Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment

Industry 4.0 is a modern approach that aims at enhancing the connectivity between different stages of production process and requirements consumers. This paper addresses relevant problem for both flow shop literature: missing operations scheduling problem. In general, in order to reduce computational effort required solve problems only permutation schedules (PFS) are considered, i.e., same job sequence used all machines involved. However, considering PFS not constraint based on real-world conditions industrial environments, it simplification strategy frequently literature. Moreover, non-permutation (NPFS) orderings may be most real systems, can line, since NPFS solutions usually outperform ones. this work, novel mathematical formulation minimize total tardiness resolution method, which considers (the more computationally expensive) solutions, presented with operations. The solution has two stages. First, Genetic Algorithm, applied resulting then improved second stage by means Simulated Annealing algorithm expands search space solutions. experimental tests were performed set instances varying proportions operations, as usual environment. results show clearly