We address the Permutation Flow-Shop Scheduling Problem with Predictive Maintenance presented by Varnier and Zerhouni (2012), that consists in finding the integrated schedule for production and maintenance tasks such that the total production time and the advance of maintenance services are minimized. Predictive maintenance services are scheduled based on a prognostics system that is able to provide the remaining useful life of a machine. To solve this problem, we propose a local search method with neighborhoods specifically tailored for maintenance interventions. Computational experiments performed on generated benchmarks demonstrate the effectiveness and scalability of our method with respect to an exact technique based on the mathematical model proposed by Varnier and Zerhouni (2012).
