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Solving multi-objective flexible flow-shop scheduling problem using teaching-learning-based optimisation embedded with maximum deviation theory

Author

Listed:
  • Raviteja Buddala
  • Siba Sankar Mahapatra
  • Manas Ranjan Singh

Abstract

Flexible flow-shop scheduling problem (FFSP) is an extended special case of basic flow-shop scheduling problem (FSP). FFSP is treated as complex NP-hard scheduling problem. A good scheduling practice enables the manufacturer to compete effectively in the marketplace. An efficient schedule should address multiple conflicting objectives so that customer satisfaction can be improved. In this work, a novel approach based on teaching-learning-based optimisation (TLBO) technique incorporated with maximum deviation theory (MDT) is applied to generate schedules that simultaneously optimise conflicting objective measures like makespan and flowtime. Results indicate that the proposed multi-objective TLBO (MOTLBO) outperforms non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective particle swarm optimisation (MOPSO) in majority of the problem instances.

Suggested Citation

  • Raviteja Buddala & Siba Sankar Mahapatra & Manas Ranjan Singh, 2022. "Solving multi-objective flexible flow-shop scheduling problem using teaching-learning-based optimisation embedded with maximum deviation theory," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 42(1), pages 39-63.
    • Handle: RePEc:ids:ijisen:v:42:y:2022:i:1:p:39-63
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