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    A New Hybrid MCDM Model for Insulation Material Evaluation for Healthier Environment
    (Mdpi, 2022) Aksakal, Berrak; Ulutas, Alptekin; Balo, Figen; Karabasevic, Darjan
    One of the easiest and most common methods for effectively reducing building energy demand is the selection of adequate thermal insulation materials. Thermal insulation is a substantial contribution and an evident, logical and practical first stage toward improving energy performance, particularly in envelope-load-dominant structures located in difficult climate zones. Today's insulating materials come in a broad variety of sizes and shapes, each with its a own qualities. It is well acknowledged that material selection is one of the most difficult and time-consuming aspects of a construction project. Therefore, choosing the right insulation material is also a very important topic to increase energy efficiency. However, it is a complex problem with many criteria and alternatives. This study integrates three different multi criteria decision making methods, which are Fuzzy Best-Worst Method, CRiteria Importance Through Inter-criteria Correlation and Mixed Aggregation by COmprehensive Normalization Technique. In this study, the following eight criteria were taken into account in the evaluation: thermal conductivity, periodic thermal transmittance, specific heat, density, decrement factor, surface mass, thermal transmittance, and thermal wave shift. The first method will be used to find the subjective weights, while the second method will be used to find the objective weights. The third method will be used to rank the insulation materials. According to the results of the Fuzzy Best-Worst Method, the most important criterion was determined as thermal conductivity. According to the results of the CRiteria Importance Through Inter-criteria Correlation, the most important criterion was determined as thermal wave shift. According to the results of the Mixed Aggregation by COmprehensive Normalization Technique, the top 10 insulation materials are as follows: polyisocyanurate, polyurethane (1), polyurethane (2), wood fiber (1), kenaf, jute, cellulose (2), wood fiber (1), XPS (1) and XPS (2). According to the results of the proposed method, polyisocyanurate was determined as the best insulation material for healthier environment. This study makes two contributions to the literature: first, a new hybrid method was developed in this study. Secondly, in this study, the newly introduced Mixed Aggregation by COmprehensive Normalization Technique method was used.
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    A new integrated grey mcdm model : Case of warehouse location selection
    (Faculty of Mechanical Engineering, 2021) Ulutaş, Alptekin; Balo, Figen; Sua, Lutfu; Demir, Ezgi; Topal, Ayşe; Jakovljević, Vladimir
    Warehouses link suppliers and customers throughout the entire supply chain. The location of the warehouse has a significant impact on the logistics process. Even though all other warehouse activities are successful, if the product dispatched from the warehouse fails to meet the customer needs in time, the company may face with the risk of losing customers. This affects the performance of the whole supply chain therefore the choice of warehouse location is an important decision problem. This problem is a multicriteria decision-making (MCDM) problem since it involves many criteria and alternatives in the selection process. This study proposes an integrated grey MCDM model including grey preference selection index (GPSI) and grey proximity indexed value (GPIV) to determine the most appropriate warehouse location for a supermarket. This study aims to make three contributions to the literature. PSI and PIV methods combined with grey theory will be introduced for the first time in the literature. In addition, GPSI and GPIV methods will be combined and used to select the best warehouse location. In this study, the performances of five warehouse location alternatives were assessed with twelve criteria. Location 4 is found as the best alternative in GPIV. The GPIV results were compared with other grey MCDM methods, and it was found that GPIV method is reliable. It has been determined from the sensitivity analysis that the change in criteria weights causes a change in the ranking of the locations therefore GPIV method was found to be sensitive to the change in criteria weights
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    PERFORMANCE ANALYSIS FOR THE MOST CONVENIENT WIND TURBINE SELECTION IN WIND ENERGY FACILITY
    (Editura Ase, 2022) Zavadskas, Edmundas Kazimieras; Ulutas, Alptekin; Balo, Figen; Stanujkic, Dragisa; Karabasevic, Darjan
    As governments seek renewable and more sustainable energy resources, wind energy has emerged as one of the most rapidly developing renewable power resources. The relevance of wind energy turbines has grown as more nations turn to renewable energy. A significant criterion that conduces to wind energy's efficient production is the proper wind turbine's utilize. Due to the fact that a lot of wind turbine manufacturers have built a global presence, it is critical for project administrators to do informed selections about which wind energy turbines to establish in every specific design. Therefore the problem of wind energy turbine choice is critical for nations experiencing global warming and climate change. This study proposes a new hybrid MCDM model including CCSD and MULTIMOOSRAL methods. In this study, 11 100kW wind turbines (T) are evaluated based on 14 criteria. According to the results of the proposed model, T7 coded wind turbine alternative was determined as the best one. The results of the proposed method were compared with other MCDM methods and it was confirmed that the proposed method reached accurate results. In addition, it was determined that the changes in the criteria weights changed the ranking of the alternatives.