Reinforcement learning for procurement agents of factory of the future

Factory of the future is emerging with the existence of new modeling and application tools that can both simulate and manage the whole production process in an autonomous, intelligent and interactive manner. Holonic modeling and its software correspondence agent oriented technology provides us with these tools. Especially the use of learning algorithms trying to optimize the behaviors of software agents within a dynamic environment is the key factor in reaching the required properties. In this paper, we use the well known Q learning algorithm of reinforcement learning (RL) in evaluating production orders within a supply chain management (SCM) framework and making decisions with respect to these evaluations. We introduce our SCM model and show that RL performs better than traditional tools for dynamic problem solving in daily business. We also show cases where RL fails to perform efficiently.