Reinforcement Learning With Data Envelopment Analysis and Conditional Value-At-Risk for the Capacity Expansion Problem

• Published in: IEEE transactions on engineering management Vol. 71; pp. 1 - 12
• Main Authors: Lee, Chia-Yen, Chen, Yen-Wen
• Format: Journal Article
• Language: English
• Published: New York IEEE 2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Capacity expansion; Capacity planning; conditional value-at-risk (CVAR); Costs; Data analysis; Data envelopment analysis; data envelopment analysis (DEA); Effectiveness; efficiency and effectiveness measure; Empirical analysis; Evaluation; Indexes; Machine learning; Optimization; Power generation; Productivity; reinforcement learning (RL); Resource allocation; Risk aversion; risk-averse decision; Uncertainty

The capacity expansion problem is solved by accurately measuring the existing demand-supply mismatch and controlling the emissions output, considering multiple objectives, specific constraints, resource diversity, and resource allocation. This article proposes a reinforcement learning (RL) framework...