Abstract
Current manufacturing industries are experiencing a paradigm shift towards more flexibility to respond quickly and efficiently to constant changing customers requirements, new technologies and increasing product variety. In today’s manufacturing environment it is necessary to make diverse decisions, which mainly concern to all stages of the manufacturing activity: bidding, negotiating, order acceptance, product design, processes planning and jobs scheduling. The decision consists in selecting the most suitable alternative from the potential ones, therefore a comparative assessment of the potential alternatives is required. In this paper we present a different approach to performing the comparative assessment, based on modeling with neural networks. Neural networks are used for establishing the dependence relations between the key process parameters and measured datasets e.g. cost, time span, consumed energy etc. A numerical simulation for comparative assessment by modeling with neural networks, with the help of an instances artificial database is also presented.
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