Multi-Criteria Decision Making Using Reinforcement Learning and its Application to Food, Energy, and Water Systems (Fews) Problem
Abstract
Multi-criteria decision making (MCDM) methods have evolved over the past several decades. In today’s world with rapidly growing industries, MCDM has proven to be significant in many application areas. In this study, a decision-making model is devised using reinforcement learning to carry out multi-criteria optimization problems. Learning automata algorithm is used to identify an optimal solution in the presence of single and multiple environments (criteria) using pareto optimality. The application of this model is also discussed, where the model provides an optimal solution to the food, energy, and water systems (FEWS) problem.
Degree
M.Sc.
Advisors
Mukhopadhyay, Purdue University.
Subject Area
Artificial intelligence|Industrial engineering|Information science|Operations research
Off-Campus Purdue Users:
To access this dissertation, please log in to our
proxy server.