More Web Proxy on the site http://driver.im/

research-article

Multiple attribute decision making with completely unknown weights based on cumulative prospect theory and grey system theory

Authors:

Mengke WenAuthors Info & Claims

ICIIP '16: Proceedings of the 1st International Conference on Intelligent Information Processing

Article No.: 6, Pages 1 - 7

https://doi.org/10.1145/3028842.3028848

Published: 23 December 2016 Publication History

Abstract

The purposes of this paper are to study multiple attribute decision making problems by considering the behavioral characteristics of decision makers where the attribute weights are completely unknown. To determine the attribute weights, an optimization model based on prospect theory and the grey relation deep coefficient, from which the attribute weights can be determined, was established. The value function and decision weight function were used to calculate the overall prospect values of attributes for each alternative, and then rank the alternatives to select the most desirable one in accordance with the scores. In order to verify this method, it was used to study an illustrative example using, with the results demonstrating its feasibility and effectiveness. And it can be drawn the conclusion that the proposed method can be applied to decision making problems when the attribute weights are completely unknown while considering the decision maker's behavior at the same time.

References

[1]

A. Çelen. Comparative analysis of normalization procedures in topsis method: with an application to turkish deposit banking market. Informatica, 25(2):185--208, 2014.

[2]

Y. Kuo, T. Yang, G. W. Huang. The use of grey relational analysis in solving multiple attribute decision-making problems. Computers & Industrial Engineering, 55(1):80--93, 2008.

Digital Library

[3]

P. D. Liu, F. Jin, X. Zhang, Y. Su, M.H. Wang. 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, 2011.

Digital Library

[4]

K. Goda, H. P. Hong. Application of cumulative prospect theory: Implied seismic design preference. Structural Safety, 30(6):506--516, 2008.

[5]

D. Kahneman, A. Tversky. Prospect theory: an analysis of decision under risk. Econometrica, 63(3):263--291, 1979.

[6]

A. Tversky, D. Kahneman. Advances in prospect theory: cumulative representation of uncertainty. Journal of Risk and Uncertainty, 5(4):297--323, 1992.

[7]

Z. P. Fan, X. Zhang, F. D. Chen, Y. Liu. Multiple attribute decision making considering aspiration-levels: a method based on prospect theory. Computers & Industrial Engineering, 65(2):341--350, 2013.

Digital Library

[8]

J. Q. Wang, T. Sun, X. H. Chen, Multi-criteria fuzzy decision-making method based on prospect theory with incomplete information. Control and Decision, 24(8):1198--1202, 2009.

[9]

Z. P. Fan, F. D. Chen, X. Zhang. Method for hybrid multiple attribute decision making based oncumulative prospect theory. Journal of Systems Engineering, 27(3):296--301, 2012.

[10]

F. Li, Q. S. Jiang, B. Zhang. Optimization method for air defense operation channel based on prospect theory. Journal of Projectiles, Rockets, Missiles and Guidance, 34(1):188--191, 2014.

[11]

D. Lu, W. Z. Jin. Grey correlation evaluation method of public traffic vehicles scheduling optimization based on cumulative prospect theory. Journal of Wuhan University of Technology (Transportation Science & Engineering), 37(3):608--611, 2013.

[12]

X. W. Li, W. Wang, C. C. Xu, Z. B. Li, B. J. Wang. Multi-objective optimization of urban bus networkusing cumulative prospect theory. Journal of Systems Science and Complexity, 28(3):661--678, 2015.

[13]

Y. M. Wang, Y. Luo. Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making, Mathematical and Computer Modelling, 51(1--2):1--12, 2010.

Digital Library

[14]

J. Ma, Z. P. Fan, L. H. Huang. A subjective and objective integrated approach to determine attribute weights, European Journal of Operational Research, 112(2):397--404, 1999.

[15]

T. Y. Chen, C. H. Li. Determining objective weights with intuitionistic fuzzy entropy measures: A comparative analysis. Information Sciences, 180(21):4207--4222, 2010.

Digital Library

[16]

Y.K. Wang, D. Sheng, D. Wang, H.Q. Ma, J.C. Wu, F. Xu. Variable fuzzy set theory to assess water quality of the meiliang bay in taihu lake basin. Water Resources Manage, 28(3):867--880, 2014.

[17]

J. Q. Wang, L. Zhou. Grey-stochastic multi-criteria decision-making approach based on prospect theory. Systems Engineering-Theory & Practice, 30(9):1658--1664, 2010.

[18]

W. E. Yang, J. Q. Wang. Vague linguistic matrix game approach for multi-criteria decision making with uncertain weights. Journal of Intelligent & Fuzzy Systems, 25(2):315--324, 2013.

Digital Library

[19]

L. L. Gu, C. F. Cao, L. H. Cui. Weakness and improvement of Entropy Weight in Supplier Selection. Logistics Technology, 28(1):95--111, 2009.

[20]

Z. X. Wang, Y. G. Dang, L. L. Pei, Y. Y. Wang. Multi-index grey relational decision-making based on cumulative prospect theory. Control and Decision, 25(2):232--236, 2010.

[21]

D. E. Bell. Regret in decision making under uncertainty. Operations Research, 30(5): 961--981, 1982.

Digital Library

[22]

G. Loomes, R. Sugden. Regret theory: an alternative theory of rational choice under uncertainty. The Economic Journal, 92(368):805--824, 1982.

[23]

U. Schmidt, C. Starmer, R. Sugden. Third-generation prospect theory. Journal of Risk and Uncertainty, 36(3):203--223, 2008.

[24]

D. E. Bell. Disappointment in decision making under uncertainty. Operations Research, 33(1):1--27, 1985.

Digital Library

[25]

G. Loomes, R. Sugden. Disappointment and dynamic consistency in choice under uncertainty, Review of Economic Studies, 53(2):271--282, 1986.

[26]

M. Abdellaoui. Parameter-free elicitation of utilities and probability weighting functions. Management Science, 46(11): 1497--1512, 2000.

Digital Library

[27]

U. Schmidt, H. Zank. Risk aversion in cumulative prospect theory. Management Science, 54(1):208--216, 2008.

Digital Library

[28]

H. Bleichrodt, U. Schmidt, H. Zank. Additive utility in prospect theory. Management Science, 55(5):863--873, 2009.

Digital Library

[29]

J.L. Deng. Control problems of grey systems, Systems & Control Letters, 1(5):288--294, 1982.

[30]

S. F. Liu, T. B. Guo, Y. G. Dang. Grey system theory and its application, Science Press, Beijing, China, 2014.

[31]

J. J. Jin, C. M. Mi, W. X. Xu, Q. F. Wang, H. W. Wei. The maximum entropy empowerment model for evaluating index considering the expert evaluation information. Chinese Journal of Management Science, 20(2): 135--143, 2012.

[32]

Q. F. Wang, J. J. Jin, C. M. Mi, Z. G. Fang. Maximum entropy configuration model of objective index weight based on grey correlation deep coefficient. Control and Decision, 28(2):235--240, 2013.

[33]

J. Q. Wang. Research on decision making model of thedynamic multiple-attribute system for "Rewarding good And punishing bad". Systems Engineering and Electronics, 24 (3):39--41, 2002.

[34]

J. L. Young, T. Y. Liu, C. L. Hwang. TOPSIS for MODM {J}. European Journal of Operational Research, 6(3):486--500, 1994.

[35]

L. Zhou, D. Luo. A method for multiple attribute decision making without weight information. Academic Conference Proceedings of the Grey System Theory and Application, 262--268, 2006.

Cited By

Lu ELiu YZhao ZLiu YZhang C(2021)A Weighting Radius Prediction Iteration Optimization Algorithm Used in Photogrammetry for Rotary Body Structure of Port Hoisting MachineryIEEE Access10.1109/ACCESS.2021.31170799(140397-140412)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3117079

Index Terms

Multiple attribute decision making with completely unknown weights based on cumulative prospect theory and grey system theory
1. Applied computing
  1. Operations research
    1. Decision analysis
      1. Multi-criterion optimization and decision-making

Recommendations

Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making

This paper proposes a correlation coefficient (CC) and standard deviation (SD) integrated approach for determining the weights of attributes in multiple attribute decision making (MADM) and a global sensitivity analysis to the weights determined. The ...
Prospect theory based multi-criteria decision making method
CCDC'09: Proceedings of the 21st annual international conference on Chinese control and decision conference

With respect to risk decision making problems, a multi-criteria decision making method based on prospect theory is proposed. This method firstly describes multi-criteria decision making problem. Reference points of criteria are given and the outcomes ...
Research on the random multi-attribute decision-making methods with trapezoidal fuzzy probability based on prospect theory

With respect to the random multi-attribute decision making problems with trapezoidal fuzzy probability and unknown weight, a multi-attribute decision making method based on prospect theory is proposed. To begin with, the definition of trapezoidal fuzzy ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICIIP '16: Proceedings of the 1st International Conference on Intelligent Information Processing

December 2016

358 pages

ISBN:9781450347990

DOI:10.1145/3028842

Copyright © 2016 ACM.

© 2016 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

Sponsors

Jilin Institute of Chemical Technology: Jilin Institute of Chemical Technology, Jilin, China
Wanfang Data: Wanfang Data, Beijing, China
CNKI: CNKI, Beijing, China
Airiti: Airiti, Taiwan
Guilin: Guilin University of Technology, Guilin, China
Wuhan University of Technology: Wuhan University of Technology, Wuhan, China
Ain Shams University: Ain Shams University, Egypt
International Engineering and Technology Institute, Hong Kong: International Engineering and Technology Institute, Hong Kong

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 December 2016

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ICIIP 2016

Sponsor:

Jilin Institute of Chemical Technology
Wanfang Data
CNKI
Airiti
Guilin
Wuhan University of Technology
Ain Shams University
International Engineering and Technology Institute, Hong Kong

ICIIP 2016: 2016 International Conference on Intelligent Information Processing

December 23 - 25, 2016

Wuhan, China

Acceptance Rates

ICIIP '16 Paper Acceptance Rate 55 of 165 submissions, 33%;

Overall Acceptance Rate 87 of 367 submissions, 24%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
89
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Lu ELiu YZhao ZLiu YZhang C(2021)A Weighting Radius Prediction Iteration Optimization Algorithm Used in Photogrammetry for Rotary Body Structure of Port Hoisting MachineryIEEE Access10.1109/ACCESS.2021.31170799(140397-140412)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2021.3117079

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents