基于改进向量相似度的区间数动态多指标决策模型及应用
|
摘要
针对决策信息为区间数的不确定性动态决策问题,在属性权重和时间权重未知的情况下,基于改进向量相似度的方法,构建一种兼顾决策信息和决策偏好的动态多指标决策模型.利用区间型决策信息的相对相似性和属性重要度,构造相对相似度最小规划模型以确定指标权重;在综合考虑决策信息时间价值、决策者偏好的基础上,构建极大熵模型以确定时间权重;结合向量相似度计算存在的缺陷,提出一种基于向量投影思想的向量综合相似度测度方法,从而建立不确性动态决策模型,并通过实例分析检验该模型的合理性和有效性.
For the uncertain dynamic decision-making problem that the decision information is interval number, under the condition that the attribute weights and time weights are unknown, a dynamic multiple attribute decision-making model considering both decision information and decision preference based on improved vector similarity is proposed.Based on the relative similarity relations of the interval decision information and the importance of attribute, the relative similarity degree minimum programming model is constructed to determine the index weights. Taking the time value of decision information and the decision maker's preference into account, the maximum entropy model is established to determine the time weights. Combining the shortcoming of vector similarity calculation, a measure method of vector similarity based on vector projection idea is given, so that the uncertain dynamic decision-making model is constrated.Finally, an example is given to illustrate the rationality and effectiveness of the proposed model.
For the uncertain dynamic decision-making problem that the decision information is interval number, under the condition that the attribute weights and time weights are unknown, a dynamic multiple attribute decision-making model considering both decision information and decision preference based on improved vector similarity is proposed.Based on the relative similarity relations of the interval decision information and the importance of attribute, the relative similarity degree minimum programming model is constructed to determine the index weights. Taking the time value of decision information and the decision maker's preference into account, the maximum entropy model is established to determine the time weights. Combining the shortcoming of vector similarity calculation, a measure method of vector similarity based on vector projection idea is given, so that the uncertain dynamic decision-making model is constrated.Finally, an example is given to illustrate the rationality and effectiveness of the proposed model.
引文
[1] Chen Y, Li B. Dynamic multi-attribute decision making model based on triangular intuitionistic fuzzy numbers[J].Scientia Iranica, 2011, 18(2):268-274.
[2] Sun B, Ma W. An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application[J]. Omega, 2015, 51(1):83-92.
[3] Wei G. Grey relational analysis model for dynamic hybrid multiple attribute decision making[J]. Knowledge-Based Systems, 2011, 24(5):672-679.
[4] Jin H P, Cho H J, Kwun Y C. Extension of the VIKOR method to dynamic intuitionistic fuzzy multiple attribute decision making[J]. Computers&Mathematics with Applications, 2013, 65(4):731-744.
[5] Yi P T, You H Y, Guo Y J, et al. Multi-phase evaluation method of information aggregation based on phases gain inspiriting[J]. Systems Engineering, 2015, 33(12):126-131.
[6] Sun B, Xu X F. A dynamic stochastic decision-making method based on discrete time sequences[J]. KnowledgeBased Systems, 2016, 105(C):23-28.
[7] Yang Z, Li J, Huang L, et al. Developing dynamic intuitionistic normal fuzzy aggregation operators for multi-attribute decision-making with time sequence preference[J]. Int J of Expert Systems with Applications,2017, 82(C):344-356.
[8] Liao Z G, Zhan M, Xu J P. Dynamic comprehensive evaluation and speed characteristics analysis of a matter-element method[J]. Chinese J of Management Science, 2015, 23(11):119-127.
[9] Hao J J, Zhu J J, Liu S F. Multi-stage stochastic multi-criteria decision making method based on prospect theory[J]. Chinese J of Management Science, 2015,23(1):73-81.
[10] Zhu J J, Ma Z Z, Wang H H, et al. Risk decision making method using interval numbers and its application based on the prospect value with multiple reference points[J].Information Sciences, 2017, 385/386:415-437.
[11] Wang X, Dang Y G. Dynamic multi-attribute decision making methods with three-parameter interval grey number[J]. Control and Decision, 2015, 30(9):1623-1629.
[12] Chen W, Li J Q, Yang Z L. A dynamic grey target multi-attribute decision-making method based on error propagation and membership degree[J]. J of Systems&Management, 2017, 26(6):1034-1042.
[13] Huang Z L, Luo J. Study on relative similarity elation method for uncertain multi-attribute decision making problem[J]. System Engineering—Theory&Pratice,2015, 35(11):2917-2924.
[14] Chen X, Huang Z L, Luo J. Approach for triangular fuzzy number based uncertain multi-attribute decision making based on relative similarity degree relation[J]. Control and Decision, 2016, 31(12):2232-2240.
[15] Peng Z L, Zhang Q, Li Z R, et al. Improved maximizing deviation decision-making model and its application in multi-mission planning of near space system[J]. System Engineering—Theory&Pratice, 2014, 34(2):421-427.
[16] Wang Y M. Using the method of maximizing deviations to make decision for multiindicies[J]. J of Systems Engineering and Electronics, 1998, 20(7):24-26.
[17] Guo Y J, Yao Y, Yi P T. A method and application of dynamic comprehensive evaluation[J].System Engineering—Theory&Pratice, 2007, 27(10):154-158.
[18] Qian W Y, Dang Y G, Liu S F. Model of dynamic multiple index decision based on driving principle of difference and mean correlation degree[J]. J of Systems Engineering and Electronics, 2012, 34(2):337-341.
[19] Zeng J, Wang J, Guo J Y. Local multi-model method based on similarity of vector[J]. Application Research of Computers, 2012, 29(5):1631-1633.
[20] Ye J. Cosine similarity measures for intuitionistic fuzzy sets and their applications[J]. Mathematical&Computer Modelling, 2011, 53(1):91-97.
[21] Zeng B, Li C, Liu S F. A novel grey target decisionmaking model based on cobweb area and its application for choosing the software development pattern[J]. Scienta Iranica, 2016, 23(1):361-373.
[2] Sun B, Ma W. An approach to consensus measurement of linguistic preference relations in multi-attribute group decision making and application[J]. Omega, 2015, 51(1):83-92.
[3] Wei G. Grey relational analysis model for dynamic hybrid multiple attribute decision making[J]. Knowledge-Based Systems, 2011, 24(5):672-679.
[4] Jin H P, Cho H J, Kwun Y C. Extension of the VIKOR method to dynamic intuitionistic fuzzy multiple attribute decision making[J]. Computers&Mathematics with Applications, 2013, 65(4):731-744.
[5] Yi P T, You H Y, Guo Y J, et al. Multi-phase evaluation method of information aggregation based on phases gain inspiriting[J]. Systems Engineering, 2015, 33(12):126-131.
[6] Sun B, Xu X F. A dynamic stochastic decision-making method based on discrete time sequences[J]. KnowledgeBased Systems, 2016, 105(C):23-28.
[7] Yang Z, Li J, Huang L, et al. Developing dynamic intuitionistic normal fuzzy aggregation operators for multi-attribute decision-making with time sequence preference[J]. Int J of Expert Systems with Applications,2017, 82(C):344-356.
[8] Liao Z G, Zhan M, Xu J P. Dynamic comprehensive evaluation and speed characteristics analysis of a matter-element method[J]. Chinese J of Management Science, 2015, 23(11):119-127.
[9] Hao J J, Zhu J J, Liu S F. Multi-stage stochastic multi-criteria decision making method based on prospect theory[J]. Chinese J of Management Science, 2015,23(1):73-81.
[10] Zhu J J, Ma Z Z, Wang H H, et al. Risk decision making method using interval numbers and its application based on the prospect value with multiple reference points[J].Information Sciences, 2017, 385/386:415-437.
[11] Wang X, Dang Y G. Dynamic multi-attribute decision making methods with three-parameter interval grey number[J]. Control and Decision, 2015, 30(9):1623-1629.
[12] Chen W, Li J Q, Yang Z L. A dynamic grey target multi-attribute decision-making method based on error propagation and membership degree[J]. J of Systems&Management, 2017, 26(6):1034-1042.
[13] Huang Z L, Luo J. Study on relative similarity elation method for uncertain multi-attribute decision making problem[J]. System Engineering—Theory&Pratice,2015, 35(11):2917-2924.
[14] Chen X, Huang Z L, Luo J. Approach for triangular fuzzy number based uncertain multi-attribute decision making based on relative similarity degree relation[J]. Control and Decision, 2016, 31(12):2232-2240.
[15] Peng Z L, Zhang Q, Li Z R, et al. Improved maximizing deviation decision-making model and its application in multi-mission planning of near space system[J]. System Engineering—Theory&Pratice, 2014, 34(2):421-427.
[16] Wang Y M. Using the method of maximizing deviations to make decision for multiindicies[J]. J of Systems Engineering and Electronics, 1998, 20(7):24-26.
[17] Guo Y J, Yao Y, Yi P T. A method and application of dynamic comprehensive evaluation[J].System Engineering—Theory&Pratice, 2007, 27(10):154-158.
[18] Qian W Y, Dang Y G, Liu S F. Model of dynamic multiple index decision based on driving principle of difference and mean correlation degree[J]. J of Systems Engineering and Electronics, 2012, 34(2):337-341.
[19] Zeng J, Wang J, Guo J Y. Local multi-model method based on similarity of vector[J]. Application Research of Computers, 2012, 29(5):1631-1633.
[20] Ye J. Cosine similarity measures for intuitionistic fuzzy sets and their applications[J]. Mathematical&Computer Modelling, 2011, 53(1):91-97.
[21] Zeng B, Li C, Liu S F. A novel grey target decisionmaking model based on cobweb area and its application for choosing the software development pattern[J]. Scienta Iranica, 2016, 23(1):361-373.