Name
Title | ||
|---|---|---|
Hybridation of Bayesian networks and evolutionary algorithms for multi-objective optimization in an integrated product design and project management context |
Abstract | ||
|---|---|---|
A better integration of preliminary product design and project management processes at early steps of system design is nowadays a key industrial issue. Therefore, the aim is to make firms evolve from classical sequential approach (first product design the project design and management) to new integrated approaches. In this paper, a model for integrated product/project optimization is first proposed which allows taking into account simultaneously decisions coming from the product and project managers. However, the resulting model has an important underlying complexity, and a multi-objective optimization technique is required to provide managers with appropriate scenarios in a reasonable amount of time. The proposed approach is based on an original evolutionary algorithm called evolutionary algorithm oriented by knowledge (EAOK). This algorithm is based on the interaction between an adapted evolutionary algorithm and a model of knowledge (MoK) used for giving relevant orientations during the search process. The evolutionary operators of the EA are modified in order to take into account these orientations. The MoK is based on the Bayesian Network formalism and is built both from expert knowledge and from individuals generated by the EA. A learning process permits to update probabilities of the BN from a set of selected individuals. At each cycle of the EA, probabilities contained into the MoK are used to give some bias to the new evolutionary operators. This method ensures both a faster and effective optimization, but it also provides the decision maker with a graphic and interactive model of knowledge linked to the studied project. An experimental platform has been developed to experiment the algorithm and a large campaign of tests permits to compare different strategies as well as the benefits of this novel approach in comparison with a classical EA. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1016/j.engappai.2010.01.019 | Eng. Appl. of AI |
Keywords | Field | DocType |
project optimization,original evolutionary algorithm,learning,multi-objective optimization,product preliminary design,bayesian network,evolutionary operator,new evolutionary operator,integrated product design,evolutionary algorithm,project management,project management context,project manager,classical ea,project design,experience feedback,project management process,expert knowledge,multi objective optimization,decision maker,system design,product design | Evolutionary algorithm,Computer science,Systems design,Multi-objective optimization,Bayesian network,Artificial intelligence,Formalism (philosophy),Product design,Decision maker,Machine learning,Project management | Journal |
Volume | Issue | ISSN |
23 | 5 | Engineering Applications of Artificial Intelligence |
Citations | PageRank | References |
5 | 0.50 | 10 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Paul Pitiot | 1 | 16 | 3.80 |
| Thierry Coudert | 2 | 27 | 7.91 |
| Laurent Geneste | 3 | 107 | 14.82 |
| Claude Baron | 4 | 36 | 12.88 |