TY - JOUR
T1 - Solving discrete multicriteria decision problems based on logic-based decision support systems
AU - Li, Han-Lin
PY - 1987/1/1
Y1 - 1987/1/1
N2 - This study aims to formulate and solve discrete multicriteria decision making (D-MCDM) problems by utilizing artificial intelligent decision support systems. The major advantage of this approach is that data, functions, many D-MCDM methods, choice rules for methods, and the decision maker's preferences in D-MCDM can be integrated in a logical structure. Besides, based on the modulared D-MCDM method base and data base, the decision maker can flexibly choose suitable methods to solve decision problems. This paper first decomposes D-MCDM problems into alternative-attribute, attribute-criterion, criterion-method-recommendation and choice-method relationships, then transforms these relationships into 'Data', 'Function' and 'Rule' formats of logic-based programs. By following that the typical D-MCDM methods as Dominant method, Lexicographic method, Weighting method, ELECTRE method, TOPSIS method and Method with fuzzy concept are coded in a PROLOG-type language in a consistent format. Some choice rules for these D-MCDM methods are then discussed. Finally, the inference process and the man-machine dialog of this system are analyzed.
AB - This study aims to formulate and solve discrete multicriteria decision making (D-MCDM) problems by utilizing artificial intelligent decision support systems. The major advantage of this approach is that data, functions, many D-MCDM methods, choice rules for methods, and the decision maker's preferences in D-MCDM can be integrated in a logical structure. Besides, based on the modulared D-MCDM method base and data base, the decision maker can flexibly choose suitable methods to solve decision problems. This paper first decomposes D-MCDM problems into alternative-attribute, attribute-criterion, criterion-method-recommendation and choice-method relationships, then transforms these relationships into 'Data', 'Function' and 'Rule' formats of logic-based programs. By following that the typical D-MCDM methods as Dominant method, Lexicographic method, Weighting method, ELECTRE method, TOPSIS method and Method with fuzzy concept are coded in a PROLOG-type language in a consistent format. Some choice rules for these D-MCDM methods are then discussed. Finally, the inference process and the man-machine dialog of this system are analyzed.
KW - AI
KW - Connection Graph
KW - D-MCDM
KW - DSS
KW - ELECTRE
KW - PROLOG
KW - Production Systems
KW - TOPSIS
UR - http://www.scopus.com/inward/record.url?scp=38249036067&partnerID=8YFLogxK
U2 - 10.1016/0167-9236(87)90070-4
DO - 10.1016/0167-9236(87)90070-4
M3 - Article
AN - SCOPUS:38249036067
SN - 0167-9236
VL - 3
SP - 101
EP - 119
JO - Decision Support Systems
JF - Decision Support Systems
IS - 2
ER -