Like most other industries, logistics services are currently encountering enormous transformation. Many companies worldwide are applying big data analytics to implement operational strategies and facilitate location selection. Logistics companies need to focus on now and explore some futures of logistics hub location problems.Creating a profitable logistic network is a crucial task for airline and postal services.To establish a cost-effective network, transportation expenses should be decreased, and networks should be simplified. Hub location problem is born out of these needs.The concept of hub cumulates the flows and makes networks more reliable. In this article, a novel hub location selection approach is introduced in a group decision making (GDM) environment with uncertainty by integrating a modified weighted k-means clustering algorithm with multi-criteria decision-making (MCDM) tools. The combined MCDM method integrates analytic hierarchy process (AHP) to measure criteria weights and Additive Ratio Assessment technique to measure the performance of hub location alternatives in a spherical fuzzy set (SFS) environment. The SFS has shown definite advantages in handling vagueness and uncertainty over crisp, fuzzy, or intuitionistic fuzzy sets to depict experts' evaluations with a richer structure, allowing for more representative decision making. Using Turkish logistics data, big data algorithm facilitates 15 possible locations, and these sites are ranked in order by the integrated GDM methodology. The validation of the proposed evaluation model is illustrated in an application of the network structure in Turkey. Finally, sensitivity and comparison evaluations are introduced to demonstrate the feasibility and effectiveness of the proposed approach.additive ratio assessment (ARAS), analytic hierarchy process (AHP), big data, hub location, k-means clustering, spherical fuzzy sets (SFSs)
| INTRODUCTIONThe network design problem and performance analysis for logistic networks have attracted much attention from researchers in recent years. Different types of network structures are created to identify the needs and expectations of customers. Hub location problem arises due to the complexity of the transportation networks. In traditional networks, all nodes in the system must be linked with each other. This linkage structure causes complexities and complications for network design problems. On the other hand, given nodes must be linked with hubs in hub location
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