| Article ID: | iaor19982987 |
| Country: | United Kingdom |
| Volume: | 34 |
| Issue: | 12 |
| Start Page Number: | 89 |
| End Page Number: | 104 |
| Publication Date: | Dec 1997 |
| Journal: | Computers & Mathematics with Applications |
| Authors: | Liu Baoding |
| Keywords: | artificial intelligence, simulation, stochastic processes |
This paper provides a theoretical framework of dependent-chance programming, as well as dependent-chance multiobjective programming and dependent-chance goal programming which are new types of stochastic optimization. A stochastic simulation based genetic algorithm is also designed for solving dependent-chance programming models.