Multi-product scheduling through process mining: bridging optimization and machine process intelligence

Small and medium enterprises (SMEs) may not have the maturity to put forward and unfold all the benefits from an ERP based system, a vital tool for production planning. Manufacturing ubiquitous trends, however, are more approachable to SMEs, and even the more affordable tools could be of great advantage. In this paper we propose an algorithmic framework that uses process mining tools to extract the underlying industrial process via Petri nets, and then retrieve all product tree necessary information to perform the multi-level scheduling. A faster solution decoding is proposed, for algorithms that uses random-keys. Computational experiments show that the new decoding is faster than the usual, leading to promising new paths on its future uses.

Automated summary

This study introduces an algorithmic framework that utilizes process mining tools to extract industrial processes and retrieve necessary product tree information for multi-level scheduling. A faster decoding solution is proposed for algorithms using random keys. Computational experiments demonstrate that the new decoding is quicker than the conventional approach, opening up promising new pathways for future research.