An Integrated Artificial Bee Colony Algorithm for Scheduling Jobs and Flexible Maintenance with Learning and Deteriorating Effects

Abstract

In this paper, we address two versions of the permutation flowshop scheduling problem (PFSP) with makespan minimization under availability constraints with learning and deteriorating effects. Availability constraints are due to flexible maintenance activities scheduled based on prognostics and health management (PHM) results. In the first study, human learning effect is considered and position-dependent model is applied to generate variable maintenance processing times. In the second one, besides learning effect, time-dependent machine deteriorating jobs are assumed. Since the PFSP is proven to be NP-complete, improved artificial bees colony algorithms were proposed. Intense computational experiments are carried out on Taillard’s well known benchmarks, to which we add both PHM and maintenance data. The results of comparison and experiments show the efficiency of our algorithms.