New Grey Integrated Model to Solve a Machine Selection Problem for a Textile Company

• Autorzy: Ulutaş Alptekin

Identyfikatory

DOI

10.5604/01.3001.0013.5853

Warianty tytułu

PL

Nowy zintegrowany model rozwiązujący problem wyboru maszyny dla firmy tekstylnej

• Języki publikacji: EN

Abstrakty

EN

The textile sector has become an indispensable part of the Turkish economy. The sewing machine is a long-lasting and easy-to-use tool widely used in the garment industry, which is a branch of the textile industry. The sewing machine is an indispensable production tool for the textile industry and sewing machine selection is a significant decision for the production performance of textile companies. Selecting an appropriate sewing machine increases production performance, while selecting an improper one reduces production performance. The sewing machine selection problem is a typical machine selection issue. Many criteria, such as cost, productivity, safety etc. are considered in the machine selection. Therefore, MCDM methods are applicable to solve the machine selection problem. This study develops an integrated grey MCDM model including Grey AHP and ROV-G to select the most appropriate sewing machine for an apparel textile company.

PL

Sektor tekstylny stał się nieodłączną częścią tureckiej gospodarki. Maszyna do szycia jest trwałym i łatwym w użyciu narzędziem szeroko stosowanym w przemyśle odzieżowym, który jest gałęzią przemysłu tekstylnego. Maszyna do szycia jest niezbędnym narzędziem produkcyjnym dla przemysłu tekstylnego, a wybór maszyny do szycia jest znaczącą decyzją w zakresie wydajności produkcyjnej firm tekstylnych. Wybór odpowiedniej maszyny do szycia zwiększa wydajność produkcji, a wybór niewłaściwej zmniejsza wydajność produkcji. Problem wyboru maszyny do szycia jest typowym problemem przy wyborze maszyny. Przy wyborze maszyny branych jest pod uwagę wiele kryteriów, takich jak: koszt, wydajność, bezpieczeństwo itp. Dlatego metody MCDM mają zastosowanie do rozwiązania problemu wyboru maszyny. W badaniu, w celu wybrania najbardziej odpowiedniej maszyny do szycia dla firmy produkującej odzież, opracowano zintegrowany model MCDM, w tym AHP i ROV-G.

Słowa kluczowe

EN

AHP; ROV-G; sewing machine selection; machine selection problem

PL

AHP; ROV-G; wybór maszyny do szycia; problem z wyborem maszyny

• Wydawca: Sieć Badawcza Łukasiewicz - Łódzki Instytut Technologiczny
• Czasopismo: Fibres & Textiles in Eastern Europe
• Rocznik: 2020
• Tom: Vol. 28, no. 1 (139)
• Strony: 20--25
• Opis fizyczny: Bibliogr. 29 poz., rys., tab.

Twórcy

autor

Ulutaş Alptekin

• Sivas Cumhuriyet University, Faculty of Economics and Administrative Sciences, Department of International Trade and Logistics, Sivas, Turkey

Bibliografia

• 1. Ertuğrul İ, Öztaş T. The Application of Sewing Machine Selection with the Multi-Objective Optimization On The Basis Of Ratio Analysis Method (MOORA) in Apparel Sector. Tekstil ve Konfeksiyon 2015; 25(1): 80-85.
• 2. İstanbul Tekstil ve Hammaddeleri İhracatçıları Birliği. Available from: https://www.ithib.org.tr/tr/bilgi-merkezi-raporlar-aylik-ihracat-degerlendirme-bilgi-notlari-2019.html.
• 3. Yayla AY, Yildiz A, Ozbek A. Fuzzy TOPSIS Method in Supplier Selection and Application in the Garment Industry. FIBRES & TEXTILES in Eastern Europe 2012; 20, 4(93): 20-23.
• 4. Kabak M, Dagdeviren M. A Hybrid Approach Based on ANP and Grey Relational Analysis for Machine Selection. Tehnicki Vjesnik-Technical Gazette 2017; 24(S1): 109-118.
• 5. Ayağ Z, Özdemir RG. A Fuzzy AHP Approach to Evaluating Machine Tool Alternatives. Journal of intelligent manufacturing 2006; 17(2): 179-190.
• 6. Aloini D, Dulmin R, Mininno V. A Peer IF-TOPSIS Based Decision Support System for Packaging Machine Selection. Expert Systems with Applications 2014; 41(5): 2157-2165.
• 7. Özgen A, Tuzkaya G, Tuzkaya UR, Özgen D. A Multi-Criteria Decision Making Approach for Machine Tool Selection Problem in a Fuzzy Environment. International Journal of Computational Intelligence Systems 2011; 4(4): 431-445.
• 8. Taha Z, Rostam S. A Hybrid Fuzzy AHP-PROMETHEE Decision Support System for Machine Tool Selection in Flexible Manufacturing Cell. Journal of Intelligent Manufacturing 2012; 23(6): 2137-2149.
• 9. Dawal SZM, Yusoff N, Nguyen HT, Aoyama H. Multi-Attribute Decision-Making for CNC Machine Tool Selection in FMC Based on the Integration of the Improved Consistent Fuzzy AHP and TOPSIS. ASEAN Eng J Part A 2013; 3(2):15-31.
• 10. Nguyen HT, Dawal SZM, Nukman Y, Aoyama H, Case K. An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation. PloS one 2015; 10(9): e0133599.
• 11. Ayağ Z, Özdemir RG. An Intelligent Approach to Machine Tool Selection Through Fuzzy Analytic Network Process. Journal of Intelligent Manufacturing 2011; 22(2): 163-177.
• 12. Ayağ Z, Özdemir RG. Evaluating Machine Tool Alternatives Through Modified TOPSIS and Alpha-Cut Based Fuzzy ANP. International Journal of Production Economics 2012: 140(2): 630-636.
• 13. Nguyen HT, Dawal SZM, Nukman Y, Aoyama H. A Hybrid Approach for Fuzzy Multi-Attribute Decision Making in Machine Tool Selection with Consideration of the Interactions of Attributes. Expert Systems with Applications 2014; 41(6): 3078-3090.
• 14. Kumru M, Kumru PY. A Fuzzy ANP Model For The Selection Of 3D Coordinate-Measuring Machine. Journal of Intelligent Manufacturing 2015; 26(5): 999-1010.
• 15. Özceylan E, Kabak M, Dağdeviren M. A Fuzzy-Based Decision Making Procedure for Machine Selection Problem. Journal of Intelligent & Fuzzy Systems 2016; 30(3): 1841-1856.
• 16. Samvedi A, Jain V, Chan FTS. An Integrated Approach for Machine Tool Selection Using Fuzzy Analytical Hierarchy Process and Grey Relational Analysis. International Journal of Production Research 2012; 50(12): 3211-3221.
• 17. Aghdaie MH, Hashemkhani ZS, Zavadskas EK. Decision Making in Machine Tool Selection: An Integrated Approach with SWARA and COPRAS-G Methods. Engineering Economics 2013; 24(1): 5-17.
• 18. Ulutaş A. Sewing Machine Selection for a Textile Workshop By Using EDAS Method. Journal of Business Research Turk 2017; 9(2): 169-183.
• 19. Mohammad YN, Vahid B, Majid A. Planning a Model for Supplier Selection with AHP and Grey Systems Theory. Business and Management Review 2011; 1(7): 09-19.
• 20. Ulutaş A. A Grey Group Decision-Making Model to Solve Supplier Selection Problem for a Textile Company. Paper presented at 16. Üretim Araştırmaları Sempozyumu. 2016 Oct. 12; İstanbul, Turkey. p. 1186-1191.
• 21. Ulutaş A, Bayrakçıl AO, Gri AHS ve ARAS-G Kullanımı ile Bir Restoran için Sebze Tedarikçisinin Değerlendirilmesi. Cumhuriyet Üniversitesi İktisadi ve İdari Bilimler Dergisi 2017; 18(2): 189-204.
• 22. Bakhat R, Rajaa M. Developing a Novel Grey Integrated Multi-Criteria Approach for Enhancing the Supplier Selection Procedure: A Real-World Case of Textile Company. Decision Science Letters 2019; 8(3): 211-224.
• 23. Yakowitz DS, Lane LJ, Szidarovszky F. Multi-Attribute Decision Making: Dominance with Respect to an Importance Order of the Attributes. Applied Mathematics and Computation 1993; 54(2-3): 167-181.
• 24. Hajkowicz S, Higgins A. A Comparison of Multiple Criteria Analysis Techniques for Water Resource Management. European Journal of Operational Research 2008; 184(1): 255-265.
• 25. Liu S, Lin Y. Grey Information: Theory and Practical Applications. Springer-Verlag; 2006.
• 26. Saaty TL. How to Make a Decision: The Analytic Hierarchy Process. European Journal of Operational Research 1990; 48(1): 9-26.
• 27. Wu WW, Lee YT. Developing Global Managers’ Competencies using the Fuzzy DEMATEL Method. Expert Systems with Applications 2007; 32(2):499-507.
• 28. Zavadskas EK, Kaklauskas A, Turskis Z, Tamošaitiene J. Selection of the Effective Dwelling House Walls by Applying Attributes Values Determined at Intervals. Journal of Civil Engineering and Management 2008; 14(2): 85-93.
• 29. Turskis Z, Zavadskas EK. A Novel Method for Multiple Criteria Analysis: Grey Additive Ratio Assessment (ARAS-G) Method. Informatica 2010; 21(4): 597-610.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).