Performance optimization of integrated resilience engineering and lean production principles

Abstract

Performance optimization of integrated resilience engineering and lean production.An intelligent algorithm based ANN, DEA and statistical methods.Previous studies neglected the combined impacts of both factors.Data envelopment analysis is used for validation of results.Considers a real-world pipe manufacturer as case study. This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that pull system and fault tolerant among lean and IRE factors, respectively have been implemented inappropriately, while other factors are either suitably executed or ineffective.