Date of Award
Summer 6-19-2026
Document Type
Thesis
Publication Status
Version of Record
Submission Date
July 2026
Department
Computer and Electrical Engineering and Computer Science
College Granting Degree
College of Engineering and Computer Science
Department Granting Degree
Electrical Engineering and Computer Science
Degree Name
Master of Science (MS)
Thesis/Dissertation Advisor [Chair]
Zhen Ni,
Thesis/Dissertation Co-Chair
Evangelos I. Kaisar
Abstract
Reinforcement learning methods have demonstrated strong performance on vehicle routing benchmarks, yet their behavior under severe capacity constraints remains unexplored. This dissertation investigates whether PPO-based neural routing policies maintain operational viability when vehicle capacity is severely constrained, as occurs in resource-limited rural logistics settings.
Through controlled experiments on synthetic instances and validation on real-world rural healthcare networks in Florida, this research reveals a critical capacity threshold effect. Moderate capacity reductions from 40 to 20 produce negligible performance loss (4.1%), while severe reductions to capacity 10 trigger catastrophic failure with 243% degradation, manifested through degenerate single-customer routing patterns. Convergence analysis identifies the underlying mechanism: low-capacity configurations exhibit chronic critic instability (loss 14.77 versus 0.68) due to extended decision horizons that fundamentally compromise the actor-critic learning dynamics.
Real-world validation across healthcare facility networks of varying scale (20, 50, 100 nodes) confirms that this threshold generalizes beyond synthetic distributions, with degradation intensifying systematically as problem size increases. The findings establish an operational feasibility boundary between capacities 10 and 20, below which PPO-based routing becomes deployment-infeasible despite producing formally valid solutions.
These results demonstrate that benchmark performance does not guarantee robustness under operational constraints, with immediate implications for deploying learning-based routing in rural logistics contexts where vehicle capacity is dictated by infrastructure limitations rather than optimization objectives.
Recommended Citation
Moreno, Freddy Giovanny Aviles, "OPERATIONAL FEASIBILITY OF REINFORCEMENT LEARNING FOR VEHICLE ROUTING UNDER HETEROGENEOUS FLEET CAPACITY CONSTRAINTS" (2026). Electronic Theses and Dissertations. 384.
https://digitalcommons.fau.edu/etd_general/384