EE-RJMTFN: A novel manufacturing risk evaluation method for alternative resource selection in cloud manufacturing

Abstract

Cloud manufacturing is a new service-oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on demand. Cloud manufacturing is fully recognized as one of the most important manufacturing modes yet created and has received considerable attention in recent years. In practical scenarios, due to the diversity and uncertainty of manufacturing resource, the cloud manufacturing platform should have the ability of manufacturing risk evaluation to search a suitable alternative resource when certain original manufacturing nodes failed suddenly. Such ability may reduce the probability of the alternative resource failing to the lowest and avoid the manufacturing process/system from complete collapse. It ensures the cloud manufacturing be a reliable, robust and flexible manufacturing system with strong self-repair ability. In this article, a new risk evaluation method termed Extension Evaluation based on Reciprocal Judging Matrix with Triangular Fuzzy Numbers (EE-RJMTFN) is proposed. According to the frame of extension theory, the proposed method provides not only the risk level but also the related correlation degree that describes the deviation from the standard risk level. Moreover, the risk index weight definition based on reciprocal judging matrix with triangular fuzzy numbers can better meet user’s customized demand. The results of a case study illustrate the implementation of the proposed method.