Modeling of job sequencing rule on shop-floor by machine learning techniques

Abstract

Production simulation is useful to predict and optimize future production, but it requires effort and expertise to create accurate simulation models. For instance, operational control rules, such as job sequencing rules and resource assignment rules, are modeled based on interviews with shop-floor managers and some assumptions since those rules are tacit in general. Since operational control rules determine the dynamic behavior of production systems, it is important to model these rules accurately. In this paper, we consider a data-driven approach to model operational control rules. We develop job sequencing rule identification methods that automatically model operational control rules from historical production data using machine learning techniques. These methods are evaluated based on accuracy and robustness against uncertainty in human decision making using computational experiments and actual factory data.

Keywords

• production simulation
• dispatching rule
• job sequencing rule
• system identification
• machine learning