Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics

Purpose - The purpose of this paper is to present a new mathematical model for the unrelated parallel machine scheduling problem with aging effects and multi-maintenance activities. Design/methodology/approach - The authors assume that each machine may be subject to several maintenance activities over the scheduling horizon and a machine turn into its initial condition after maintenance activity and the aging effects start anew. The objective is to minimize the weighted sum of early/tardy times of jobs and maintenance costs. Findings - As this problem is proven to be non-deterministic polynomial-time hard (NP-hard), the authors employed imperialist competitive algorithm (ICA) and genetic algorithm (GA) as solution approaches, and the parameters of the proposed algorithms are calibrated by a novel parameter tuning tool called Artificial Neural Network (ANN). The computational results clarify that GA performs better than ICA in quality of solutions and computational time. Originality/value - Predictive maintenance (PM) activities carry out the operations on machines and tools before the breakdown takes place and it helps to prevent failures before they happen.