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    Fuzzy Location Selection Techniques
    (Springer-Verlag Berlin, 2010) Kahraman, Cengiz; Cebi, Selcuk; Tuysuz, Fatih
    Facility location has an important role in terms of firms' strategic planning in the management science and operational research. One of the most common facility location problems is the location selection problem which is the most ancient but has been still a current problem for organizations. Plants, warehouses, retail outlets, terminals, storage yards, distribution centers etc. are typical facilities that must be located strategically since the location selection problem influences organizations' strategic competitive position in terms of operating cost, transportation cost, delivery speed performance, and organization's flexibility to compete in the marketplace. In this chapter, different approaches and techniques used in location selection problems are presented. In the scope of this chapter, we focus on the fuzzy multi-criteria decision making methods. Especially, in the literature, fuzzy AHP and fuzzy TOPSIS are presented since they are the most widely used ones. Furthermore, it is the first time a framework based on the fuzzy information axiom is proposed for facility location selection.
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    Fuzzy Real Option Value Integrated Fuzzy ANP Method for Location Selection Problems
    (IEEE, 2010) Tolga, A. Cagri; Tuysuz, Fatih; Kahraman, Cengiz
    Location selection decisions are irreversible for any type of enterprise. Making the right choice is vital. In this work, at the beginning, criteria for location selection in retail sector are determined. Then because of the dependency among criteria we choose ANP method as multi-criteria decision aid. The data are ambiguous and the nature of the problem is dynamic, hence fuzzy ANP is preferred. As the measurement technique of the financial criterion, we used the fuzzy real option value that calculates the risky side of the problem. Fuzzy trinomial lattice method without any future competitor is offered as the solution procedure for fuzzy real option value through its impact on efficiency and accuracy.
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    Manufacturing System Modeling Using Petri Nets
    (Springer-Verlag Berlin, 2010) Kahraman, Cengiz; Tueysuez, Fatih
    Petri nets which are used for modeling and analyzing complex systems that can be characterized as synchronous, parallel, simultaneous, distributed, resource sharing, nondeterministic and/or stochastic form a powerful modeling tool and are widely used today. In this study, fundamental concepts of Petri nets and their extensions are presented. Since the application area of Petri nets is wide, the subject is handled in the view of flexible manufacturing systems. A two stage modeling approach which combines the modeling power of stochastic Petri nets together with fuzzy sets is also presented. A numerical example is given to present how the proposed approach can be applied. We believe that this approach better represents both dimensions of uncertainty, stochastic variability and imprecision, in system modeling.
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    Modeling a flexible manufacturing cell using stochastic Petri nets with fuzzy parameters
    (Pergamon-Elsevier Science Ltd, 2010) Tuysuz, Fatih; Kahraman, Cengiz
    In this paper, an approach for modeling and analysis of time critical, dynamic and complex systems using stochastic Petri nets together with fuzzy sets is presented. The presented method consists of two stages. The first stage is same as the conventional stochastic Petri nets with the difference that the steady-state probabilities are obtained parametrically in terms of transition firing rates. In the second stage, the transition firing rates are described by triangular fuzzy numbers and then by applying fuzzy mathematics, the fuzzy steady-state probabilities are calculated. A numerical example for modeling and analysis of a flexible manufacturing cell is given to show the applicability of proposed method. The importance of the proposed approach is that it can take into consideration both dimensions of uncertainty in system modeling, stochastic variability and imprecision. (C) 2009 Elsevier Ltd. All rights reserved.