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An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach

Author

Listed:
  • Hany Seidgar
  • M. Zandieh
  • Iraj Mahdavi

Abstract

This paper investigates integrated two-stage assembly flow shop problem with preventive maintenance (PM) activities under the multi-objective optimisation approaches. Reliability models are employed to carry out the maintenance activities. This paper attempts to find the appropriate sequence of jobs on machines in order to minimise the makespan and determining when to perform the PM activities in order to minimise the system unavailability. As this problem is proven to be NP-hard two multi-objective optimisation methods that are named non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are employed to find the Pareto-optimal front. The parameters of proposed algorithms are calibrated by artificial neural network (ANN) and the performances of the algorithms on the problem of various sizes are analysed based on four metrics. The computational results reveal NRGA is statistically better than NSGA-II.

Suggested Citation

  • Hany Seidgar & M. Zandieh & Iraj Mahdavi, 2017. "An efficient meta-heuristic algorithm for scheduling a two-stage assembly flow shop problem with preventive maintenance activities and reliability approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 26(1), pages 16-41.
    • Handle: RePEc:ids:ijisen:v:26:y:2017:i:1:p:16-41
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