Title :
A New MCDM Approach to Solve Public Sector Planning Problems
Author :
Kaplan, Pervin Ozge ; Ranjithan, S. Ranji
Author_Institution :
Dept. of Civil, Constr. & Environ. Eng., North Carolina State Univ., Raleigh, NC
Abstract :
An interactive method is developed to aid decision makers in public sector planning and management. The method integrates machine learning algorithms along with multiobjective optimization and modeling-to-generate-alternatives procedures into decision analysis. The implicit preferences of the decision maker are elicited through screening of several alternatives. The alternatives are selected from Pareto front and near Pareto front regions that are identified first in the procedure. The decision maker´s selections are input to the machine learning algorithms to generate decision rules, which are then incorporated into the analysis to generate more alternatives satisfying the decision rules. The method is illustrated using a municipal solid waste management planning problem
Keywords :
Pareto optimisation; data mining; decision making; decision support systems; learning (artificial intelligence); operations research; planning; public administration; Pareto front; association rule mining; interactive multiple criteria decision making; machine learning algorithms; modeling-to-generate-alternatives procedures; multiobjective optimization; municipal solid waste management planning; preference elicitation methods; public sector planning; Algorithm design and analysis; Application software; Association rules; Computational intelligence; Data mining; Decision making; Decision trees; Delta modulation; Machine learning; Machine learning algorithms; MCDM; association rule mining; interactive methods; preference elicitation methods;
Conference_Titel :
Computational Intelligence in Multicriteria Decision Making, IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0702-8
DOI :
10.1109/MCDM.2007.369430
