Tailored Genetic Algorithm for Scheduling Jobs and Predictive Maintenance in a Permutation Flowshop

Abstract

We tackle in this paper the Permutation Flow-shop Scheduling Problem (PFSP) with predictive maintenance interventions. The objective is to propose an integrated model that coordinates production schedule and predictive maintenance planning so that the total time to complete the schedule after predictive maintenance insertion is minimized. Predictive maintenance interventions are scheduled based on Prognostics and Health Management (PHM) results using a new proposed heuristic. To jointly establish an integrated scheduling of production jobs and predictive maintenance actions, we propose a tailored genetic algorithm incorporating properly designed operators. Computational experiments carried out on Taillard well known benchmarks, to which we add both PHM and maintenance data, show the efficiency of the newly proposed maintenance planning heuristic and genetic algorithm.