Using interval weights in MADM problems

Abstract The choice of weights vectors in multiple attribute decision making (MADM) problems has generated an important literature, and a large number of methods have been proposed for this task. In some situations the decision maker (DM) may not be willing or able to provide exact values of the weights, but this difficulty can be avoided by allowing the DM to give some variability in the weights. In this paper we propose a model where the weights are not fixed, but can take any value from certain intervals, so the score of each alternative is the maximum value that the weighted mean can reach when the weights belong to those intervals. We provide a closed-form expression for the scores achieved by the alternatives so that they can be ranked them without solving the proposed model, and apply this new method to an MADM problem taken from the literature.

[1]  Evangelos Triantaphyllou,et al.  Multi-criteria Decision Making Methods: A Comparative Study , 2000 .

[2]  Valerie Belton,et al.  On a short-coming of Saaty's method of analytic hierarchies , 1983 .

[3]  E. Roszkowska Rank Ordering Criteria Weighting Methods – a Comparative Overview , 2013 .

[4]  Kyung S. Park,et al.  Tools for interactive multiattribute decisionmaking with incompletely identified information , 1997 .

[5]  N. Malys,et al.  Comparative analysis of MCDM methods for the assessment of sustainable housing affordability , 2016 .

[6]  David J. Weiss,et al.  SMARTS and SMARTER: Improved Simple Methods for Multiattribute Utility Measurement , 2008 .

[7]  Raimo P. Hämäläinen,et al.  Decision Support by Interval SMART/SWING - Incorporating Imprecision in the SMART and SWING Methods , 2005, Decis. Sci..

[8]  J. Siskos Assessing a set of additive utility functions for multicriteria decision-making , 1982 .

[9]  Behnam Malakooti,et al.  Ranking and screening multiple criteria alternatives with partial information and use of ordinal and cardinal strength of preferences , 2000, IEEE Trans. Syst. Man Cybern. Part A.

[10]  Bernard Roy,et al.  Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure , 2002, Eur. J. Oper. Res..

[11]  Martin Weber Decision Making with Incomplete Information , 1987 .

[12]  H. Keselman,et al.  Modern robust data analysis methods: measures of central tendency. , 2003, Psychological methods.

[13]  Bonifacio Llamazares,et al.  Ranking Candidates Through Convex Sequences of Variable Weights , 2016 .

[14]  Ahti Salo,et al.  Rank inclusion in criteria hierarchies , 2005, Eur. J. Oper. Res..

[15]  Rand R. Wilcox,et al.  Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction , 2011 .

[16]  Bonifacio Llamazares,et al.  Preference aggregation and DEA: An analysis of the methods proposed to discriminate efficient candidates , 2009, Eur. J. Oper. Res..

[17]  Love Ekenberg,et al.  Rank Ordering Methods for Multi-criteria Decisions , 2014, GDN.

[18]  Ana Paula Cabral Seixas Costa,et al.  A new method for elicitation of criteria weights in additive models: Flexible and interactive tradeoff , 2016, Eur. J. Oper. Res..

[19]  Vic Barnett,et al.  Outliers in Statistical Data , 1980 .

[20]  Ami Arbel,et al.  Approximate articulation of preference and priority derivation , 1989 .

[21]  Raimo P. Hämäläinen,et al.  Preference Assessment by Imprecise Ratio Statements , 1992, Oper. Res..

[22]  F. H. Barron,et al.  Selecting a best multiattribute alternative with partial information about attribute weights , 1992 .

[23]  Ali Jahan,et al.  A state-of-the-art survey on the influence of normalization techniques in ranking: Improving the materials selection process in engineering design , 2015 .

[24]  Chung-Hsing Yeh,et al.  Inter-company comparison using modified TOPSIS with objective weights , 2000, Comput. Oper. Res..

[25]  W. Cook,et al.  A data envelopment model for aggregating preference rankings , 1990 .

[26]  Ching-Lai Hwang,et al.  Fuzzy Multiple Attribute Decision Making - Methods and Applications , 1992, Lecture Notes in Economics and Mathematical Systems.

[27]  Emma Mulliner,et al.  What Attributes Determine Housing Affordability , 2012 .

[28]  Ying Luo,et al.  Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making , 2010, Math. Comput. Model..

[29]  Yucheng Dong,et al.  RANKING RANGE BASED APPROACH TO MADM UNDER INCOMPLETE CONTEXT AND ITS APPLICATION IN VENTURE INVESTMENT EVALUATION , 2019, Technological and Economic Development of Economy.

[30]  Gao-Feng Yu,et al.  A Compromise-Typed Variable Weight Decision Method for Hybrid Multiattribute Decision Making , 2019, IEEE Transactions on Fuzzy Systems.

[31]  Christophe Ley,et al.  Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median , 2013 .

[32]  John W. Tukey,et al.  Exploratory Data Analysis. , 1979 .

[33]  Gregory E. Kersten,et al.  Group Decision and Negotiation. A Process-Oriented View , 2014, Lecture Notes in Business Information Processing.

[34]  S. Seo A Review and Comparison of Methods for Detecting Outliers in Univariate Data Sets , 2006 .

[35]  E. Zavadskas,et al.  Multiple criteria decision making (MCDM) methods in economics: an overview , 2011 .

[36]  R. Hämäläinen,et al.  Preference programming through approximate ratio comparisons , 1995 .

[37]  Stefano Tarantola,et al.  Handbook on Constructing Composite Indicators: Methodology and User Guide , 2005 .

[38]  Charu C. Aggarwal,et al.  Outlier Analysis , 2013, Springer New York.

[39]  Teri A. Crosby,et al.  How to Detect and Handle Outliers , 1993 .

[40]  Bonifacio Llamazares Aggregating preference rankings using an optimistic-pessimistic approach: Closed-form expressions , 2017, Comput. Ind. Eng..

[41]  Enrique Herrera-Viedma,et al.  Multiple Attribute Strategic Weight Manipulation With Minimum Cost in a Group Decision Making Context With Interval Attribute Weights Information , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[42]  Bonifacio Llamazares,et al.  Aggregating preferences rankings with variable weights , 2013, Eur. J. Oper. Res..

[43]  Chiang Kao,et al.  Weight determination for consistently ranking alternatives in multiple criteria decision analysis , 2010 .

[44]  J. Rezaei Best-worst multi-criteria decision-making method: Some properties and a linear model , 2016 .

[45]  Chao Fu,et al.  Fair framework for multiple criteria decision making , 2018, Comput. Ind. Eng..

[46]  José Rui Figueira,et al.  A Sorting Model for Group Decision Making: A Case Study of Water Losses in Brazil , 2012 .

[47]  S. M. Muzakkir,et al.  A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals , 2018, Comput. Ind. Eng..

[48]  Matthias Ehrgott,et al.  Multiple criteria decision analysis: state of the art surveys , 2005 .

[49]  Chao Fu,et al.  A method of determining attribute weights in evidential reasoning approach based on incompatibility among attributes , 2015, Comput. Ind. Eng..