Towards an artificial immune system for scheduling jobs and preventive maintenance operations in flowshop problems

This paper investigates permutation flowshop problem with preventive maintenance (PM). The objective functions are to minimize the total completion time for the production part and the total earliness/tardiness for PM part. The resolution consists of two steps: the one consists on scheduling production jobs using an artificial immune algorithm (AIA); the second one consists on deploying PM operations, taking the production schedule as a mandatory constraint of resources unavailability in the resolution of the problem. Furthermore, we use the principles of vaccination and receptor editing in order to strengthen search ability. The efficiency of the proposed AIAs with respect to minimization of makespan for the production part and performance loss after PM insertion, is compared to some referred in the related scheduling literature metaheuristics. Simulation results on both standard PFSP problems and non- standard integrated PFSP with PM problems show the superiority of our proposed algorithms.