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    Alpha-cut based fuzzy cognitive maps with applications in decision-making
    (Pergamon-Elsevier Science Ltd, 2021) Baykasoğlu, Adil; Gölcük, İlker
    Fuzzy cognitive maps (FCMs) are widely used fuzzy modeling tools for handling causal interdependencies in complex systems. In FCMs, system variables (concepts) and degree of interrelationships are quantified by means of fuzzy sets. However, these fuzzy sets are defuzzified and fuzzy singletons are processed in the inference algorithm. Defuzzification of fuzzy numbers before the FCM inference implies information loss which is an undesired situation. Moreover, the resultant concept values are fuzzy singletons that these crisp numbers do not provide any information about the range of possible outcomes. There is a research gap in the literature regarding fuzzy number representation in FCMs that the both of the inputs and outputs of FCMs being fuzzy sets. This study proposes alpha-cut based computational procedures for simulating FCMs in which concepts and degree of relationships are represented via fuzzy numbers. The proposed model is tested on well-known problems adopted from the literature by using type-1 fuzzy numbers and the results are compared with the extension principle-based approach. Moreover, the proposed model is extended to interval type-2 (IT2) fuzzy sets and computational details regarding IT2 FCMs are given. Because relative importance of criteria is usually modeled via fuzzy sets in multiple-attribute decision making problems, a new FCM-based objective weighting method is proposed in order to demonstrate the usefulness of fuzzy number representation in FCMs. The proposed model is implemented in the real-life third-party logistics service provider selection problem.
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    Analysis of key performance indicators in a manufacturing plant via fuzzy cognitive maps
    (IEEE, 2019) Baykasoğlu, Adil; Atalay, Zehra Nur; Gölcük, İlker
    Companies have to follow key performance indicators (KPIs) for their key objectives, such as increasing of productivity and profit, and reducing cost. Performance measurement and management is also important in this context. The interaction between key performance indicators is often ignored and these interactions are expressed by the subjective evaluations of decision makers. In this study, fuzzy cognitive map (FCM) was trained based on historical data in a production system and connection matrix was formed/analyzed based on the data. KPIs and their values are taken from the company where the present application is performed.
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    A FCM-Based systematic approach for building and analyzing problem solving networks in open innovation
    (Old City Publishing Inc, 2020) Felekoğlu, Burcu; Baykasoğlu, Adil; Gölcük, İlker
    The purpose of this paper is to propose a fuzzy cognitive mapping (FCM)-based systematic approach for building and analyzing problem solving networks in open innovation and illustrate the use of this approach with an application in a company operating in prepress preparation sector. First, the proposed approach is explained step by step. Then, for illustrating the use of this approach, case study method was used and data was collected in a company using a visual form developed for this study and analysis of data was performed using social network analysis (SNA) and FCM methods. The results of the study show that by using the proposed approach managers can better understand, sustain, and develop knowledge management practices in open innovation networks in their companies by considering dynamic contextual factors on their relationships. Visual representation of the resulting network helps managers to better understand their internal and external relationships and identify the most valuable network participants. To the best of our knowledge, this is the first study using SNA and FCM methods together in order to capture the effect of dynamic contextual factors on the relationships in innovation field.
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    An Interactive data-driven (dynamic) multiple attribute decision making model via interval type-2 fuzzy functions
    (MDPI, 2019) Baykasoğlu, Adil; Gölcük, İlker
    A new multiple attribute decision making (MADM) model was proposed in this paper in order to cope with the temporal performance of alternatives during different time periods. Although dynamic MADM problems are enjoying a more visible position in the literature, majority of the applications deal with combining past and present data by means of aggregation operators. There is a research gap in developing data-driven methodologies to capture the patterns and trends in the historical data. In parallel with the fact that style of decision making evolving from intuition-based to data-driven, the present study proposes a new interval type-2 fuzzy (IT2F) functions model in order to predict current performance of alternatives based on the historical decision matrices. As the availability of accurate historical data with desired quality cannot always be obtained and the data usually involves imprecision and uncertainty, predictions regarding the performance of alternatives are modeled as IT2F sets. These estimated outputs are transformed into interpretable forms by utilizing the vocabulary matching procedures. Then the interactive procedures are employed to allow decision makers to modify the predicted decision matrix based on their perceptions and subjective judgments. Finally, ranking of alternatives are performed based on past and current performance scores.