Industrial Performance Optimization Using Artificial Neural Networks and Analytical Hierarchy Process

Abstract

Performance optimization of an industrial system is constrained by several factors as efficient decision-making actions. Decision-makers are continuously facing complex problems challenging their multi-objective optimization capabilities while satisfying customer preferences. However, A first literature review showed that the majority of models established in this field are based on multicriteria analysis which is less suitable for the complex industrial context. This paper proposes a new model for decision-making to enhance industrial systems performance based on Artificial Neural Networks, Analytic Hierarchy Process and balanced scorecard approach, to identify the best decision from a set of available options using real-time performance data. Accuracy of the proposed model is validated through an empirical case study and a survey conducted among several different industries. Current research proposes a decision-making tool to assist decision in performance based on a set of independent variables, future research may use other artificial intelligence tools to enhance this approach.