2.50
Hdl Handle:
http://hdl.handle.net/10547/224518
Title:
Heuristic-based neural networks for stochastic dynamic lot sizing problem
Authors:
Şenyiğit, Ercan; Düğenci, Muharrem; Aydin, Mehmet Emin ( 0000-0002-4890-5648 ) ; 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 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.
Affiliation:
University of Bedfordshire
Citation:
Şenyiğit, E., Düğenci, M., Aydin, M.E. & Zeydan, M. (2012) 'Heuristic-based neural networks for stochastic dynamic lot sizing problem', Applied Soft Computing, .
Publisher:
Elsevier
Journal:
Applied Soft Computing
Issue Date:
18-May-2012
URI:
http://hdl.handle.net/10547/224518
DOI:
10.1016/j.asoc.2012.02.026
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S1568494612001196
Type:
Article
Language:
en
Description:
This is the final version of the manuscripted accepted before production by the publisher
ISSN:
15684946
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorŞenyiğit, Ercanen_GB
dc.contributor.authorDüğenci, Muharremen_GB
dc.contributor.authorAydin, Mehmet Eminen_GB
dc.contributor.authorZeydan, Mithaten_GB
dc.date.accessioned2012-05-18T08:13:54Z-
dc.date.available2012-05-18T08:13:54Z-
dc.date.issued2012-05-18-
dc.identifier.citationŞenyiğit, E., Düğenci, M., Aydin, M.E. & Zeydan, M. (2012) 'Heuristic-based neural networks for stochastic dynamic lot sizing problem', Applied Soft Computing, .en_GB
dc.identifier.issn15684946-
dc.identifier.doi10.1016/j.asoc.2012.02.026-
dc.identifier.urihttp://hdl.handle.net/10547/224518-
dc.descriptionThis is the final version of the manuscripted accepted before production by the publisheren_GB
dc.description.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.en_GB
dc.language.isoenen
dc.publisherElsevieren_GB
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S1568494612001196en_GB
dc.rightsArchived with thanks to Applied Soft Computingen_GB
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectG400 Computer Scienceen_GB
dc.subjectheuristic-based learning approachesen
dc.subjectgenetic algorithmen
dc.subjectbee algorithmen
dc.subjectrevised silver mealen
dc.subjectrevised least unit costen
dc.subjectcost benefiten
dc.subjectfeed-forward neural network modelen
dc.subjectneural networksen
dc.titleHeuristic-based neural networks for stochastic dynamic lot sizing problemen
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen_GB
dc.identifier.journalApplied Soft Computingen_GB
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