A New MCDM Approach to Solve Public Sector Planning Problems

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