TY  - JOUR
AB  - 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.
VL  - 12
IS  - 3
SN  - 1746-5664
DO  - 10.1108/JM2-02-2016-0011
UR  - https://doi.org/10.1108/JM2-02-2016-0011
AU  - Abedi Mehdi
AU  - Seidgar Hany
AU  - Fazlollahtabar Hamed
PY  - 2017
Y1  - 2017/01/01
TI  - Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics
T2  - Journal of Modelling in Management
PB  - Emerald Publishing Limited
SP  - 525
EP  - 550
Y2  - 2024/09/19
ER  -