Robust solutions using fuzzy chance constraints

Robust solutions using fuzzy chance constraints

0.00 Avg rating0 Votes
Article ID: iaor20081460
Country: United Kingdom
Volume: 38
Issue: 6
Start Page Number: 627
End Page Number: 645
Publication Date: Sep 2006
Journal: Engineering Optimization
Authors: , ,
Keywords: programming: probabilistic, fuzzy sets
Abstract:

It is well known that optimization problems for the decision-making process in real environments should consider uncertainty to attain robust solutions. Although this uncertainty has been usually modelled using probability theory, assuming a random origin, possibility theory has emerged as an alternative uncertainty model when statistical information is not available, or when imprecision and vagueness have to be considered. This article proposes two different criteria to obtain robust solutions for linear optimization problems when the objective function coefficients are modelled with possibility distributions. To do so, chance constrained programming is used, leading to equivalent crisp optimization problems, which can be solved by commercial optimization software. A simple case example is presented to illustrate the use of the proposed methodology.

Reviews

Required fields are marked *. Your email address will not be published.