Yayın: APPLICATION OF ADAPTIVE NEURO FUZZY INFERENCE SYSTEM TO SUPPLIER SELECTION PROBLEM
| dc.contributor.author | Yucel, Atakan | |
| dc.contributor.author | Guneri, Ali Fuat | |
| dc.date.accessioned | 2026-06-27T12:51:47Z | |
| dc.date.issued | 2010 | |
| dc.description.abstract | Supplier selection is a key factor for firms in achieving its goals in supply chain management. To build effective relationships and gain competitive advantage, firms should select best supplier(s) according to its needs by applying proper methods and appropriate criteria. Supplier selection problem includes tangible and intangible factors such as quality, cost, relationship closeness and delivery in practice. This issue makes the problem as a Multi-Criteria Decison Making (MCDM) problem. In this paper, an approach that based on neural network and fuzzy logic named as Adaptive Neuro Fuzzy Inference System (ANFIS) is presented for supplier selection problem. The proposed model takes the learning advantage of neural networks and integrates this with fuzzy logic that represents human reasoning mechanism effectively. The basics of ANFIS are illustrated and an algorithm based on the method is proposed for supplier selection problems in the paper. | en |
| dc.identifier.eissn | 1304-7191 | |
| dc.identifier.endpage | 234 | |
| dc.identifier.issn | 1304-7205 | |
| dc.identifier.issue | 3 | |
| dc.identifier.startpage | 224 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14981/47569 | |
| dc.identifier.volume | 28 | |
| dc.identifier.wos | 000219493000005 | |
| dc.language.iso | tur | |
| dc.publisher | YILDIZ TECHNICAL UNIV | |
| dc.relation.ispartof | SIGMA JOURNAL OF ENGINEERING AND NATURAL SCIENCES-SIGMA MUHENDISLIK VE FEN BILIMLERI DERGISI | |
| dc.subject | Supplier Selection | |
| dc.subject | Adaptive Neuro Fuzzy Inference System | |
| dc.subject | Engineering | |
| dc.title | APPLICATION OF ADAPTIVE NEURO FUZZY INFERENCE SYSTEM TO SUPPLIER SELECTION PROBLEM | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| local.import.source | WOS |