Makine öğrenmesi tabanlı optimizasyon: Tutundurma yönetimi ve sıralama toplulaştırma uygulamaları

This thesis examines the synergy between Machine Learning (ML) and Operations Research (OR) to tackle complex optimization challenges in digital marketing (through proposing a genuine retention management framework) and sharing economy (via introducing a rank aggregation based matching approach for ride pooling problem). It aims to develop innovative models that combine ML's predictive accuracy with OR's decision-making strategies to enhance customer retention and optimize ride-sharing operations. The research follows a structured progression from theoretical concepts to practical applications, leading to creation of sophisticated algorithms that demonstrate significant improvements in operational efficiency. The introduction outlines the synergistic potential between ML and OR, emphasizing the need for a cross-disciplinary approach to solve real-world problems. It focuses on the application areas of customer retention and ride pooling. The reasoning behind these application areas is that these fields posses digital data, which makes possible to deploy advanced analytical techniques. Developed applications reaffirms the successful integration of ML and OR, as evidenced by enhanced decision-making processes and operational efficiencies in targeted applications. The results show notable advancements in predictive models for customer retention and a data-driven ride-matching algorithm, both of which significantly outperform the existing methods. Future research is directed towards developing more advanced algorithms that can handle larger datasets and deliver real-time analytics. The thesis also encourages further interdisciplinary studies to explore complex systemic challenges, promising to extend the scope and efficacy of the proposed models.