Sistema de soporte de decisiones de producción en un entorno flexible job shop basado en un modelo predictivo-reactivo sujeto a perturbaciones (2020)
Production scheduling under real-time events has high importance for the successful performance of real-world scheduling systems. Most manufacturing systems operate in dynamic environments vulnerable to various non-programmed real-time events which continuously forces revision and reconsideration of pre-established schedules. In an uncertain environment, efﬁcient ways to adapt current solutions to unexpected events, are preferable to solutions that soon become obsolete. This situation motivated the development of a decision support system that attempts to fill the gap between scheduling theory and practice. The developed prototype uses metaheuristics to generate a predictive schedule for an initial solution before execution. Then, whenever disruptions happen, like arrival of new tasks or cancelation of others, the decision support system starts updating the schedule through a reactive module that uses heuristics based on dispatching rules principles. The proposed system was tested in a simulated scenario of a real flexible manufacturing system located in Valenciennes (France), called AIP-PRIMECA Valenciennes. A disruption was carried out during the execution in the manufacturing system in order to demonstrate the effectiveness of the proposed model.