Nonlinear goal programming theory and practice: a survey
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Simulation and the Monte Carlo Method
Simulation and the Monte Carlo Method
Stochastic programming models for vehicle routing problems
Focus on computational neurobiology
Special classes of mathematical programming models with fuzzy random variables
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Study on Stochastic Programming Methods Based on Synthesizing Effect
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
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This paper develops a general formulation of dependent-chance goal programming (DCGP) which is an extension of stochastic goal programming in a complex stochastic system, and gives an example of water allocation and supply to show the application of DCGP. A genetic algorithm based approach is also presented to solve such a model. DCGP is available to the systems in which there are multiple stochastic inputs and multiple outputs with their own reliability levels. The characteristic of DCGP is that the chances of some probabilistic goals are Dependent, i.e., the goals cannot be considered in isolation or converted to their deterministic equivalents. Finally, Monte Carlo simulation is also discussed for calculating the chance functions in complex stochastic constraints.