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Issue title: Special issue: Surveys in Artificial Intelligence-based Technologies
Article type: Research Article
Authors: de Assis, Amanda Gomes* | Cabral, Eric Lucas dos Santos | Castro, Wilkson Ricardo Silva | da Costa Junior, João Florêncio | de Souza, Ricardo Pires | Cabral, Marco Antonio Leandro
Affiliations: Department of Production Engineering, Technology Center, University Campus Lagoa Nova, The Federal University of Rio Grande do Norte, 59078-970, Natal, RN, Brazil
Correspondence: [*] Corresponding author: Amanda Gomes de Assis, Department of Production Engineering, Technology Center, University Campus Lagoa Nova, The Federal University of Rio Grande do Norte, 59078-970, Natal, RN, Brazil. Tel.: +55 84987920111; E-mail: amandagdassis@gmail.com.
Abstract: Inventory management decisions have become increasingly complex as they are driven by multiple criteria both within and without the organizations; as a result of that, the multi-criteria analysis methods have been gaining importance as an alternative to support the decision making process. The present work endeavours to systematize the main articles that have approached MCDA and Inventory within the last 31 years (1987–2018), determining the multi-criteria methods with greater prominence in the scientific community discussions as well as the main problems addressed by them. The authors conducted a study about the criteria for decision making in inventory and its alignment with the specialized literature, initially 439 articles catalogued in the Scopus/Embase and Web of Science database were considered for the present revision; after careful analysis of their theoretical content, 47 of them were chosen as they held the information needed to complete the study. The authors observed that the majority of the selected articles refer to the utilization of the Analytic Hierarchy Process (AHP) method and its fuzzy extension whilst the main problems addressed are ranking and sorting.
Keywords: Inventory control, multi-criteria analysis, decision making
DOI: 10.3233/IDT-190093
Journal: Intelligent Decision Technologies, vol. 13, no. 4, pp. 453-462, 2019
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