A Possibilistic Framework for Identifying the Performance to be Improved in the Imprecise Context of Preliminary Design Stage

Diadie Sow (1) , Abdelhak Imoussaten (1) , Pierre Couturier (1) , Jacky Montmain (1)

1 LGI2P - Laboratoire de Génie Informatique et Ingénierie de Production

In a highly competitive and unstable environment, manufacturers must constantly improve their products to remain competitive and satisfy their customers while minimizing incurred costs and risk taking. At the early stages of (re-) engineering, performances forecasting of new product is complicated. Indeed, the impacts of any characteristic change on the product performance are not precisely known. Decision-makers must thus identify the performances to be improved while limiting the engineering efforts spent on innovative upgrades. Although some theoretical worth indexes have been proposed in the multiple criteria literature to estimate the expectable gains when improving changes are planned, they generally rely on non-realistic assumptions on the achievability of the expected improvements. Based on multi-criteria decision analysis techniques and uncertainty theory, this paper proposes an extension of the worth index concept when the likelihood of the expected improvements is not precisely known as it is the case at the preliminary stages of design activities.

Mots clés: Performance improvement; Imprecise Assessment; Multiple criteria Decision Analysis; Possibility Theory

Domaines: Intelligence artificielle [cs.AI]

Diadie Sow, Abdelhak Imoussaten, Pierre Couturier, Jacky Montmain. A Possibilistic Framework for Identifying the Performance to be Improved in the Imprecise Context of Preliminary Design Stage. CIVEMSA 2017, Jun 2017, Annecy, France. ⟨10.1109/CIVEMSA.2017.7995301⟩. ⟨hal-01556487⟩