Adaptive Intelligence in Retail Space Optimization: Modeling the Coffee Shop Dilemma with Q-Learning Agents

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.

@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},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Siranee Nuchitprasitchai and Kanchana Viriyapant and Kanjanee Satitrangseewong and May Myo Naing},
  doi       = {10.14569/IJACSA.2025.0161288},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161288}
}