Scenario selection optimization in system engineering projects under uncertainty: a multi-objective ant colony method based on a learning mechanism
| Autor: | Majda Lachhab, Cédrick Béler, Thierry Coudert |
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| Přispěvatelé: | Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Risk
Engineering 021103 operations research Total cost business.industry Ant colony optimization algorithms Algorithme et structure de données 0211 other engineering and technologies Uncertainty Performance et fiabilité 02 engineering and technology Ant colony Multi-objective optimization Ant Colony Optimization Pareto-optimality 0202 electrical engineering electronic engineering information engineering Systems engineering Graph (abstract data type) Learning 020201 artificial intelligence & image processing business Risk management Global risk System engineering project |
| Popis: | This paper presents a multi-objective Ant Colony Optimization (MOACO) algorithm based on a learning mechanism (named MOACO-L) for the optimization of project scenario selection under uncertainty in a system engineering (SE) process. The objectives to minimize are the total cost of the project, its total duration and the global risk. Risk is considered as an uncertainty about task costs and task durations in the project graph. The learning mechanism aims to improve the MOACO algorithm for the selection of optimal project scenarios in aSE project by considering the uncertainties on the project objectives. The MOACO-L algorithm is then developed by taking into account ants’ past experiences. The learning mechanism allows a better exploration of the search space and an improvement of the MOACO algorithm performance. To validate our approach, some experimental results are presented. |
| Databáze: | OpenAIRE |
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