Utility-based decision support system for schedule optimization

The present study quantifies the impact of individual preferences of decision makers on schedule optimization and proposes a decision support system (DSS) to account for the diversity in the time–cost tradeoff analysis. The proposed DSS defines the multiattribute utility function based on subjective assessment of one-dimensional utility functions and scaling factors of time and cost. The multiattribute utility function is subsequently optimized by aid of a new particle swarm optimization algorithm. The application of the proposed DSS is demonstrated through case studies. It has been verified, both statistically and subjectively, that the proposed DSS is effective, efficient, and robust. It has also been shown that the proposed DSS outperforms genetic algorithms. The formulation of the proposed DSS is of practical value because it considers, in addition to direct and indirect costs, the amount of liquidated damages and bonus for early completion. Moreover, the formulation has no restriction on the forms of activity time–cost functions and therefore provides the most flexibility.