An empirically grounded analytical approach to hog farm finishing stage management: Deep reinforcement learning as decision support and managerial learning tool

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.