Impact of Various Priority Scenarios on a Fuzzy-Simulated Flowshop Scheduling Model

One of the main factors effective on Manufacturing Lead Time (MLT) of a flow shop system is the resources’ queue priority rules which has not been discussed well in literaturespecially in the case of multiple queue systems. In this paper a real-world application of flow shop system has been simulatedand analyzed in crisp and fuzzy states. α-cuts method and fuzzy probabilistic functions are used to perform the fuzzy simulation,taking into account the impreciseness of mostly process times. Amathematical and graphical statistics analysis is performed on data acquired from variety of priority scenarios applied in the simulated models. It is then observed and conjectured that the resources with the biggest queue length, average waiting time and resource utility percentage, have attracted influence from priority scenarios in altering the MLT, more than any otherresources. Eventually an Artificial Neural Networks (ANN) optimization algorithm is applied for training the simulated flow shop system in order to figure out any variations of MLT due to priority scenario changes, in future analyses. The outputs imply that the priority rules; Last in First out (LIFO) and First in First out (FIFO) are more likely to cause variations in MLT than the other ones.