Date of Award
2017
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science : Modeling and Simulation
Committee Chair
Mikel D. Petty
Committee Member
Daniel Rochowiak
Committee Member
James J. Swain
Subject(s)
Discrete-time systems--Simulation methods, Reinforcement learning, Materials handling
Abstract
In this thesis, a discrete event simulation model of Steelcase's Athens facility is constructed to simulate material handling plans generated by an external reinforcement learning algorithm. The results of this simulation are consolidated into key performance parameters and provided as a reward to the reinforcement learning algorithm. The model interfaces developed for the reinforcement leaning algorithm are verified through several sets of tests. Validation of the model is conducted in two separate approaches, input model validation using truth data and output data validation using key performance parameters.
Recommended Citation
Preston, Ryan A., "Utilizing a discrete event simulation of material handling plans to calculate reinforcement learning rewards" (2017). Theses. 215.
https://louis.uah.edu/uah-theses/215