Robust optimization of manufacturing systems flexibility

Mass customization requires frequent product changeovers thus leading to the need of manufacturing systems endowed with flexibility and reconfiguration capabilities, in order be robust changes production scenario. Therefore, companies face a relevant risk when taking strategic decisions about which system resources should acquired. This can mitigated by exploiting performance evaluation models, such as analytical models Discrete Event Simulation, that are effectively adopted estimate possible configurations. However, current decision-support tools for optimizing configurations only loosely coupled hence undermining actual optimization itself, even more if requirements may evolve future. work presents an methodology support configuration subject evolving requirements. The integrates stochastic model lines into mixed integer programming problem, means linearization. advantage using proposed is shown on line where buffer capacities machine capabilities have jointly optimized, minimize costs satisfy target performance.