A hybrid artificial neural network: computer simulation approach for scheduling a flow shop with multiple processors
by Ali Azadeh, Arash Naghavi, Mohsen Moghaddam
International Journal of Industrial and Systems Engineering (IJISE), Vol. 7, No. 1, 2011

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.

Online publication date: Sat, 31-Jan-2015

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