A fuzzy collaborative forecasting approach based on XAI applications for cycle time range estimation

Estimating the cycle time ranges of jobs is a critical task in a factory. this study proposes a fuzzy collaborative intelligence (FCI) approach to improve the precision of cycle time range estimation. In the proposed methodology, a DNN is first built to accurately predict the cycle time of a job. A random forest (RF) is then constructed to explain the DNN. Each decision tree of the RF is fuzzified to estimate the cycle time ranges of the jobs learned by the decision tree. A fuzzy collaboration mechanism is also established between decision trees to narrow the cycle time ranges. The proposed methodology is novel because the RF is not applied to predict job cycle times but used to explain and fuzzify the DNN without solving complex nonlinear programming problems. The proposed methodology has been applied to a real case. According to the experimental results, the FCI approach improved the estimation precision by 18%.