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An Empirically Grounding Analytics (EGA) Approach to Hog Farm Finishing Stage Management: Deep Reinforcement Learning as Decision Support and Managerial Learning Tool
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An Empirically Grounding Analytics (EGA) Approach to Hog Farm Finishing Stage Management: Deep Reinforcement Learning as Decision Support and Managerial Learning Tool

Panos Kouvelis, Ye Liu and Danko Turcic
SSRN
SSRN
11/28/2023
DOI: 10.2139/ssrn.4617964
url
https://doi.org/10.1002/joom.1342View
Published (Version of record) This article has now been published in a journal and has been peer-reviewed by subject experts. This version may differ significantly from the preprint version. Access restricted to faculty, staff and students
url
https://doi.org/10.2139/ssrn.4617964View
Preprint (Author's original) This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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Abstract

In hog farming, optimizing hog sales is a complex challenge due to uncertain factors such as hog availability, market prices, and operating costs. This study uses a Markov Decision Process (MDP) to model these decisions, revealing the importance of the final weeks in profit management. The MDP's intractability due to the curse of dimensionality leads us to employ Deep Reinforcement Learning (DRL) for optimization. Using real-world and synthetic data, our DRL model outperforms existing practices. However, it lacks interpretability, hindering trust and legal compliance in the food industry. To address this, we introduce ``managerial learning,'' extracting actionable insights from DRL outputs using classification trees that would have been difficult to obtain otherwise. We leverage these insights to devise a smart heuristic that significantly beats the current heuristic. This study has broader implications for operations management, where DRL can solve complex dynamic optimization problems that are often intractable due to dimensionality. By applying methods such as classification trees and DRL, one can scrutinize solutions for actionable managerial insights that can enhance existing practices with straightforward planning guidelines.

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