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MULTIPLE‐CRITERIA DECISION MAKING INCLUDING QUALITATIVE FACTORS: THE POST‐MODEL ANALYSIS APPROACH
Author(s) -
Lee Jae Kyu,
Hurst E. Gerald
Publication year - 1988
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1988.tb00271.x
Subject(s) - computer science , qualitative analysis , quantitative analysis (chemistry) , qualitative research , qualitative property , decision maker , process (computing) , management science , qualitative reasoning , operations research , decision support system , data mining , artificial intelligence , machine learning , mathematics , engineering , social science , chemistry , chromatography , sociology , operating system
As a method of solving multiple‐criteria decision making problems with a single quantitative objective and multiple qualitative objectives, the post‐model analysis (PMA) approach is proposed. The essence of PMA is to support the trade‐offs between a quantitative objective and multiple qualitative objectives so that the decision maker can find a perceived most preferred nondominated solution. To this end, the optimal solution of a quantitative model is found first, without regard for qualitative factors. The solution is then evaluated in terms of qualitative objectives. When the initial quantitatively optimal solution is adjusted to allow improvement of qualitative goals, opportunity costs of achieving qualitative goals are incurred. In this process, an expert system and/or graphical display can be used. PMA therefore provides a way to incorporate quantitative models into knowledge‐based expert systems.