146-153. 62. Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507. 63. Zhou, F., Wang, X., & Samvedi, A. (2018). Quality improvement pilot program selection based on dynamic hybrid MCDM approach. Industrial Management & Data Systems, 118(1), 144. 64. Zhu, J., Ma, Z., Wang, H., & Chen, Y. (2017). Risk decision-making method using interval numbers and its application based on the prospect value with multiple reference points. Information Sciences, 385-386, 415-437. |
論文全文使用權限 |
如有問題,歡迎洽詢!
圖書館數位資訊組 (02)2621-5656 轉 2487 或 來信