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Erschienen in: Neural Computing and Applications 8/2017

20.01.2016 | Original Article

Applying fuzzy multi-objective linear programming to a project management decision with nonlinear fuzzy membership functions

verfasst von: Ehsan Ehsani, Nima Kazemi, Ezutah Udoncy Olugu, Eric H. Grosse, Kurt Schwindl

Erschienen in: Neural Computing and Applications | Ausgabe 8/2017

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Abstract

This paper investigates a multi-objective project management problem where the goals of the decision maker are fuzzy. Prior research on this topic has considered linear membership functions to model uncertain project goals. Linear membership functions, however, are not much flexible to model uncertain information of projects in many situations, and therefore, fuzzy models with linear membership functions are not suitable to be applied in many practical situations. Hence, the purpose of this paper is to apply nonlinear membership functions in order to develop a better representation of fuzzy project planning in practice. This approach supports managers in examining different solution strategies and in planning projects more realistically. In doing so, a fuzzy mathematical project planning model with exponential fuzzy goals is developed first which takes account of (a) the time between events, (b) the crashing time for activities, and (c) the available budget. Following, a weighted max–min model is applied for solving the multi-objective project management problem. The performance of the developed solution procedure is compared with the literature that applied linear membership functions to this problem, and it is shown that the model developed in this paper outperforms the existing solution.

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Metadaten
Titel
Applying fuzzy multi-objective linear programming to a project management decision with nonlinear fuzzy membership functions
verfasst von
Ehsan Ehsani
Nima Kazemi
Ezutah Udoncy Olugu
Eric H. Grosse
Kurt Schwindl
Publikationsdatum
20.01.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 8/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-015-2160-0

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