Fuzzy set theory—and its applications (3rd ed.)
Fuzzy set theory—and its applications (3rd ed.)
Fuzzy Sets and Systems: Theory and Applications
Fuzzy Sets and Systems: Theory and Applications
Linear programming under randomness and fuzziness
Fuzzy Sets and Systems
The use of parametric programming in fuzzy linear programming
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
An input relaxation measure of efficiency in fuzzy data envelopment analysis (FDEA)
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
An evaluation of airline service quality using the fuzzy weighted SERVQUAL method
Applied Soft Computing
Expert Systems with Applications: An International Journal
A geometrical approach for fuzzy DEA frontiers using different T norms
WSEAS TRANSACTIONS on SYSTEMS
Fuzzy patterns in multi-level of satisfaction for MCDM model using modified smooth S-curve MF
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
A fuzzy method for measuring efficiency under fuzzy environment
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Modeling of a manufacturing cell design problem with fuzzy multi-objective parametric programming
Mathematical and Computer Modelling: An International Journal
An evolutionary approach to decision-making, with an application to media selection
Mathematical and Computer Modelling: An International Journal
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In this paper we discuss how to deal with decision problems that are described with LP models and formulated with elements of imprecision and uncertainty. More precisely, we will study LP models in which the parameters are not fully known but only with some degree of precision. Even with incomplete information the model builder (or model user) is normally able to give a realistic interval for the parameters of an LP model. For the constraint vector this is combined with some wishes or some leeway on the constraints. Even with ambiquity in the objective function, there is normally some preference ordering to be found among alternative ways of action. We will demonstrate that these modelling complications can be handled with the help of some results developed in the theory of fuzzy sets. After an overview of some central contributions to fuzzy linear programming, we will develop an LP model in which the parameters are not fully known, only with some degree of precision, and show that the model can be parametrised in such a way that the optimal solution becomes a function of the degree of precision. The fuzzy LP model derived in this way appears to be fairly easy to handle computationally, which is demonstrated with a numerical example.