Context—In today’s competitive business environment, increasing sustainability concerns and environmental awareness have encouraged companies to adopt closed-loop supply chain (CLSC) structures. CLSCs involve not only the forward flow of products from suppliers to consumers but also the reverse flow, including the collection, recovery, recycling, and disposal of returned products. However, the reintegration of returned products into the supply chain increases network complexity, especially under dynamic market conditions. Therefore, effective management of forward and reverse supply chain processes is essential. In this context, the appropriate use of push and pull strategies and the correct positioning of the push–pull boundary constitute an important decision problem that directly affects supply chain performance.Objective—The aim of this study is to investigate the effect of different push–pull boundary positions on the performance of a CLSC. Specifically, the study aims to determine which service provider should be selected as the push–pull boundary to improve key performance indicators, including total profit, inventory holding cost, penalty cost, sales profit, and delivery lead time.Method—In this study, a simulation-based approach was employed to model the CLSC structure. Eight different simulation models were developed, each representing a different push–pull boundary position at various service providers within the supply chain network. To systematically analyze the factors affecting model performance, the Taguchi experimental design method was used. This method enabled the determination of appropriate factor levels and the evaluation of different push–pull configurations under controlled experimental conditions.Results—The results show that the performance of the CLSC varies depending on the location of the push–pull boundary. Positioning the push–pull boundary between the supplier and the recycling facility maximizes average total profit. A pure pull strategy provides the lowest inventory holding cost. Locating the boundary at the distribution center leads to better performance in terms of penalty cost and sales profit. In addition, placing the push–pull boundary at the wholesaler reduces the average delivery lead time.Conclusion—The findings indicate that the position of the push–pull boundary plays a critical role in CLSC performance. Since different boundary locations improve different performance measures, managers should determine the push–pull strategy according to their primary operational objectives. The study provides useful insights for designing more effective and sustainable CLSCs. Future studies may extend this approach by considering uncertainty, multiple product types, stochastic demand, or real-world case applications.
Context—Panel board procurement is the primary cost driver in furniture manufacturing, requiring simultaneous evaluation of price competitiveness, delivery reliability, quality consistency, and compliance requirements under uncertainty. In the Turkish context, inflationary pressure during 2023–2025 elevated price stability as a procurement criterion alongside absolute price level, while growing export requirements introduced Forest Stewardship Council (FSC) certification as a qualification threshold for part of the supplier pool. Despite these conditions, MCDM applications in furniture procurement remain methodologically conservative—AHP-based weighting paired with TOPSIS or ARAS ranking dominates—and no prior study has combined consistency-optimizing weighting with rank reversal-resistant ranking in a fuzzy environment for panel board supplier selection.Objective—This paper integrates Fuzzy FUCOM and Fuzzy RAFSI to address the gap mentioned in the context section. The integration has appeared in only a single prior study, confined to a defense-sector application; the present work extends it to a manufacturing procurement context, replaces D-number inputs with a fully fuzzy triangular fuzzy number formulation, and explicitly tests rank reversal resistance rather than assuming it.Method—The framework is applied to a medium-scale Turkish furniture manufacturer evaluating twelve panel board suppliers across eight criteria. FUCOM derives criterion weights from a vector of fuzzy significance values and exactly n - 1 derived comparative priorities, solved as a single fuzzy program in which consistency is enforced as a mathematical constraint. RAFSI ranks alternatives on a ratio scale anchored by ideal and anti-ideal values specified exogenously from operational thresholds, which removes the dependence on the alternative set underlying the rank reversal susceptibility of TOPSIS-family methods. Three domain experts provided criterion priority rankings; Kendall's W = 0.979 confirmed near-perfect inter-expert agreement. Rankings are validated through Fuzzy VIKOR and Fuzzy MABAC, with stability assessed under nine weight perturbation scenarios and four rank reversal tests.Results—Unit price and delivery reliability together account for 43.27% of total criterion weight, reflecting the cost-sensitive, schedule-driven character of furniture production under inflationary conditions. S1 ranks first across all three evaluation methods and holds that position under all nine perturbation scenarios. Rank reversal resistance is confirmed through four tests—addition of an alternative inside and outside the performance envelope of the existing set, and removal of a mid-ranked and an extreme-profile alternative—each of which reproduces every retained composite score to six decimal places, with Spearman . The Spearman coefficient between RAFSI and MABAC is , indicating strong methodological convergence.Conclusion—The Fuzzy FUCOM–RAFSI integration delivers an internally consistent, computationally transparent supplier ranking for panel board procurement under inflationary conditions, addressing a methodological gap in the furniture sector literature. The rank reversal resistance confirmed empirically here—including under the exterior-addition test that alternative-set-dependent aggregations cannot pass—provides procurement managers with a stable decision support tool whose outputs hold under changes to the candidate set.
Arka Plan—Günümüzde iklim değişiklikleri ve çevre problemleri önemli bir gündem olarak karşımıza çıkmaktadır. Özellikle ulaşım sektöründe sürdürülebilir olmayan yakıtların kullanımı, artan çevre kirliliği ile doğrudan ilişkilidir. Fosil yakıtlı araçlar hava, gürültü ve çevre kirliliklerine sebep olmaktadır. Bununla birlikte sera gazı emisyonlarının azaltılmasına yönelik uluslararası anlaşmalar ve politikalar, ulaşım sektöründe dönüşümü zorunlu hale getirmektedir. Artan araç sayısı ile fosil yakıtların tükenme hızının yükselmesi kullanıcıları alternatif enerji kaynaklarına yönlendirmektedir. Bu bağlamda enerji verimliliği ve çevresel sürdürülebilirlik kavramları daha fazla önem kazanmaktadır. Tüm bu etkenlerle sürdürülebilir bir tercih olarak karşımıza çıkan elektrikli araçlar, fosil yakıtlı araçlara ek yeni bir kavramı doğurmuştur. Elektrikli araçlar; sıfıra yakın karbon emisyonu, daha düşük yakıt maliyeti, düşük gürültü seviyesi ile çalışmaları dolayısıyla daha çevre odaklıdırlar. Pek çok kullanım avantajı olan bu araçların kullanımının yaygınlaşabilmesi için altyapının doğru şekilde sağlanması gerekmektedir. Altyapı planlamasının yetersiz olması durumunda kullanıcıların menzil kaygısı artmakta ve bu durum elektrikli araçlara olan talebi olumsuz etkileyebilmektedir. Yeterli sayıda ve doğru konumda şarj istasyonlarının bulunmaması, elektrikli araç kullanımını teşvik etmede en büyük engeldir ve sürücüler üzerinde olumsuz bir etki yaratmaktadır. Tüm bu etkenler şarj istasyonlarının stratejik ve doğru alanlara yerleşiminin gereksinimini ortaya çıkarmaktadır. Alanların doğru tayin edilebilmesi de yeni bir seçim problemini doğurmuştur. Bu seçim problemi, teknik, ekonomik ve çevresel faktörlerin birlikte değerlendirilmesini gerektiren karmaşık bir karar verme sürecidir. Özellikle organize sanayi bölgelerindeki doğru planlama ile şarj taleplerini en hızlı şekilde karşılayarak zaman kayıplarını önlemek önem arz etmektedir.Amaç—Bu çalışma, Bursa ilinde yer alan beş organize sanayi bölgesinin (NOSAB, DOSAB, HOSAB, TEKNOSAB ve İnegöl OSB) elektrikli araç şarj altyapısı yatırımları açısından makro düzeyde önceliklendirilmesini amaçlamaktadır.Yöntem—Çok kriterli karar verme (ÇKKV) yöntemleri belirsizlik içeren karar problemlerinde etkin bir analiz çerçevesi sunar. Bu çalışmada Bursa ili organize sanayi bölgeleri için elektrikli araç şarj istasyonlarının en uygun konumunun belirlenmesine yönelik ÇKKV yaklaşımlarından olan CRITIC ve Bulanık Gri İlişkisel Analiz (BGİA) yöntemleri bütünleşik olarak kullanılmıştır. Karar matrisi oluşturulurken; şebeke yük etkisi, maliyet, erişilebilirlik, talep yoğunluğu, genişleme uygunluğu, güvenlik ve altyapı kaynaklarına mesafe olmak üzere yedi kriter belirlenmiştir. Bu kriterler teknik ekonomik ve fonksiyonel parametreleri içerecek şekilde literatür incelemesi sonucu belirlenmiştir. CRITIC yöntemi ile kriterlerin ağırlıkları belirlenmiş, BGİA ile de alternatiflerin sıralaması yapılarak ideal çözüme yakınlık dereceleri hesaplanmıştır. ENTROPİ yöntemi ile yeni kriter ağırlıkları elde edilmiş Bulanık TOPSIS ile duyarlılık analizi kapsamında modelin doğruluğu kontrol edilmiştir.Bulgular—Elde edilen sonuçlar hibrit çözümlerin belirsizlik içeren ÇKKV ile yer seçimi problemi için uygulanabilir bir yaklaşım olduğunu göstermiştir. Analiz sonuçlarında, tüm kriterlerde en homojen ve dengeli performansı sergileyen HOSAB en uygun lokasyon olarak ilk sırada yer almıştır. Alternatiflerin nihai sıralaması HOSAB>NOSAB>DOSAB>TEKNOSAB>İnegöl OSB şeklinde gerçekleşmiştir.Sonuç—Elde edilen bulgular bölgesel yatırım bütçelerinin ve altyapı teşviklerinin ilk etapta HOSAB bölgesine yönlendirilmesi gerektiğini ortaya koymaktadır.
Analyzing sports activities using machine learning algorithms is an emerging approach for identifying key parameters that influence the athlete’s performance. Implementing such algorithms enables sports engineers to develop improved sports equipment and facilities, analyze athlete performance and design personalized training programs. In this study, 18 physical and performance-related parameters were collected from 30 hearing-impaired athletes, and 20 models were constructed based on different parameter combinations. Tree-based machine learning algorithms (tree boost, decision tree forest, and single decision tree) were applied to predict athletes’ standing long jumping distance. Model performance was evaluated using root mean square error (RMSE), mean absolute percentage error (MAPE), and the coefficient of determination (R2). The best results were obtained from the tree boost algorithm for Model 11 with an RMSE of 15.08, an R2 value of 0.95, and a MAPE of 6.31%. Model 11, including physical-based parameters (gender, weight, age, leg strength, subcutaneous fat percentages from thigh, triceps, biceps, chest, abdomen, suprailiac, subscapula) and performance-based parameters (push-up, and 12 min-running), demonstrated superior predictive capability.
Dye-sensitized solar cells (DSSCs) have emerged as an alternative photovoltaic technology to conventional systems due to their low cost, flexibility, semi-transparency, and adaptability to various application domains. In recent years, advancements in materials engineering, device architecture, and performance optimization have made DSSCs a prominent topic of research, particularly in indoor, wearable, and building-integrated energy systems. This review systematically addresses recent developments in DSSC technology, focusing on fundamental components such as n-type and p-type cell architectures, photoanodes, dyes, electrolytes, and counter electrodes. While the effects of material selection and nanostructure engineering on photovoltaic performance are detailed, major technical and economic barriers to commercialization such as the thermal instability and leakage risk of liquid electrolytes, the limited lifetime of organic dyes, efficiency ceilings, and the high cost of platinum-based counter electrodes are also highlighted. Opportunities and future research directions are discussed in the context of solid-state electrolytes and their potential in next-generation applications such as flexible devices, textile integration, and low-light indoor environments. In conclusion, DSSCs represent a versatile and innovative photovoltaic platform with significant future application potential, offering a promising balance of cost, stability, and performance.
