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 t...

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Bibliographic Details
Published in:Journal of modelling in management 2017-01, Vol.12 (3), p.525-550
Main Authors: Abedi, Mehdi, Seidgar, Hany, Fazlollahtabar, Hamed
Format: Article
Language:English
Subjects:
Aging
Algorithms
Cutting tools
Fines & penalties
Heuristic
Integer programming
Job shops
Maintenance management
Manufacturing
Mathematical models
Neural networks
Preventive maintenance
Production scheduling
Scheduling
Sequential scheduling
Steel production
Studies
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Summary: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.
ISSN:1746-5664
1746-5672
DOI:10.1108/JM2-02-2016-0011