Heuristic-based neural networks for stochastic dynamic lot sizing problem
AffiliationUniversity of Bedfordshire
SubjectsG400 Computer Science
heuristic-based learning approaches
revised silver meal
revised least unit cost
feed-forward neural network model
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AbstractMulti-period single-item lot sizing problem under stochastic environment has been tackled by few researchers and remains in need of further studies. It is mathematically intractable due to its complex structure. In this paper, an optimum lot-sizing policy based on minimum total relevant cost under price and demand uncertainties was studied by using various artificial neural networks trained with heuristic-based learning approaches; genetic algorithm (GA) and bee algorithm (BA). These combined approaches have been examined with three domain-specific costing heuristics comprising revised silver meal (RSM), revised least unit cost (RLUC), cost benefit (CB). It is concluded that the feed-forward neural network (FF-NN) model trained with BA outperforms the other models with better prediction results. In addition, RLUC is found the best operating domain-specific heuristic to calculate the total cost incurring of the lot-sizing problem. Hence, the best paired heuristics to help decision makers are suggested as RLUC and FF-NN trained with BA.
CitationŞenyiğit, E., Düğenci, M., Aydin, M.E. & Zeydan, M. (2012) 'Heuristic-based neural networks for stochastic dynamic lot sizing problem', Applied Soft Computing, .
JournalApplied Soft Computing
DescriptionThis is the final version of the manuscripted accepted before production by the publisher
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- Creative Commons
Except where otherwise noted, this item's license is described as Archived with thanks to Applied Soft Computing