Using ensemble and metaheuristics learning principles with artificial neural networks to improve due date prediction performance

Abstract

One of the common and important problems in production scheduling is to quote an attractive but attainable due date for an arriving customer order. Among a wide variety of prediction methods proposed to improve due date quotation (DDQ) accuracy, artificial neural networks (ANN) are considered the most effective because of their flexible non-linear and interaction effects modelling capability. In spite of this growing use of ANNs in a DDQ context, ANNs have several intrinsic shortcomings such as instability, bias and variance problems that undermine their accuracy. In this paper, we develop an enhanced ANN-based DDQ model using machine learning, evolutionary and metaheuristics learning concepts. Computational experiments suggest that the proposed model outperforms the conventional ANN-based DDQ method under different shop environments and different training data sizes.