ÜRETİMDE BAKIM STRATEJİSİ SEÇİMİ İÇİN SEZGİSEL BULANIK TOPSIS YAKLAŞIMI

Year 2025, Volume: 13 Issue: 3, 1001 - 1012, 30.09.2025

Ülge Taş

https://doi.org/10.21923/jesd.1739911

Abstract

Otomotiv endüstrisinde verimlilik, güvenlik ve maliyet optimizasyonunu sağlamak için bir bakım stratejisinin seçimi çok önemlidir. Ancak, çeşitli çelişkili kriterler ve uzman kararlarında bulunan belirsizlikler nedeniyle karar alma süreci karmaşık olabilir. Bu çalışma, bu zorlukları ele almak için İdeal Çözüme Benzerliğe Göre Sıra Tercihi için Sezgisel Bulanık Tekniği'ni (IF-TOPSIS) kullanan yapılandırılmış bir karar alma çerçevesi sunmaktadır. Bu yöntem, altı temel kritere (güvenlik, maliyet, operasyonel faktörler, güvenilirlik, risk ve katma değer) dayalı olarak dört bakım alternatifinin (Reaktif, İlkel, Önleyici ve Tahmini Bakım) kapsamlı bir şekilde değerlendirilmesini sağlar. Uzman değerlendirmeleri, üyelik, üye olmama ve tereddüt derecelerini yakalamak için sezgisel bulanık kümeler kullanılarak modellenmiştir. Her alternatifin ideal çözüme göreli yakınlığını değerlendirmek için IF-TOPSIS algoritması kullanılmıştır. Bulgular, performans, risk ve maliyeti dengeleme yeteneğini göstererek en etkili bakım stratejisini doğrulamıştır. Ayrıca, bir duyarlılık analizi, modelin çeşitli kısıt koşulları altında kararlılığını doğrulamıştır. IF-TOPSIS çerçevesi, belirsizlik ve öznelliğin hakim olduğu endüstriyel ortamlarda bakım planlaması ve strateji formülasyonu için güvenilir ve kapsamlı bir karar destek aracı olarak onaylanmıştır. Bu araştırmanın, bakım stratejilerinin seçilmesinde sezgisel bulanık çok kriterli karar verme (ÇKKV) yaklaşımlarının pratik etkinliğini göstererek literatüre ve endüstriye katkı sağlaması beklenmektedir.

Keywords

IF-TOPSIS , Bakım Stratejisi , ÇKKV , Üretim Maliyetleri , Duyarlılık Analizi

INTUITIONISTIC FUZZY TOPSIS APPROACH FOR MAINTENANCE STRATEGY SELECTION IN PRODUCTION

Abstract

The selection of a maintenance strategy is crucial for ensuring efficiency, safety, and cost optimization in the automotive industry. However, the decision-making process can be complex due to various conflicting criteria and the uncertainties inherent in expert judgments. This study presents a structured decision-making framework that utilizes the Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IF-TOPSIS) to address these challenges. This method allows for a thorough evaluation of four maintenance alternatives—Reactive, Primitive, Preventive, and Predictive Maintenance—based on six key criteria: safety, cost, operational factors, reliability, risk, and added value. Expert assessments were modeled using intuitionistic fuzzy sets to capture the degrees of membership, non-membership, and hesitation. The IF-TOPSIS algorithm was employed to evaluate the relative closeness of each alternative to the ideal solution. The findings confirmed the most effective maintenance strategy by demonstrating its ability to balance performance, risk, and cost. Additionally, a sensitivity analysis verified the model’s stability under various constraint conditions. The IF-TOPSIS framework has been confirmed as a reliable and comprehensive decision support tool for maintenance planning and strategy formulation in industrial settings characterized by uncertainty and subjectivity. This research is expected to enhance the literature and the industry by demonstrating the practical effectiveness of intuitive fuzzy multi-criteria decision-making (MCDM) approaches in selecting maintenance strategies.

Keywords

IF-TOPSIS , Maintenance Strategy , MCDM , Production Costs , Sensitivity Analysis

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