Abstract: This study models the "coffee shop dilemma", where customer attendance is discouraged by both overcrowding and emptiness. Using an agent-based model with Q-learning reinforcement learning, this study simulates the daily decisions of 100 agents over a one-year period. The results reveal a self-organizing attendance cycle around a $60\%$ capacity threshold. This study demonstrates that customer satisfaction is not driven by visit frequency, but by adaptive decision-making strategies shaped by learned congestion values. Clustering analysis identifies distinct behavioral patron groups (e.g., Ultra-Frequent, Optimized) that emerge from these subtle value differences. The study provides a data-driven framework for optimizing shop space and customer flow, offering conceptual insights into balancing the needs of quick-service and long-stay customers by dynamically managing perceived occupancy.

Siranee Nuchitprasitchai, Kanchana Viriyapant, Kanjanee Satitrangseewong and May Myo Naing. “Adaptive Intelligence in Retail Space Optimization: Modeling the Coffee Shop Dilemma with Q-Learning Agents”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161288

@article{Nuchitprasitchai2025,
title = {Adaptive Intelligence in Retail Space Optimization: Modeling the Coffee Shop Dilemma with Q-Learning Agents},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161288},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161288},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Siranee Nuchitprasitchai and Kanchana Viriyapant and Kanjanee Satitrangseewong and May Myo Naing}
}