Browse > Article

Mathematical Programming Models for Establishing Dominance with Hierarchically Structured Attribute Tree  

Han, Chang-Hee (한양대학교 디지털 경영학부)
Publication Information
Abstract
This paper deals with the multiple attribute decision making problem when a decision maker incompletely articulates his/her preferences about the attribute weight and alternative value. Furthermore, we consider the attribute tree which is structured hierarchically. Techniques for establishing dominance with linear partial information are proposed in a hierarchically structured attribute tree. The linear additive value function under certainty is used in the model. The incompletely specified information constructs a feasible region of linear constraints and therefore the pairwise dominance relationship between alternatives leads to intractable non-linear programming. Hence, we propose solution techniques to handle this difficulty. Also, to handle the tree structure, we break down the attribute tree into sub-trees. Due to there cursive structure of the solution technique, the optimization results from sub-trees can be utilized in computing the value interval on the topmost attribute. The value intervals computed by the proposed solution techniques can be used to establishing the pairwise dominance relation between alternatives. In this paper, pairwise dominance relation will be represented as strict dominance and weak dominance, which ware already defined in earlier researches.
Keywords
Citations & Related Records
연도 인용수 순위
  • Reference
1 Sarin, R.K., 1977, Screening of multiattribute alternatives. OMEGA, 5(4), 481-489
2 Bryson N. and Mobolurin A., 1995, An action learning evaluation procedure for multiple criteria decision making problems. European Journal of Operational Research, 96, 379-386
3 Keeney, R.L. and Raiffa, H., 1976, Decisions with Multiple Objectives: Preferences and Value Tradeoffs (Wiley: New York)
4 Min H.,1994, International supplier selection: A multi-attribute utility approach. International Journal of Physical Distribution and Logistics Management, 24(5), 24-33
5 Sage, A.P. and White, C.C.,1984, A Knowledge-based interactive system for planning and decision support. IEEE Transaction on Systems, Man and Cybernetics, 14, 35-47
6 Weber M., 1985, A method of multiattribute decision making with incomplete information. Management Science, 31(11), 1365-1371
7 Cook W.D. and Kress M.,1997, Multiple criteria modeling and ordinal data: Evaluation in terms of subsets of criteria. European Journal of Operational Research, 98, 602-609
8 Weber C. A. Current J. R. and Benton W.C., 1991, Vender selection criteria and methods. European Journal of Operational Research, 50, 2-18   DOI   ScienceOn
9 Saaty, T.L. and Vargas, L. G., 1987, Uncertainty and rank order in analytic hierarchy process. European journal of Operational Research, 32, 107-117
10 Hannan, E.L.,1981, Obtaining nondominated priority vectors for multiple objective decisionmaking problems with different combinations of cardinal and ordinal information. IEEE Transaction on Systems, Man and Cybernetics, 11, 538-543
11 Cook W.D. and Kress M.,1991, A multiple criteria decision model with ordinal preference data. European Journal of Operational Research, 54, 191-198
12 Kmietowicz Z.W. and Pearman A.D., 1984, Decision theory, linear partial information and statistical dominance. OMEGA, 12(4) 391-399
13 Kirkwood, C.W. and Sarin, R.K., 1985, Ranking with partial information: a method and an application. Operations Research, 33, 38-48
14 Kmietowicz Z.W. and Pearman A.D., 1982, Decision theory and strict ranking of probalilities. European Journal of Operational Research, 33, 38-48
15 Jacquet-Lagreze, E. and Siskos, J.,1982, Assessing a set of additive utility functions for multicriteria decision -rnaking, the UTA method. European journal of Operational Research, 10, 151-164
16 Arbel, A.,1989, Approximate articulation of preference and priority derivation. European journal of Operational Research, 43, 317-326
17 Kahneman, D., Slovic P., and Tversky A., 1982, judgment Under Uncertainty: Heuristics and Biases (Cambridge University Press: Cambridge, MA).
18 Salo, A.A. and Hmlnen R.P., 1995, Preference programming through approximate ratio comparison. European journal of Operational Research, 82, 458-475
19 White, C.C. and Sage, A.P., 1980, A multiple objective optimization-based approach to choicemaking, IEEE Transaction on Systems, Man and Cybernetics, 10, 315-326
20 Salo, A.A., and Hmlnen R.P., 1992, Preference assessment by imprecise ratio statement. Operations Research, 40(6), 1053-1061
21 Kim, S.H. and Han, C.H., 1999, An interactive procedure for multi-attribute group decision making with incomplete information. Computers & Operations Research, 26, 755-772
22 Fishburn, P.C.,1965, Analysis of decisions with incomplete knowledge of probabilities. Operations Research, 13, 217-237
23 Weber, M., 1987, Decision making with incomplete information. European journal of Operational Research, 28, 44-57   DOI   ScienceOn
24 Yoon, K. and Kim G., 1989, Multiple attribute decision making with imprecise information. IIE Transactions, 21(1), 21-26
25 Park K.S. and Kim S.H., 1997, Tools for interactive multiattribute decisionrnaking with incompletely identified information. European journal of Operational Research, 98, 111-123