Abstract
The volatile globalised markets and mass customisation greatly affect modern industries. In this context, the timely and accurate manufacturing network design is an important strategic decision. However, this proven NP-hard problem cannot be approached by exhaustive methods. This research work aims to support the decision-makers by introducing a Genetic Algorithm for the identification of near optimum manufacturing network configurations. The examined problem tackles the multi-stage manufacturing network design for single customised products, through satisfaction of multiple objectives. The performance of the alternative designs deriving from the GA is compared to the results of an intelligent search algorithm with adjustable control parameters, and with an exhaustive search method. The conflicting criteria for evaluating the alternative configurations include cost, time, quality and environmental parameters. The applicability of the proposed method is validated through a case study, utilising data acquired from the automotive sector.
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