Abstract
With a discussion on the slow convergence in the traditional Genetic Algorithm for scheduling problems and the improvement of the crossover operator and mutation operator in the process of optimization, this paper proposes a new method to use the Self-adaptive GA to resolve the “Fixed Time Limit for a Project” problem of Multi-Resource Balanced Scheduling Optimization, with a goal of the balanced resources under the fixed time. Comparison of experimental results shows that the Self-adaptive GA has better evolution and self-adaptivity than the traditional Genetic Algorithm on the “Fixed Time Limit for a Project, Resources Balanced” problem of Multi-Resource Balanced Scheduling Optimization.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Burns, S., Liu, L., Feng, C.: The LP/IP Hybrid Method for Construction Time-cost Trade-of Analysis. Construction Management and Economics 24, P265–P267 (1996)
Li, H., Love, P.: Using Improved Genetic Algorithms to Facilitate Time-Cost Optimization. Journal of Construction Engineering and Management 123(3), P233–P237 (1997)
Yuancheng, Z., Jianxun, Q.: The Hybrid Genetic Algorithms on Resource Leveling of optimal Technology of Network Plan. China Management 12, P40–P44 (2004)
Li, X., Tong, H., Tan, W.: Network Planning Multi-objective Optimization Based on Genetic Algorithm. In: International Symposium on Intelligence Computation and Applications Progress, pp. 143–147 (2007)
Li, X., Tan, W., Kan, L.: Research of Resource Equilibrium Optimization Based on Genetic Algorithm. Computer Engineering and Design, 4447–4449 (2008)
Li, X.: The Study on Multi-objective Optimization of The Network Plan Based on Genetic Algorithm. Ph.D. Thesis of China University of Geoscience (2008)
Li, X., Chen, Q., Li, Y.: Impacton Genetic Algorithm of Different Parameters. In: The 3rd International Symposium on Intelligence Computation and Applications, pp. 479–488 (2008)
Xiang, L., Yanli, L., Li, Z.: The Comparative Research of Solving Problems of Equilibrium and Optimizing Multi-resources with GA and PSO. In: 2008 International Conference on Computational Intelligence and Security (2008)
Li, X., Tan, W., Tong, H.: A Resource Equilibrium Optimization Method Base on Improved Genetic Algorithm. China Artificial Intelligence Progress 2, P737–P743 (2007)
Lova, A., Tormos, P., Cervantes, M., Barber, F.: An efficient hybrid genetical gorithm for scheduling projects with resource constraints and mulitiple execution modes. Int. J. Production Economics, P302–P316 (2009)
Xiang, L., Yanli, L., Li, Z.: The Comparative Research of Solving Problems of Equilibrium and Optimizing Multi-resources with GA and PSO. In: 2008 International Conference on Computational Intelligence and Security (2008)
Xiaoping, W., Liming, C.: Genetic algorithms - theory, application and software implementation [M]. Xi’an Jiao tong University Press, Xi’an (2002)
Liao, R., Chen, Q., Mao, N.: Genetic algorithm for resource - constrained project scheduling. Computer Integrated Manufacturing Systems 10(7) (July 2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chen, P., Zhu, L., Li, X. (2010). Multi-resource Balanced Scheduling Optimization Based on Self-adaptive Genetic Algorithm. In: Cai, Z., Tong, H., Kang, Z., Liu, Y. (eds) Computational Intelligence and Intelligent Systems. ISICA 2010. Communications in Computer and Information Science, vol 107. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16388-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-642-16388-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16387-6
Online ISBN: 978-3-642-16388-3
eBook Packages: Computer ScienceComputer Science (R0)