Reconfigurable Manufacturing System Design Using a Genetic Algorithm

In the modern context governed by Industry 4.0, Reconfigurable Manufacturing Systems (RMSs) rose as an effective production strategy able to cope with the increased product variety, the dynamic market demand and the need for flexible production batches. The manufacturing environment is usually made of a set of intelligent machines, i.e. Reconfigurable Machine Tools (RMTs), consisting of basic and auxiliary modules, which allow performing different operations. In this context, this paper proposes an optimization model for the dynamic design of RMSs with alternative part routing and multiple time periods, aiming at determining the part routing mix and the auxiliary module allocation best balancing the part flows among RMTs and the effort to install the modules on the machines. The model is solved through the application of a genetic algorithm applying different crossover operators and different threshold values for the occurrence of crossover and mutation processes. Results from the considered instance highlight that the two point crossover operator allows achieving the lowest fitness value, i.e. the lowest value of the defined objective function, getting a manufacturing system configuration characterized by low inter-cell part flow and machine reconfiguration time.