Abstract
Stochastic programming is a well-known optimization problem in resource allocation, optimization decision etc. in this paper, by analyzing the essential characteristic of stochastic programming and the deficiencies of the existing methods, we propose the concept of synthesizing effect function for processing the objective function and constraints, and further we give an axiomatic system for synthesizing effect function. Finally, we establish a general solution model (denoted by BSE-SGM for short) based on synthesizing effect function for stochastic programming problem, and analyze the model through an example. All the results indicate that our method not only includes the existing methods for stochastic programming, but also effectively merge the decision preferences into the solution, so it can be widely used in many fields such as complicated system optimization and artificial intelligence etc.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Charnes, A., Cooper, W.W.: Management Models and Industrial Applications of Linear Programming. Johhn Wiley & Sons Inc., New York (1961)
Liu, B.: Dependent-chance programming: A class of stochastic programming. Computers & Mathematics with Applications 34(12), 89–104 (1997)
Song, J.K., Zhang, Z.X., Zhang, Y.: A Stochastic expected value model for measures program of oil field. Journal of Shandong University of Technology (Sci & Tech) 20(3), 9–12 (2006)
Sun, Y.J., Kang, L.Y., Shi, W.X., et al.: tudy on sizing of batteries for distributed power system utilizing chance constrained programming. Journal of System Simulation 17(1), 41–44 (2005)
Zhao, Y., Deng, H.Y., Li, J.H., et al.: Chance-constrained programming based on optimal allocation of harmonic filters on a distribution network. Proceedings of the CSEE 21(1), 12–17 (2001)
Iwamura, K., Liu, B.: A genetic algorithm for chance constrained programming. Journal of Information & Optimization Sciences 17(2), 40–47 (1996)
Zhao, R., Iwamura, K., Liu, B.: Chance constrained interger programming and stochastic simulation based genetic algorithms. Journal of Systems Science and Systems Engineering 7(1), 96–102 (1998)
Chen, J.M.: Solving order problems with genetic algorithms based on stochastic simulation. J. Chongqing Technol. Business Univ (Nat. Sci. Ed.) 22(2), 179–181 (2005)
Liu, B.: Dependent-chance goal programming and its genetic algorithm based approach. Mathematical and Computer Modelling 24(7), 43–52 (1996)
Holland, J.H.: Genetic algorithms and the optimal allocations of trials. SIAM J. of Computing 2, 8–105 (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, F., Liu, X., Jin, C. (2009). Study on Stochastic Programming Methods Based on Synthesizing Effect. In: Deng, H., Wang, L., Wang, F.L., Lei, J. (eds) Artificial Intelligence and Computational Intelligence. AICI 2009. Lecture Notes in Computer Science(), vol 5855. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05253-8_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-05253-8_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05252-1
Online ISBN: 978-3-642-05253-8
eBook Packages: Computer ScienceComputer Science (R0)