Hybrid Data Science and Reinforcement Learning in Data Envelopment Analysis

Chia-Yen Lee (), Yu-Hsin Hung () and Yen-Wen Chen ()
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Chia-Yen Lee: National Taiwan University
Yu-Hsin Hung: National Taiwan University
Yen-Wen Chen: National Cheng Kung University

A chapter in Data-Enabled Analytics, 2021, pp 93-122 from Springer

Abstract: Abstract This study proposes a hybrid data science (DS) framework and reinforcement learning (RL) in data envelopment analysis (DEA). The framework supports the functional form identification of the production frontier and the RL derives the optimal resource reallocation policy which guides the productivity improvement. In fact, both DS and RL techniques complement efficiency analysis. Emphasizes on planning over evaluation, we use data generating process (DGP) and an empirical dataset of power plants to drive productivity to validate the benefits of the hybrid DS framework and RL, respectively. Based on the results, we find that the hybrid DS framework and RL can enhance the interpretation of the production frontier and identify the optimal resource policy.

Keywords: Data envelopment analysis (DEA); Data science; Reinforcement learning; Data generating process; Symbolic regression (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-75162-3_4

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DOI: 10.1007/978-3-030-75162-3_4

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