Heuristic-based neural networks for stochastic dynamic lot sizing problem

?enyi?git, Ercan; D�?enci, Muharrem; Aydin, Mehmet E.; ?enyi?it, Ercan; D�?enci, Muharrem; Aydin, Mehmet Emin; Zeydan, Mithat

Abstract

Multi-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 heuristicbased 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. © 2012 Elsevier B.V. All rights reserved.

Citation

Aydin, M. E., Düǧenci, M., Şenyiǧgit, E., Şenyiğit, E., Düğenci, M., Aydin, M. E., & Zeydan, M. (2013). Heuristic-based neural networks for stochastic dynamic lot sizing problem. Applied Soft Computing, 13(3), 1332-1339. https://doi.org/10.1016/j.asoc.2012.02.026

• Journal Article Type: Article
• Publication Date: Jan 1, 2013
• Journal: Applied Soft Computing Journal
• Print ISSN: 1568-4946
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 13
• Issue: 3
• Pages: 1332-1339
• DOI: https://doi.org/10.1016/j.asoc.2012.02.026
• Keywords: stochastic lot-sizing, feed-forward neural networks, bee algorithm, genetic algorithms, taguchi methods
• Public URL: https://uwe-repository.worktribe.com/output/934562
• Publisher URL: http://dx.doi.org/10.1016/j.asoc.2012.02.026