2024-11-25 カリフォルニア大学リバーサイド校(UCR)
A hog farm in Elma, Iowa. H(Photo by Scott Olson/Getty Images)
＜関連情報＞
- https://news.ucr.edu/articles/2024/11/25/ai-optimizes-hog-farming-profitability
- https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4617964
養豚場の仕上げ段階管理への経験的根拠に基づく分析（EGA）アプローチ： 意思決定支援および経営学習ツールとしての深層強化学習 An Empirically Grounded Analytical (EGA) Approach to Hog Farm Finishing Stage Management: Deep Reinforcement Learning as Decision Support and Managerial Learning Tool
Panos Kouvelis,Ye Liu,Danko Turcic
Social Science Research Network Last revised: 29 Oct 2024
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.
