Part family formation method for delayed reconfigurable manufacturing system based on machine learning

Delayed reconfigurable manufacturing system (D-RMS), a subclass of reconfigurable manufacturing system (RMS), were proposed to solve the convertibility problems of RMS. As a part family-oriented manufacturing system paradigm, D-RMS should concern delayed reconfiguration at the outset of part family formation. To bring the characteristics of delayed reconfiguration into the part family of D-RMS, an exclusive part family formation method for D-RMS based on machine learning is proposed in this paper. Firstly, a similarity coefficient that considers the characteristics of D-RMS is put forward based on the operation sequence of part. The positions of the common operations in the corresponding operation sequences are investigated. The more former common operations there are, the more probability it is that the parts are grouped into the same part family. The relative positions of the common operations are considered by proposing a concept of the longest relative position common operation subsequence (LPCS). Additionally, the position difference and discontinuity of the LPCSs in the corresponding operation sequences are analyzed. A similarity coefficient is proposed that incorporates the abovementioned factors. Secondly, a machine learning method named K-medoids is adopted to group parts into families based on the calculation result of the similarity coefficient. Finally, a case study is presented to implement the proposed part family formation method for D-RMS, where the effectiveness of the proposed method is verified through comparison.