Fuzzified deep neural network ensemble approach for estimating cycle time range

Because of the high uncertainty associated with predicting the cycle time of a job in a complex manufacturing system, this task is a challenge for production planners. To replace an inaccurate cycle time forecast, the range of cycle time is estimated. To estimate the cycle time range of a job as precisely as possible, a fuzzified deep neural network (FDNN) ensemble approach is proposed in this paper. This approach involves the following steps. First, a deep neural network (DNN) is constructed to predict the cycle time of a job. The parameters of the DNN are then fuzzified to generate a fuzzy cycle time forecast that contains the actual cycle time. In contrast to existing methods that involve fuzzifying multiple parameters simultaneously, which is computationally intensive, the proposed methodology involves fuzzifying parameters independently to facilitate problem-solving. Subsequently, the cycle time ranges estimated by fuzzifying various parameters are aggregated through fuzzy intersection. The proposed FDNN ensemble approach was applied to a real case to validate its effectiveness. The experimental results indicated that the precision of cycle time range estimation was up to 38% higher with the FDNN ensemble approach than with the fuzzy backpropagation network approach.(c) 2022 Elsevier B.V. All rights reserved.