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A hybrid artificial neural network: computer simulation approach for scheduling a flow shop with multiple processors

Author

Listed:
  • Ali Azadeh
  • Arash Naghavi
  • Mohsen Moghaddam

Abstract

Depending on the characteristics of the manufacturing system and production objectives, dispatching rules have different efficiencies. In this regard, a multiattribute combinatorial dispatching (MACD) decision problem for scheduling a flow shop with multiple processors environment is presented in this paper. We propose a hybrid artificial neural network (ANN) simulation approach as a valid and superior alternative for solving the MACD decision problem. ANNs are one of the commonly used meta-heuristics and are a proven tool for solving complex optimisation problems. The hybrid approach is capable of modelling a non-linear and stochastic problem. Feed forward, multilayered neural network meta-models were trained through the back propagation learning algorithm to provide a complex MACD problem. The solution quality is illustrated by a case study from a multilayer ceramic capacitor manufacturing plant. The manufacturing lead times produced by the hybrid ANN simulation model turned out to be as valid and superior to the conventional simulation model.

Suggested Citation

  • Ali Azadeh & Arash Naghavi & Mohsen Moghaddam, 2011. "A hybrid artificial neural network: computer simulation approach for scheduling a flow shop with multiple processors," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 7(1), pages 66-89.
    • Handle: RePEc:ids:ijisen:v:7:y:2011:i:1:p:66-89
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