In production environments, such as Flexible Manufacturing Systems (FMS), the production schedule can be disturbed by the occurrence of unplanned events. Machines stop due to failures, maintenance, tool changes due to wear, or tool reassignments. Because of the changing nature of an FMS, a global and static schedule created at time zero can only provide a rough guideline for the control activity. The rescheduling process, however, can be costly and time consuming. An updated schedule may require new tool assignments with more setup time. Hence, ideal rescheduling frequency is another unknown factor. In dynamic production environments, like FMS, selecting fixed rescheduling points is not appropriate.
In this study, the possibility of defining a dynamic measure of flexibility to help determine an appropriate time for rescheduling an FMS has been investigated. Flexibility is defined as a function of Capability and Capacity. Accordingly, two metrics have been developed to monitor the capability and capacity efficiency of each machine in the system for responding to the dynamic system status. The value of each metric falls between 0 and 1 at all times. Higher values in the capability metric signify better machine selection and part distribution strategies among the machines. Higher values for the capacity metric indicate higher machine utilization in the production plan. Based on the interaction between the metrics and their respective behavior in the system, four states have been identified and characterized.
In order to evaluate the significance, accuracy, and sensitivity of the developed metrics, an FMS simulation model is employed. Parameters such as demand, part processing times, and available machining times are known for the period of scheduling. Then, based on a feasible production plan, initial tool assignments and tool addition strategies are determined. The behavior of the metrics under different scenarios are studied and analyzed. If the value of the metric falls below a predefined value, indicating of a lower level of system flexibility, the simulation is paused and the rescheduling procedure is initiated. Then simulation continues, using the revised schedule. This research contributes to the verification of an adequate metric to determine the optimal rescheduling point.
