Fuzzy systems evaluation: The inference error approach

A Framework for Applying CSFs to ERP Software Selection

The literature review studies on ERP system indicate two main areas of thrust: the study of critical success factors for the risk aversion in ERP implementation projects and the ERP system selection studies. However, ERP system selection is a tedious and a time-consuming activity. The critical success factors (CSFs) serve as vital input ingredients to the ERP selection Models. It is however notable that, that none of the CSFs listing propagated by the researchers find a straightforward application in the selection procedure. The paper bridges the gap between the two thrust areas by proposing a framework for applying the prioritized CSFs listed for direct utilization in the selection process. An exhaustive review on the ERP selection techniques reveals the focus on AHP and/or the Fuzzy Logic approaches to ERP selection problem. A new approach to ERP selection problem with the extensions of the Fuzzy-TOPSIS is subsequently introduced and is illustrated by a solved numerical example. Also, the computational simplicity of the extensions of Fuzzy TOPSIS is demonstrated.

A possibilistic framework for constraint-based metabolic flux analysis

BACKGROUND

Constraint-based models allow the calculation of the metabolic flux states that can be exhibited by cells, standing out as a powerful analytical tool, but they do not determine which of these are likely to be existing under given circumstances. Typical methods to perform these predictions are (a) flux balance analysis, which is based on the assumption that cell behaviour is optimal, and (b) metabolic flux analysis, which combines the model with experimental measurements.

RESULTS

Herein we discuss a possibilistic framework to perform metabolic flux estimations using a constraint-based model and a set of measurements. The methodology is able to handle inconsistencies, by considering sensors errors and model imprecision, to provide rich and reliable flux estimations. The methodology can be cast as linear programming problems, able to handle thousands of variables with efficiency, so it is suitable to deal with large-scale networks. Moreover, the possibilistic estimation does not attempt necessarily to predict the actual fluxes with precision, but rather to exploit the available data--even if those are scarce--to distinguish possible from impossible flux states in a gradual way.

CONCLUSION

We introduce a possibilistic framework for the estimation of metabolic fluxes, which is shown to be flexible, reliable, usable in scenarios lacking data and computationally efficient.