Nonlinear time/cost tradeoff models in project management
Computers and Industrial Engineering
Swarm intelligence
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
Evolutionary Optimization Versus Particle Swarm Optimization: Philosophy and Performance Differences
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Optimization using particle swarms with near neighbor interactions
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
An approach to multimodal biomedical image registration utilizing particle swarm optimization
IEEE Transactions on Evolutionary Computation
Two-level trust-based decision model for information assurance in a virtual organization
Decision Support Systems
Multiobjective optimization for manpower assignment in consulting engineering firms
Applied Soft Computing
Improving Decision Quality Through Preference Relaxation
Proceedings of the 2010 conference on Bridging the Socio-technical Gap in Decision Support Systems: Challenges for the Next Decade
A DSS to manage platelet production supply chain for regional blood centers
Decision Support Systems
Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS
Expert Systems with Applications: An International Journal
Optimization-based decision support system for crew scheduling in the cruise industry
Computers and Industrial Engineering
Multi-objective operating room scheduling considering desiderata of the surgical team
Decision Support Systems
Prototype system for pursuing firm's core capability
Information Systems Frontiers
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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.