Multi-criteria decision making using reinforcement learning and its application to food, energy, and water systems (FEWS) problem

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.

Discipline thesis:degree_discipline

Computer & Information Science

Year dc:date.issued

2021

Author dc:creator

• Deshpande, Aishwarya

Advisor dc:contributor.advisor

• Mukhopadhyay, Snehasis

• Multi-criteria decision making
• Reinforcement learning
• Learning automata
• Pareto optimality

Statement dc:rights

• CC0 1.0 Universal

Licence dc:rights.uri

• http://creativecommons.org/publicdomain/zero/1.0/

Language dc:language.iso

en_US

Identifier URI

http://dx.doi.org/10.7912/C2/119

OAI identifier oai:identifier

oai:scholarworks.indianapolis.iu.edu:1805/27395