Title :
Ant colony optimization algorithm for expert assignment problem
Author :
Li, Na-Na ; Zhao, Zheng ; Gu, Jun-hua ; Liu, Bo-ying
Author_Institution :
Sch. of Comput. Sci. & Technol., Tianjin Univ., Tianjin
Abstract :
Expert assignment is chief and basic work of project review in project management. So it is significant to research how to solve expert assignment problem (EAP). In previous papers, we established the mathematical model of expert assignment problem, and proposed genetic algorithms (GAs) to solve EAP. Though it has been proven GAs are effective ways for EAP, they have disadvantages of slow convergence speed. In this paper, ant colony optimization (ACO), which has more powerful ability to solve complicated discrete optimization problem, is introduced to solve EAP. The simulation results show that ACO improves the convergence and generates solutions of better quality.
Keywords :
convergence; optimisation; project management; reviews; ant colony optimization algorithm; complicated discrete optimization problem; expert assignment problem; project management; project review; Ant colony optimization; Computer science; Convergence; Cybernetics; Genetic algorithms; Machine learning; Machine learning algorithms; Mathematical model; Project management; Resource management; Ant Colony Optimization; Expert Assignment Problem; Heuristic information; Pheromone;
Conference_Titel :
Machine Learning and Cybernetics, 2008 International Conference on
Conference_Location :
Kunming
Print_ISBN :
978-1-4244-2095-7
Electronic_ISBN :
978-1-4244-2096-4
DOI :
10.1109/ICMLC.2008.4620487
