A hybrid artificial neural network: computer simulation approach for scheduling a flow shop with multiple processors. (3rd December 2010)

• Record Type: Journal Article
• Authors: Azadeh, Ali; Naghavi, Arash; Moghaddam, Mohsen
• Is Part Of: International journal of industrial and systems engineering. Volume 7:Number 1(2011)
• Page Start: 66
• Page End: 89
• Publication Date: 2010-12-03
• Subjects: ANN -- artificial neural network -- meta-modelling -- flow shop -- simulation -- MACD -- multiattribute combinatorial dispatching -- optimisation; Systems engineering -- Periodicals; Industrial engineering -- Periodicals; 620.001171
• Languages: English
• ISSNs: 1748-5037

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.