Journal of Industrial Engineering and Management Studies

Abstract The rapid digital transformation of the tourism sector has fundamentally altered customer behavior, rendering traditional demographic and transactional segmentation approaches insufficient for modern e-marketing. This study addresses the critical research gap in data-driven customer segmentation by developing a multidimensional clustering framework tailored to the Iranian tourism industry. Utilizing a comprehensive dataset of 6,000 digital tourism consumers, the research employs the K-Means clustering algorithm integrated with advanced validation indices, including the Silhouette coefficient, Within-Cluster Sum of Squares (WCSS), and the Elbow method. The methodology encompasses rigorous data preprocessing, Min-Max normalization, and the derivation of five strategic customer value dimensions: Customer Lifetime Value (CLV), Customer Referral Value (CRV), Customer Influencer Value (CIV), Customer Brand Value (CBV), and Customer Knowledge Value (CKV). The clustering analysis identifies three distinct, statistically valid customer segments, with an optimal Silhouette score of 0.562 and a stabilized inertia decline at K=3. The resulting segments reveal heterogeneous behavioral profiles: a low-value, high-churn-risk group requiring onboarding optimization; a stable, high-retention group demanding loyalty reinforcement; and a high-value, high-influence group necessitating strategic referral and co-creation initiatives. Key numerical findings demonstrate that Cluster 2 contributes disproportionately to total customer value (TCV) while exhibiting superior brand engagement and influencer metrics. The study’s managerial implications emphasize precision resource allocation, hyper-personalized e-marketing campaigns, and dynamic CRM routing. Theoretically, this research extends customer value literature by validating a multidimensional clustering architecture in an emerging market context. By replacing heuristic segmentation with algorithmic, behavior-driven profiling, the framework provides tourism managers with a scalable, actionable tool for enhancing digital marketing efficiency and sustainable competitive advantage.

Abstract This paper introduces a two stage stochastic programming to address strategic hub location decisions and tactical flight routes decisions for various customer classes considering uncertainty in demands. We considered the airline network with the arc capacitated single hub location problem based on complete–star p-hub network. In fact, the flight routes are allowed to stop at most two different hubs. The first stage of the model (strategic level) determines the network configuration, which does not change in a short space of time. The second stage is dedicated to specify a service network consists of determining the flight routes and providing booking limits for all itineraries and fare classes after realization of uncertain scenarios. To deal with the demands uncertainty, a stochastic variations caused by seasonally passengers’ demands through a number of scenarios is considered. Since airline transportation networks may face different disruptions in both airport hubs and communication links (for example due to the severe weather), proposed model controls the minimum reliability for the network structure. Due to the computational complexity of the resulted model, a hybrid algorithm improved by a caching technique based on genetic operators is provided to find a near optimal solution for the problem. Numerical experiments are carried out on the Turkish network data set. The performance of the solutions obtained by the proposed algorithm is compared with the pure GA and Particle Swarm Optimization (PSO) in terms of the computational time requirements and solution quality.