FMS scheduling and control: Learning to achieve multiple goals

摘要

This paper describes FMS-GDCA, a loosely coupled system using a machine learning paradigm known as goal-directed conceptual aggregation (GDCA) and simulation to address the problem of Flexible Manufacturing System (FMS) scheduling for a given configuration and management goals. The main advantage of FMS-GDCA is that it provides a manufacturing manager with an extremely flexible and goal-seeking control mechanism that allows for a continuous improvement in decision outcomes. The manager can choose a goal or a combination of goals or a combination of goals or can prioritize the partial goals by assigning weights. Given the goals, FMS-GDCA attempts to achieve them to the best of its ability. If it cannot meet the goals due to its lack of knowledge, it will acquire the relevant knowledge from data and solve the problem. The results indicate that FMS-GDCA can consistently produce improved overall performance over the traditional scheduling techniques.