Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics
Abstract
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.
Keywords
Citation
Abedi, M., Seidgar, H. and Fazlollahtabar, H. (2017), "Hybrid scheduling and maintenance problem using artificial neural network based meta-heuristics", Journal of Modelling in Management, Vol. 12 No. 3, pp. 525-550. https://doi.org/10.1108/JM2-02-2016-0011
Publisher
:Emerald Publishing Limited
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