|Ph.D Student||Shnits Boris|
|Subject||Dynamic Scheduling and Control of Flexible Manufacturing|
|Department||Department of Industrial Engineering and Management||Supervisors||Mr. David Sinreich (Deceased)|
|Mr. Jacob Rubinovitz (Deceased)|
This study suggests a methodology for the development of an FMS control system that is capable of coping with the dynamic environment the FMS operates in by addressing the following sources of variability: changes in the production environment, multiple and changing system evaluation criteria and the system redundancy manifested by alternative production routes.
The proposed control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level.
In this study four different triggering methods for the proposed control mechanism were suggested and implemented. The main difference between these methods is the timing and conditions needed to activate the decision making process. It was concluded that the activation of the decision making process right before a resource becomes available is the preferable triggering policy for the suggested control methodology.
The efficiency of the proposed control methodology was evaluated and compared to individual scheduling rules/policies and to an adaptive single-criteria scheduling method. The results obtained demonstrate the superiority of the suggested control methodology for different shop-floor conditions as well as its capability to cope with a fast changing environment. The analysis clearly shows that in a dynamic environment it is important not only to select a good scheduling rule/policy, but also to determine an appropriate decision criterion according to which the performance of each scheduling rule/policy is measured.
The suggested dynamic control system was also compared to a static control system operated according to the predefined fixed schedule. The results obtained demonstrate that as long as the variation in the production environment is low the static scheduling approach achieves comparable performance to the dynamic scheduling approach. On the other hand, when the system volatility is high, especially when the system is prone to machine failure, a dynamic scheduling approach is by far the most superior control strategy.