Tahmin sonrası optimizasyon ve kısıt öğrenme: E-ticaret karar alma süreçleri için ampirik bir karşılaştırma

In today's rapidly evolving e-commerce landscape, effectively balancing price and commission strategies is vital for platforms aiming to maximize both merchant profitability and their own revenue. This MSc. thesis introduces an innovative approach by integrating advanced machine learning techniques with optimization frameworks, specifically comparing the traditional Predict-then-Optimize (PTO) method against the integrated Constraint Learning (CL) paradigm. Through rigorous simulation and empirical evaluation using real-world marketplace data, the proposed frameworks demonstrate clear benefits in strategic decision-making, efficiency, and robustness. Ultimately, this research paves the way for smarter, more dynamic pricing decisions in complex online retail ecosystems.