## 2 Literature Review
### Scientific Research on Sustainable Strategy Assessment
Although there are numerous instances in the literature that have explored the approach to sustainability in logistics, most studies have focused on individual aspects such as economic or environmental factors, ignoring the complex interactions between economic, social, and environmental factors. Furthermore, some studies have lacked effective quantitative methods to evaluate and compare sustainability strategies, which makes it difficult to apply in practice. Therefore, the development of an integrated framework, capable of considering multiple complex and multidimensional factors simultaneously, is a notable gap that needs to be filled. Identifying Research Gaps and Advancing MCDM Approaches for Sustainable Logistics Strategy Evaluation  
MCDM methods have proven to be important in solving complex problems that require analysis of multiple criteria with different weights and priorities. Methods such as AHP, TOPSIS, DEA, FAHP, and, recently, NFAD are widely applied in logistics to evaluate operational efficiency, select suppliers, optimize supply chains, and evaluate sustainable strategies. Research by [PERSON] et al. [26] proposed a model for selecting smart network strategies and determining the green transport index for logistics companies in the Black Sea region of Turkiye. The goal is to support companies in optimizing resources and protecting the environment while creating competitive advantages. The PF-LBWA and PF-CoCoSo methods were used to evaluate and propose suitable strategies for sustainable logistics. To manage domestic logistics operations with a focus on environmental sustainability, as shown by [PERSON] et al. [27], fuzzy DEMATEL analysis was used. The study identified key management concepts and the relationships between them, emphasizing the important roles of strategic and operations management. The model not only provides a strong theoretical framework for sustainable logistics management, but also provides practical implications for integrating the model into domestic logistics operators to improve efficiency and meet environmental standards. Research by [PERSON] et al. [28] selected the best scenario for developing a smart reverse logistics system to support the circular economy and closed-loop supply chain. Using an MCDM model integrating Delphi, ANP, and COBRA in a fuzzy environment, the study evaluated four scenarios based on six key criteria. The results suggested the integration of Industry 4.0 technologies, such as IoT, AI, blockchain, and big data, emphasizing the importance of consensus among stakeholders. In a study by [PERSON] and [PERSON] [29], the authors combined the fuzzy theory and MCDM model to evaluate and select potential third-party logistics (3 PL) service providers. This was in order to propose a framework for selecting suitable urban logistics (CL) concepts, supporting sustainable urban development. With conflicting objectives among stakeholders, [PERSON] et al. [30] applied a hybrid MCDM model combining DEMATEL, ANP, and VIKOR to handle conflicting and uncertain factors. This framework supports planners, authorities, and service providers in choosing the optimal solution. The method is illustrated through an application in the City of Belgrade. Research by [PERSON] [31] presented the first stage of the MCDM process, using the Electre III method to select logistics hub locations. The regions were analyzed on the macrolevel, evaluating them according to technological, economic, social, infrastructure, and environmental criteria. The ranking results reflected the suitability of each region and the preferences of the decision-maker. Research by [PERSON] et al. [32] proposed a fuzzy set theory-based MCDM model to optimize reverse logistics (RLS) strategies and select the most efficient recycling option. The model evaluates both qualitative and quantitative criteria such as cost, environmental impact, quality, and market, supporting flexible and effective policy design.