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

Ali Azadeh, Arash Naghavi and Mohsen Moghaddam

International Journal of Industrial and Systems Engineering, 2011, vol. 7, issue 1, 66-89

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.

Keywords: hybrid ANNs; artificial neural networks; metamodelling; flow shop scheduling; simulation; MACD; multiattribute combinatorial dispatching; optimisation; dispatching rules; modelling; multilayer ceramic capacitors; ceramic capacitor manufacturing; manufacturing lead times. (search for similar items in EconPapers)
Date: 2011
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