Absnon-Factory of the future is emerging with the existence of now 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 wftwsre correspondence agent oriented technology provides UP with these toeis. Especially the w e of learning algdthmi trying to optimize the behaviors of software agenls within a dynamic environment is the key faclor 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) framewark and making decisions with respect to these evaluations. We introduce our SCM model and show that RL performs bener than trsditionai tools for dynamic problem solving in daily business. We also show cases where RL fails to perform emciently.
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