A Multicriteria Solution Approach for Material Combination Selection in Furniture Production

Öz

Today, environmental sustainability and advances in technology have directed industries’ attention towards more environmentally friendly and innovative materials. Wood-plastic composites (WPC) contribute to the conservation of natural resources and the reduction of environmental pollution and present a durable material option. The performance of these composite materials is closely related to the combinations of materials they contain. Determining the most suitable material combination assists manufacturers, designers, and materials engineers in developing products that meet specific application requirements. This study presents an integrated BWM-WASPAS approach to be used in the selection of suitable material combinations for furniture production. Different combinations of beech wood and polycarbonate sheets are evaluated by taking into consideration their physical and mechanical properties. The BWM method is used to prioritize the decision criteria, while the WASPAS method is employed to determine the ranking of the alternatives. In the final stage of the study, a sensitivity analysis is conducted to support the ranking results. This study presents its novelty by formulating the material layer organization evaluation problem in the furniture industry as a complex multicriteria decision-making problem and integrating the BWM and WASPAS methods for material combination selection.

Anahtar Kelimeler

• BWM
• Multicriteria analysis
• Furniture industry
• Layered material
• WASPAS

Mobilya Üretiminde Malzeme Kombinasyonu Seçimi İçin Çok Kriterli Bir Çözüm Yaklaşımı

Öz

Günümüzde, çevresel sürdürülebilirlik ve teknolojideki ilerlemeler endüstrilerin ilgisini çevreye daha duyarlı ve yenilikçi malzemelere yönlendirmiştir. Odun-plastik kompozit (OPK) malzemeler, doğal kaynakların korunmasına ve çevre kirliliğinin azaltılmasına katkıda bulunurken aynı zamanda dayanıklı bir malzeme seçeneği sunmaktadır. Bu kompozit malzemelerin performansı içerdikleri malzemelerin kombinasyonları ile yakından ilişkilidir. En uygun malzeme kombinasyonunun belirlenmesi spesifik uygulama gereksinimlerini karşılayan ürünler geliştirmede üreticilere, tasarımcılara ve malzeme mühendislerine yardımcı olabilmektedir. Bu çalışma, mobilya üretimi için uygun malzeme kombinasyonlarını seçme sürecinde kullanılmak üzere bütünleşik bir BWM-WASPAS yaklaşımı sunmaktadır. Doğu kayını ve polikarbonat levhaların farklı kombinasyonları fiziksel ve mekanik özellikler göz önüne alınarak değerlendirilmektedir. BWM yöntemi karar kriterlerini önceliklendirirken, alternatiflerin öncelik sıralamasını belirlemek için WASPAS yöntemi kullanılmaktadır. Çalışmanın son aşamasında, sıralama sonuçlarını desteklemek için bir duyarlılık analizi gerçekleştirilmektedir. Bu çalışma, mobilya endüstrisinde malzeme katman organizasyonu değerlendirme problemini karmaşık bir çok kriterli karar verme problemi olarak formüle ederek ve malzeme kombinasyonu seçimi için BWM ve WASPAS yöntemlerini bütünleştirerek yeniliğini sunmaktadır.

Anahtar Kelimeler

• BWM
• Çok kriterli analiz
• Mobilya endüstrisi
• Katmanlı malzeme
• WASPAS

Kaynakça

1. [1] Hasanin M.S., Abd El-Aziz M.E., El-Nagar I., Hassan Y.R., Youssef A.M., Green enhancement of wood plastic composite based on agriculture wastes compatibility via fungal enzymes, Scientific Reports, 12 (2022) 19197.
2. [2] Yu Q., Wang Y., Ye H., Sheng Y., Shi Y., Zhang M., Fan W., Yang R., Xia C., Ge S., Preparation and properties of wood plastic composites with desirable features using poplar and five recyclable plastic wastes, Applied Sciences, 11 (2021) 6838.
3. [3] Mali J., Sarsama P., Suomi-Lindberg L., Metsä-Kortelainen S., Peltonen J., Vilkki M., Koto T., Tiisala S., Woodfiber-plastic composites, VTT Technical Research Centre of Finland Report, (2003).
4. [4] Xiao R., Yu Q., Ye H., Shi Y., Sheng Y., Zhang M., Nourani P., Ge S., Visual design of high-density polyethylene into wood plastic composite with multiple desirable features: A promising strategy for plastic waste valorization, Journal of Building Engineering, 63 (2023) 105445.
5. [5] Keskisaari A., Kärki T., The use of waste materials in wood-plastic composites and their impact on the profitability of the product, Resources, Conservation and Recycling, 134 (2018) 257-261.
6. [6] Ilçe A.C., Budakçı M., Özdemir S., Akkuş M., Analysis of usability in furniture production of wood plastic laminated board, BioResources, 10 (2015) 4300-4314.
7. [7] Singer H., Özşahin Ş., Multicriteria evaluation of structural composite lumber products, Journal of Anatolian Environmental and Animal Sciences, 5 (2020) 807-813.
8. [8] Al Mohamed A.A., Al Mohamed S., Zino M., Application of fuzzy multicriteria decision-making model in selecting pandemic hospital site, Future Business Journal, 9 (2023) 14.

9. [9] Çalış Boyacı A., Tüzemen M.Ç., Bütünleşik SWARA-MULTIMOORA yaklaşımı ile uçak gövdesi için malzeme seçimi, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 8 (2020) 768-782.
10. [10] Daş G.S., Tetik T., Bir iletişim uydu operatörünün fırlatma aracı seçim problemi için kesin ve bulanık VZA yaklaşımlarının karşılaştırılması, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 5 (2017) 21-30.
11. [11] Alvalı G.T., Balbay A., Şişman T., Güneş S., Çok kriterli karar verme teknikleri kullanılarak elektrikli araç şasi malzemesi seçimi, Gazi Üniversitesi Fen Bilimleri Dergisi Part C: Tasarım ve Teknoloji, 9 (2021) 573-588.
12. [12] Mi X., Tang M., Liao H., Shen W., Lev B., The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega, 87 (2019) 205-225.
13. [13] Stević Ž., Pamučar D., Kazimieras Zavadskas E., Ćirović G., Prentkovskis O., The selection of wagons for the internal transport of a logistics company: A novel approach based on rough BWM and rough SAW methods, Symmetry, 9(2017) 264.
14. [14] Youssef A.E., An integrated MCDM approach for cloud service selection based on TOPSIS and BWM, IEEE Access, 8 (2020) 71851-71865.
15. [15] Deng F., Li Y., Lin H., Miao J., Liang X., A BWM-TOPSIS hazardous waste inventory safety risk evaluation, International Journal of Environmental Research and Public Health, 17 (2020) 5765.
16. [16] Torkayesh A.E., Pamucar D., Ecer F., Chatterjee P., An integrated BWM-LBWA-CoCoSo framework for evaluation of healthcare sectors in Eastern Europe, Socio-Economic Planning Sciences, 78 (2021) 101052.
17. [17] Banihashemi S.A., Khalilzadeh M., Assessing employees’ job motivation using BWM method and fuzzy goal programming: a case study of a petrochemical company, International Journal of Energy Sector Management, 16 (2022) 1259-1280.
18. [18] Avakh Darestani S., Palizban T., Imannezhad R., Maintenance strategy selection: a combined goal programming approach and BWM-TOPSIS for paper production industry, Journal of Quality in Maintenance Engineering, 28 (2022) 14-36.
19. [19] Kumar S., Bhaumik S., Patnaik L., Maity S.R., Paleu V., Application of integrated BWM Fuzzy-MARCOS approach for coating material selection in tooling industries, Materials, 15 (2022) 9002.
20. [20] Shayani Mehr P., Hafezalkotob A., Fardi K., Seiti H., Movahedi Sobhani F., Hafezalkotob A., A comprehensive framework for solar panel technology selection: A BWM‐ MULTIMOOSRAL approach, Energy Science & Engineering, 10 (2022) 4595-4625.
21. [21] Badi I., Pamučar D., Stević Ž., Muhammad L.J., Wind farm site selection using BWM-AHP-MARCOS method: A case study of Libya, Scientific African, 19 (2023) e01511.
22. [22] Ishizaka A., Khan S.A., Kheybari S., Zaman S.I., Supplier selection in closed loop pharma supply chain: a novel BWM–GAIA framework, Annals of Operations Research, 324 (2023) 13-36.
23. [23] Mehrnoor S., Robati M., Kheirkhah Zarkesh M.M., Farsad F., Baikpour S., Land subsidence hazard assessment based on novel hybrid approach: BWM, weighted overlay index (WOI), and support vector machine (SVM), Natural Hazards, 115 (2023) 1997-2030.
24. [24] Tuş A., Aytaç Adalı E., The new combination with CRITIC and WASPAS methods for the time and attendance software selection problem, OPSEARCH, 56 (2019) 528-538.
25. [25] Rao C.N., Sujatha M., A consensus-based Fermatean fuzzy WASPAS methodology for selection of healthcare waste treatment technology selection, Decision Making: Applications in Management and Engineering, 6 (2023) 600-619.
26. [26] Karande P., Zavadskas E.K., Chakraborty S., A study on the ranking performance of some MCDM methods for industrial robot selection problems, International Journal of Industrial Engineering Computations, 7 (2016) 399-422.
27. [27] Bakır M., Akan Ş., Durmaz E., Exploring service quality of low-cost airlines in Europe: An integrated MCDM approach, Economics and Business Review, 5 (2019) 109-130.
28. [28] Baç U., An integrated SWARA-WASPAS group decision making framework to evaluate smart card systems for public transportation, Mathematics, 8 (2020) 1723.
29. [29] Pathapalli V.R., Basam V.R., Gudimetta S.K., Koppula M.R., Optimization of machining parameters using WASPAS and MOORA, World Journal of Engineering, 17 (2020) 237-246.
30. [30] Singh R.K., Modgil S., Supplier selection using SWARA and WASPAS – a case study of Indian cement industry, Measuring Business Excellence, 24 (2020) 243-265.
31. [31] Akpinar M.E., Third-party logistics (3PL) provider selection using hybrid model of SWARA and WASPAS, International Journal of Pure and Applied Sciences, 7 (2021) 371-382.
32. [32] Ao Xuan H., Vu Trinh V., Techato K., Phoungthong K., Use of hybrid MCDM methods for site location of solar-powered hydrogen production plants in Uzbekistan, Sustainable Energy Technologies and Assessments, 52 (2022) 101979.
33. [33] Moghimi F., Baradaran V., Hosseinian A.H., Identifying the influential factors on the effectiveness of industrial parks and using an MCDM method to rank them: case study of Iran, Journal of Facilities Management, (2022). doi:10.1108/JFM-12-2021-0151
34. [34] Shivade A.S., Sapkal S.U., Selection of optimum plant layout using AHP-TOPSIS and WASPAS approaches coupled with Entropy method, Decision Science Letters, 11 (2022) 545-562.
35. [35] Rezaei J., Best-worst multi-criteria decision-making method, Omega, 53 (2015) 49-57.
36. [36] Amari A., Moussaid L., Tallal S., New parking lot selection approach based on the multi-criteria decision making (MCDM) methods: health criteria, Sustainability, 15 (2023) 938.
37. [37] Satıcı S., MEREC temelli WASPAS yöntemiyle üniversitelerin girişimci ve yenilikçi performanslarının değerlendirilmesi, Girişimcilik ve Kalkınma Dergisi, 17 (2022) 106-128.