Promoting reverse logistics decisions using a new hybrid model based on deep learning and failure mode and effects analysis approaches

Simple item page
dc.contributor.authorAhmadkhan, Kamelia
dc.contributor.authorYazdani-Chamzini, Abdolreza
dc.contributor.authorBakhshizadeh, Alireza
dc.contributor.authorŠaparauskas, Jonas
dc.contributor.authorTurskis, Zenonas
dc.contributor.authorZeidyahyaee, Niousha
dc.contributor.otherEkonomická fakultacs
dc.date.accessioned2025-12-08T11:24:16Z
dc.date.available2025-12-08T11:24:16Z
dc.description.abstractThe problem of reusing and recycling the returned products plays a crucial role in mitigating waste. Therefore, authorities must make the best decision in such situations. However, this problem is a paradoxical decision because different components often conflict with each other, which can impact the decision-making process. The proposed framework uses sentiment analysis algorithms to help decision-makers adopt the best reverse logistics decision strategy based on customer feedback. The framework provides a procedure for extracting, categorizing, and analyzing customer opinions. It strategically decides in reverse logistics to increase profit, efficiency, and customer satisfaction while reducing the returned products, costs, and waste. The framework has a high potential for utilization in a wide range of industries, so the probability of a biased opinion resulting from the limitation of taking into account a specific location or time is significantly diminished. This paper employs a big data mining approach to optimize the decision procedure in reverse logistics by using social media data based on customer satisfaction. To demonstrate the capability and effectiveness of the proposed framework, a real case study based on the Apple Notebook, a branch of the electronics industry, is illustrated. Consequently, a separate sentiment analysis based on a recurrent neural network (RNN), a deep learning approach, is fulfilled for notebook features and models. The framework can determine the most appropriate disposition decision in reverse logistics. Furthermore, a failure mode and effects analysis (FMEA) procedure was employed to make some suggestions about Apple.en
dc.formattext
dc.identifier.doi10.15240/tul/001/2025-4-006
dc.identifier.eissn2336-5604
dc.identifier.issn1212-3609
dc.identifier.urihttps://dspace.tul.cz/handle/15240/178357
dc.language.isoen
dc.publisherTechnická Univerzita v Libercics
dc.publisherTechnical university of Liberec, Czech Republicen
dc.publisher.abbreviationTUL
dc.relation.ispartofEkonomie a Managementcs
dc.relation.ispartofEconomics and Managementen
dc.relation.isrefereedtrue
dc.rightsCC BY-NC
dc.subjectReverse logisticsen
dc.subjectsocial mediaen
dc.subjectrecurrent neural network (RNN)en
dc.subjectfailure mode and effects analysis (FMEA)en
dc.subjectsentiment analysisen
dc.subject.classificationM11
dc.subject.classificationM21
dc.subject.classificationC45
dc.subject.classificationD81
dc.titlePromoting reverse logistics decisions using a new hybrid model based on deep learning and failure mode and effects analysis approachesen
dc.typeArticleen
local.accessopen
local.citation.epage98
local.citation.spage79
local.facultyFaculty of Economics
local.filenameEM_4_2025_6
local.fulltextyes
local.relation.abbreviationE+Mcs
local.relation.abbreviationE&Men
local.relation.issue4
local.relation.volume28
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
E+M_4_2025_06.pdf
Size:
2.38 MB
Format:
Adobe Portable Document Format
Description:
článek
Download
Collections
Číslo 4