Modern manufacturing environments seek adaptive solutions to integrate predictive maintenance into job shop scheduling. This paper conducts a comparative study of various Genetic Algorithm (GA) based approaches for Integrated Scheduling and Predictive Maintenance Planning (ISPMP). The study assesses the performance of four GAs across three job load conditions (i.e., Low, Medium, and High), considering both single and multiple machine breakdown scenarios. The results highlight the standard GA's potential for near-real-time scheduling solutions, emphasizing its adaptability and scalability. Bridging the theoretical innovations with practical applications, this research highlights an adaptive production planning paradigm, championing the role of GA-enabled simulation and decision support systems in the ever-evolving industrial landscape.
A Comparative Study of Genetic Algorithms for Integrated Predictive Maintenance and Job Shop Scheduling
Elbasheer M.;Longo F.;Mirabelli G.;Padovano A.;Solina V.
2023-01-01
Abstract
Modern manufacturing environments seek adaptive solutions to integrate predictive maintenance into job shop scheduling. This paper conducts a comparative study of various Genetic Algorithm (GA) based approaches for Integrated Scheduling and Predictive Maintenance Planning (ISPMP). The study assesses the performance of four GAs across three job load conditions (i.e., Low, Medium, and High), considering both single and multiple machine breakdown scenarios. The results highlight the standard GA's potential for near-real-time scheduling solutions, emphasizing its adaptability and scalability. Bridging the theoretical innovations with practical applications, this research highlights an adaptive production planning paradigm, championing the role of GA-enabled simulation and decision support systems in the ever-evolving industrial landscape.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
