Scopus Kapalı Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10761

Browse

Filters

2021 - 20222

Settings

Author: search.filters.author.Yurdakul, MustafaSubject: search.filters.subject.Performance measurement

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Item
    The Use of Petri Nets in Performance Analysis of Flexible Manufacturing Cell
    (2022) Bulca, Fatima Busra; Ic, Yusuf Tansel; Yurdakul, Mustafa
    A Flexible Manufacturing Cell (FMC) is a system that consists of computer-integrated machines, automatic material handling systems, and robots. It produces different part types with minimal worker involvement. Many different methods, such as queuing systems and simulation models, have been applied in FMC modeling in the literature. This study aims to use Petri Nets (PN) in modelling complex FMCs. The study is carried out in the FMC located in the Advanced Manufacturing Systems Laboratory of Baskent University. The FMC consists of a CNC machining center, a material handling robot and a pallet, which has three work-piece carriage capacities. Processing times of FMC elements may vary due to reasons (the power usage performance, air supply efficiency, electrical power efficiency, etc.) arising from the mechanical structure of the system. This uncertainty needs to be taken into account when modeling the system. In this study, the uncertain (fuzzy) processing times associated with the transitions were caused by the mechanical structure of the system. In order to compare with the newly developed fuzzy models, the system was first modeled using Transition Time Petri Nets (TTPN), in which the running times associated with the transition times take precise values. On the other hand, for handling fuzzy values of the actual operating times in the FMC, two models namely, Partial-Fuzzy Transition Time Petri Nets (Partial-FTTPN) and Fuzzy Transition Time Petri Nets (FTTPN) are developed. The results of the three models are compared to analyze the benefits of combining "time uncertainties" resulting from the natural behavior of FMCs.
  • No Thumbnail Available
    Item
    Development of a new trapezoidal fuzzy AHP-TOPSIS hybrid approach for manufacturing firm performance measurement
    (2021) Ic, Yusuf Tansel; Yurdakul, Mustafa; 0000-0001-9274-7467; AGE-3003-2022
    This study develops a multi-level hierarchical performance measurement model to measure a manufacturing firm's overall performance score by grading its success levels in critical operations and combining them. Linking overall performance score to local grades of a manufacturing firm in critical operations requires placement of manufacturing goals in the performance measurement model. The relative importance scores of the components at any level in the multi-level performance measurement model with respect to each component belonging to the immediately above level are determined using the fuzzy analytic hierarchy process (FAHP) method. The relative importance scores of the components are combined with success grades in seventeen pre-determined critical operations to obtain overall performance scores for manufacturing firms using the technique for order preference by similarity to ideal solution (TOPSIS) approach. In this study, scorecards are developed to guide scoring in each critical operation by checking levels of success in terms of practices, infrastructures, investments and actions. The developed performance measurement approach provides a structured decision-making environment with the scorecards and fixed hierarchy. Furthermore, the developed approach is more comprehensive in representing important issues necessary for obtaining realistic overall performance scores. For example, fuzzy numbers take into account vagueness (uncertainties) in the assignment of scores. Another advantage identified by the users is that the developed decision hierarchy can be adapted to new sectors or decision environments by adding new components or removing existing ones using the same overall structure and calculation steps.