Aralık Değerli Nötrosofik Ortamda Mavi Yaka Personel Seçimi İçin Çok Kriterli Karar Verme Tekniklerini İçeren Entegre Bir Metodoloji
An Integrated Methodology Compromising MCDM Techniques for Blue-Collar Personnel Selection in an Interval-Valued Neutrosophic Environment
- Tez No: 1014673
- Danışmanlar: DR. ÖĞR. ÜYESİ SENA KIR
- Tez Türü: Yüksek Lisans
- Konular: Endüstri ve Endüstri Mühendisliği, Industrial and Industrial Engineering
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2026
- Dil: Türkçe
- Üniversite: Sakarya Üniversitesi
- Enstitü: Fen Bilimleri Enstitüsü
- Ana Bilim Dalı: Endüstri Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Belirtilmemiş.
- Sayfa Sayısı: Belirtilmemiş.
Özet
Sanayi ve üretim sektörlerinde artan rekabet koşulları, işletmeleri verimlilik, kalite ve sürdürülebilirlik hedeflerine daha fazla odaklanmaya zorlamaktadır. Bu hedeflere ulaşılmasında en kritik unsurlardan biri insan kaynağı olup, özellikle üretim hatlarında görev alan mavi yaka personelin doğru seçimi, üretim süreçlerinin sürekliliği ve performansı açısından belirleyici bir rol oynamaktadır. Buna rağmen, mavi yaka personel seçimi problemi, beyaz yaka pozisyonlara kıyasla literatürde daha sınırlı ele alınmakta ve çoğu zaman sezgisel ya da tek boyutlu değerlendirmelere dayalı olarak gerçekleştirilmektedir. Bu durum, karar vericilerin belirsizlik ve öznel yargılar içeren değerlendirme süreçlerinde sistematik ve güvenilir yöntemlere duyduğu ihtiyacı artırmaktadır. Bu çalışmanın temel amacı, mavi yaka personel seçimine özgü değerlendirme kriterlerini bütüncül bir bakış açısıyla ele almak ve belirsizlik içeren karar ortamlarında uygulanabilir, tutarlı ve karşılaştırmalı bir, Çok Kriterli Karar Verme (ÇKKV) çerçevesi geliştirmektir. Bu doğrultuda, personel seçimi alanındaki çalışmalar kapsamlı biçimde incelenmiş ve literatürde yaygın olarak kullanılan yirmi personel değerlendirme kriteri belirlenmiştir. Belirlenen kriterler, mavi yaka personel seçimi konusunda deneyimli dört insan kaynakları uzmanı tarafından değerlendirilmiş ve uzman görüşleri doğrultusunda karar sürecinde dikkate alınmıştır. Kriter ağırlıklarının belirlenmesinde, Analitik Hiyerarşi Süreci (AHP) ve Adım Adım Ağırlık Değerlendirme Oranı Analizi (SWARA) yöntemleri Aralık Değerli Nötrosofik (IVN) ortamda uygulanmıştır. IVN yaklaşımı, uzman değerlendirmelerinde yer alan belirsizlik, tutarsızlık ve kararsızlığı daha etkin biçimde modellemeye olanak sağlamaktadır. AHP ve SWARA yöntemlerinden elde edilen sonuçlar, güçlü ve bütünleşik bir ağırlıklandırma yapısı oluşturmak amacıyla Copeland Skoru yöntemi ile birleştirilmiş ve en yüksek ağırlık değerine sahip olan on kriter belirlenerek yeniden ağırlıklandırılmıştır. Çalışmanın uygulama aşamasında, bir lastik üretim hattı için değerlendirilen beş mavi yaka personel adayı, IVN ortamda İdeal Çözüme Benzerlik ile Tercih Sırası Tekniği (TOPSIS) kullanılarak sıralanmıştır. Elde edilen sonuçlar, önerilen yaklaşımın mavi yaka personel seçimi problemlerinde etkin, sistematik ve güvenilir bir karar destek aracı olarak kullanılabileceğini göstermektedir. Bu çalışma, IVN tabanlı AHP, SWARA, Copeland Skoru ve TOPSIS yöntemlerinin bütünleşik bir uygulamasını sunarak, ÇKKV literatüründe mavi yaka personel seçimine yönelik çalışmalara metodolojik katkı sağlamakta ve uygulayıcılara yol gösterici bir çerçeve sunmaktadır.
Özet (Çeviri)
Increasing competition in industrial and manufacturing sectors has compelled organizations to focus more intensively on operational efficiency, product quality, sustainability, and long-term competitiveness. Among all organizational resources, human resources constitute one of the most critical factors affecting organizational performance. In particular, blue-collar employees working directly on production lines have a significant impact on manufacturing efficiency, product quality, workplace safety, and operational continuity. Therefore, selecting the most suitable personnel is a strategic decision-making process that directly influences organizational success. Personnel selection is inherently a complex decision-making problem involving multiple qualitative and quantitative criteria. In practice, decision-makers often evaluate candidates based on factors such as technical competency, professional experience, teamwork capability, communication skills, discipline, adaptability, and physical suitability. Since these criteria frequently involve subjective judgments and linguistic assessments, personnel selection processes are characterized by uncertainty, vagueness, and inconsistency. Consequently, relying solely on intuition or traditional evaluation methods may lead to biased decisions and ineffective recruitment outcomes. To address such complexities, Multi-Criteria Decision-Making (MCDM) techniques have been widely utilized in personnel selection studies. MCDM approaches provide a systematic framework for evaluating multiple criteria simultaneously and support decision-makers in identifying the most appropriate alternative among several candidates. Although numerous studies have investigated white-collar personnel selection using various MCDM methods, blue-collar personnel selection has received comparatively limited attention in the literature. Considering the critical role of blue-collar employees in manufacturing environments, there is a need for more comprehensive and reliable decision-support frameworks specifically designed for this problem. Uncertainty has long been a major challenge in decision-making processes. To overcome this issue, Zadeh introduced Fuzzy Set Theory in 1965, allowing decision-makers to express their evaluations through membership degrees rather than precise numerical values. Later, Intuitionistic Fuzzy Sets were proposed by Atanassov to represent both membership and non-membership degrees simultaneously, enabling a more detailed representation of uncertainty. However, these approaches may still be insufficient in situations involving incomplete, inconsistent, or indeterminate information. To address these limitations, Neutrosophic Set Theory was introduced by Smarandache. Unlike fuzzy and intuitionistic fuzzy approaches, neutrosophic sets represent information using three independent components: truth-membership, indeterminacy-membership, and falsity-membership. This structure enables a more realistic representation of human judgments and allows decision-makers to express uncertainty and hesitation more effectively. Furthermore, Interval-Valued Neutrosophic (IVN) sets extend this concept by representing each component as an interval rather than a single value. Consequently, IVN sets provide greater flexibility and are particularly suitable for modeling expert opinions in uncertain decision-making environments. The main objective of this study is to develop an integrated MCDM framework for blue-collar personnel selection under uncertainty by utilizing Interval-Valued Neutrosophic sets. The proposed methodology aims to identify the most significant evaluation criteria, determine their relative importance, and rank candidate alternatives in a systematic and reliable manner. The study began with an extensive review of the personnel selection literature. As a result of this review, twenty evaluation criteria commonly used in personnel selection studies were identified. These criteria were subsequently assessed by four human resources experts who possess considerable experience in blue-collar recruitment processes. Expert evaluations were collected using linguistic variables represented within the IVN environment, enabling uncertainty and hesitation to be incorporated into the decision-making process. To determine the relative importance of the criteria, two different weighting approaches were employed: the Analytic Hierarchy Process (AHP) and the Step-wise Weight Assessment Ratio Analysis (SWARA) method. AHP is one of the most widely used MCDM techniques for deriving criteria weights through pairwise comparisons. The method provides a structured hierarchy and allows consistency analysis of expert judgments. On the other hand, SWARA determines criteria importance based on the sequential evaluation of criteria by experts and offers a relatively straightforward weighting procedure. Since each method has different strengths and evaluation mechanisms, applying both approaches contributes to the robustness of the weighting process. In this study, both AHP and SWARA were implemented within an Interval-Valued Neutrosophic environment (IVN). The IVN-AHP and IVN-SWARA methods enabled the incorporation of uncertainty, inconsistency, and indeterminacy into expert assessments, thereby improving the realism of the evaluation process. Following the application of these methods, two separate sets of criteria weights were obtained. Since the weights derived from AHP and SWARA may differ due to their methodological characteristics, the Copeland Score method was employed to integrate the results. Copeland Score is a ranking aggregation technique that combines the outcomes of multiple methods and generates a consensus ranking. Through this procedure, the strengths of both weighting methods were combined, resulting in a more comprehensive and balanced weighting structure. Based on the integrated results, the ten most important criteria were selected from the initial set of twenty criteria. These criteria were subsequently reweighted and used in the final candidate evaluation stage. To demonstrate the applicability of the proposed framework, a real-world case study was conducted in a tire manufacturing company. The company intended to recruit a blue-collar employee for a production line position. Five candidate alternatives were evaluated by the same panel of four experts. Candidate assessments were performed under an IVN environment in order to capture uncertainty and subjectivity in expert judgments. The final ranking of candidates was obtained using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). TOPSIS is a widely adopted MCDM method that identifies the best alternative based on its proximity to the ideal solution and distance from the negative ideal solution. In the proposed framework, IVN-TOPSIS was employed to maintain consistency with the neutrosophic representation used throughout the study. Candidate evaluations were aggregated, normalized, weighted, and analyzed according to the IVN-TOPSIS procedure, resulting in a final ranking of the five candidates. The findings demonstrate that the proposed integrated framework provides a systematic and effective mechanism for blue-collar personnel selection under uncertainty. The methodology successfully incorporates expert knowledge, handles ambiguity in evaluations, and supports objective decision-making. Moreover, the use of multiple weighting methods combined through Copeland Score enhances the reliability and robustness of the final results. This study makes several contributions to the literature. First, it focuses specifically on blue-collar personnel selection, a topic that has received limited attention compared with white-collar recruitment problems. Second, it integrates IVN-AHP and IVN-SWARA methods for criteria weighting and combines their results through the Copeland Score approach, which has rarely been investigated in personnel selection studies. Third, the study presents a comprehensive application of IVN-based MCDM techniques, including IVN-AHP, IVN-SWARA, Copeland Score, and IVN-TOPSIS, within a unified decision-making framework. Finally, the proposed methodology is validated through a real-world case study, demonstrating its practical applicability and usefulness for human resource management. In conclusion, the proposed integrated methodology offers an effective decision-support tool for blue-collar personnel selection in uncertain environments. By combining IVN theory with multiple MCDM techniques, the framework enables decision-makers to evaluate candidates more objectively, consistently, and systematically. The results suggest that the methodology can support organizations in improving recruitment quality, reducing selection errors, and enhancing workforce performance. Furthermore, the proposed approach can be adapted and extended to other human resource management problems and decision-making applications characterized by uncertainty and subjective evaluations.
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