Abstract
The complexity of a manufacturing system can be considered as the state of being difficult to understand, describe, predict and control the system. From the information, i.e. theoretic point of view, manufacturing system complexity is defined as the expected amount of information required to describe the state of a manufacturing system. The complexity of a manufacturing system is investigated by two important measures, i.e. static and dynamic. Static complexity is defined as the amount of information required to describe the expected states of a manufacturing system that are predicted to occur by the schedule, whereas dynamic complexity is defined as the amount of information required to describe the states of the manufacturing system that actually occur over time in operation. An information-theoretic approach is applied to study the complexity measurement of manufacturing systems, and the entropy-based measurement models are developed. As an application, the proposed theoretical models are used to investigate the effectiveness of schedules for manufacturing systems, where the concepts of maximum feasible schedule horizon and schedule adherence are introduced to quantitatively evaluate the effectiveness of schedules. An example demonstrates the validity of the proposed methodology.
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