Journal article
An empirically grounded analytical approach to hog farm finishing stage management: Deep reinforcement learning as decision support and managerial learning tool
Journal of operations management, Vol.71(4), pp.426-446
06/2025
DOI: 10.1002/joom.1342
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 heuristic currently used in practice. 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. We study how farmers decide which hogs to sell, to whom, and when, in the last stage of the hog growth cycle. We use a state‐of‐the‐art machine learning method called Deep Reinforcement Learning (DRL) to find the best way to make these decisions. The DRL model is trained on real‐world data from a large U.S. farm. The DRL model helps farmers make more money than current practices. We introduce the concept of "managerial learning" to extract easy‐to‐understand insights from complex data. We present a simple and effective decision‐making rule for farmers, derived from the DRL model.
Details
- Title: Subtitle
- An empirically grounded analytical approach to hog farm finishing stage management: Deep reinforcement learning as decision support and managerial learning tool
- Creators
- Panos Kouvelis - Washington University in St. LouisYe Liu - Syracuse UniversityDanko Turcic - University of California, Riverside
- Resource Type
- Journal article
- Publication Details
- Journal of operations management, Vol.71(4), pp.426-446
- DOI
- 10.1002/joom.1342
- ISSN
- 0272-6963
- eISSN
- 1873-1317
- Number of pages
- 21
- Language
- English
- Date published
- 06/2025
- Academic Unit
- Business Analytics
- Record Identifier
- 9985217136202771
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