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Heuristic-based neural networks for stochastic dynamic lot sizing problem

  • Ercan Şenyiğit
    ,
  • Muharrem Düğenci
    ,
  • Mehmet Emin Aydin
    ,
  • Mithat Zeydan
Research Output: Contribution to journal Article Peer-review

Open access

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 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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 1332-1339

Journal (Volume, Issue Number)

Applied Soft Computing (Volume 13, Issue 3)

Publication milestones

  • Published - 18/05/2012

Publication status

Published - 18/05/2012

ISSN

1568-4946

External Publication IDs

  • handle.net: 10547/224518
  • Scopus: 84881663536

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