MEREC ve Entropi Yöntemleriyle Yük Kaldırma Platformu Seçiminde Kullanılan Kriterlerin Ağırlıklarının Belirlenmesi

Öz

Lojistik sektöründe ürün/hammadde/yarı mamullerin konulduğu veya bekletildiği yerler depolama yerleridir. Depolama alanlarında lojistik faaliyetlere ve tedarik zincirine değer katan çok çeşitli işlemler gerçekleştirilir. Bu işlemler yapılırken depo alanında kullanılan çeşitli ekipmanlara ihtiyaç duyulur. Depo ekipmanlardan birisi depoya gelen-giden ve depo içindeki malların istenilen yerlere kaldırılmasında kullanılan yük kaldırma platformlarıdır (YKP). Çalışmanın amacı YKP seçiminde kullanılan kriterlerin ve ağırlıklarının belirlenmesi, MEREC yönteminin gerçek bir yaşam probleminde uygulanarak Türkçe literatüre tanıtılması ile MEREC ve Entropi yöntemlerinin karşılaştırılmasıdır. YKP seçiminde “yük kaldırma kapasitesi, fiyatı, platform boyutu, platform ağırlığı ve kaldırma yüksekliği” olmak üzere beş kriter, 9 alternatif belirlenmiştir. Kriterlerin ağırlıklarını belirlemek için MEREC yöntemi ve karşılaştırmak için Entropi kullanılmıştır. MEREC yöntemiyle yapılan hesaplamalara göre %43,3 platform boyutu, %28,2 platform ağırlığı, %12 yük kaldırma kapasitesi, %11,3 kaldırma yüksekliği ve %5,1 fiyat olarak belirlenmiştir. Entropi yöntemine göre %36,5 platform boyutu, %27,4 platform ağırlığı, %20,7 kaldırma yüksekliği, %13,4 yük kaldırma kapasitesi ve %2,1 fiyat olarak hesaplanmıştır. Kriter ağırlıkları arasında farklılıklar bulunmuş, Pearson korelasyon analiziyle inceleme yapılmış ve bulgular arasında çok kuvvetli, anlamlı ve pozitif yönde bir ilişki olduğu tespit edilmiştir. Bulgulara göre daha karmaşık ve uzun hesaplama aşamalarına sahip Entropi yöntemi yerine objektif bir kriter ağırlık belirleme yöntemi olarak MEREC yönteminin kullanılabileceği değerlendirilmiştir.

Anahtar Kelimeler

• Lojistik
• Depolama
• Yük kaldırma platformu
• MEREC
• Entropi

Determination of criteria weights used in load lifting platform selection via MEREC and Entropy methods

Öz

In the logistics sector, the places where the product/semi-finished products are placed or kept are warehouse areas. A wide variety of operations are carried out that add value to logistics activities in warehouse areas. While performing these operations, various equipment used in the warehouse area is needed. One of the warehouse equipment is the load lifting platforms (LLP). This study aims to determine the criteria and weights used in the selection of LLP, introduce the MEREC method in a real life problem, and compare the MEREC and Entropy methods. In the selection of LLP, five criteria and 9 alternatives were determined as “load lifting capacity, price, platform size, platform weight, and lifting height”. According to the MEREC method, 43.3% platform size, 28.2% platform weight, 12% load lifting capacity, 11.3% lift height, and 5.1% price, and then Entropy method, it was calculated as 36.5% platform size, 27.4% platform weight, 20.7% lifting height, 13.4% load lifting capacity, and 2.1% price. Differences were found between criteria weights, analysis was made with Pearson correlation and it was determined that there was a very strong, significant, and positive correlation between the findings. Finally, it has been evaluated that the MEREC method can be used as an objective criteria weight determination method instead of the Entropy method, which has more complex and long calculation steps.

Anahtar Kelimeler

• Logistics
• Warehousing
• Load lifting platform
• MEREC
• Entropy

Kaynakça

1. Ahmad, S., Ali, M., Khan, Z. A., & Asjad, M. (2022). Investigating the effect of input variables on the performance of FMS followed by multi-response optimization: A simulation study. Materials Today: Proceedings, in press, 1-4. https://doi.org/10.1016/j.matpr.2022.05.169
2. Ecer, F., & Pamucar, D. (2022). A novel LOPCOW-DOBI multi-criteria sustainability performance assessment methodology: An application in developing country banking sector. Omega, 112, 1-17, 102690, https://doi.org/10.1016/j.omega.2022.102690.
3. Alao, M. A., Ayodele, T. R., Ogunjuyigbe, A. S. O., & Popoola, O. M. (2020). Multi-criteria decision based waste to energy technology selection using entropy-weighted TOPSIS technique: The case study of Lagos, Nigeria. Energy, 201, 117675.
4. Alkan, Ö., & Albayrak, Ö. K. (2020). Ranking of renewable energy sources for regions in Turkey by fuzzy entropy based fuzzy COPRAS and fuzzy MULTIMOORA. Renewable Energy, 162, 712-726.
5. Demirci, A., & Manavgat, G. (2021). Veri Zarflama Analizi, TOPSİS ve VIKOR teknikleriyle forklift aracı seçimi: karma model önerisi. Hacettepe Üniversitesi Sosyal Bilimler Dergisi, 3(1), 2-27.
6. Ersoy, Y. (2020). Green machine selection in a manufacturing company using TOPSIS Method. Akademia Doğa ve İnsan Bilimleri Dergisi, 6(1), 45-56.
7. Fazlollahtabar, H., Smailbašić, A., & Stević, Ž. (2019). FUCOM method in group decision-making: Selection of forklift in a warehouse. Decision Making: Applications in Management and Engineering, 2(1), 49-65.
8. Goswami, S. S., Mohanty, S. K., & Behera, D. K. (2022). Selection of a green renewable energy source in India with the help of MEREC integrated PIV MCDM tool. Materials Today: Proceedings, 52, 1153-1160.

9. Hadi, A., & Abdullah, M. Z. (2022). Web and IoT-based hospital location determination with criteria weight analysis. Bulletin of Electrical Engineering and Informatics, 11(1), 386-395.
10. Hezam, I. M., Mishra, A. R., Rani, P., Cavallaro, F., Saha, A., Ali, J., Strielkowski, W., & Štreimikienė, D. (2022). A Hybrid Intuitionistic Fuzzy-MEREC-RS-DNMA method for assessing the alternative fuel vehicles with sustainability perspectives. Sustainability, 14(9), 5463.
11. Kaya, S.K., Ayçin, E. & Pamucar, D. (2022). Evaluation of social factors within the circular economy concept for European countries. Central European Journal of Operations Research, https://doi.org/10.1007/s10100-022-00800-w.
12. Keleş, N. (2021). Türkiye’de lojistik köy yeri seçiminin çok kriterli karar verme yöntemleriyle değerlendirilmesi. Zonguldak Bulent Ecevit University PhD Thesis, Zonguldak. Keshavarz-Ghorabaee, M., Amiri, M., Zavadskas, E. K., Turskis, Z., & Antucheviciene, J. (2021). Determination of objective weights using a new method based on the removal effects of criteria (MEREC). Symmetry, 13(4), 525.
13. Khodaei, D., Hamidi-Esfahani, Z., & Rahmati, E. (2021). Effect of edible coatings on the shelf-life of fresh strawberries: A comparative study using TOPSIS-Shannon entropy method. NFS Journal, 23, 17-23.
14. Kučera, T. (2020). Selection of handling equipment in warehouse using multi-criteria decision-making. In Transport Means 2020: proceedings of the 24th International Scientific Conference. Kaunas University of Technology.
15. Li, H., Wang, W., Fan, L., Li, Q., & Chen, X. (2020). A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR. Applied Soft Computing, 91, 106207.
16. Li, Y., & Chen, W. (2021). Entropy method of constructing a combined model for improving loan default prediction: A case study in China. Journal of the Operational Research Society, 72(5), 1099-1109.
17. Marinković, M., Zavadskas, E. K., Matić, B., Jovanović, S., Das, D. K., & Sremac, S. (2022). Application of wasted and recycled materials for production of stabilized layers of road structures. Buildings, 12(5), 552.
18. Mishra, A. R., Saha, A., Rani, P., Hezam, I. M., Shrivastava, R., & Smarandache, F. (2022). An integrated decision support framework using single-valued-MEREC-MULTIMOORA for low carbon tourism strategy assessment. IEEE Access, 10, 24411-24432.
19. Nicolalde, J. F., Cabrera, M., Martínez-Gómez, J., Salazar, R. B., & Reyes, E. (2022). Selection of a phase change material for energy storage by multi-criteria decision method regarding the thermal comfort in a vehicle. Journal of Energy Storage, 51, 104437.
20. Özaydin, G., & Karakul, A. K. (2021). Entropi tabanlı MAUT, SAW ve EDAS yöntemleri ile finansal performans değerlendirmesi. Süleyman Demirel Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26(1), 13-29.
21. Özgüner, Z. (2019). Üretim işletmelerinin lojistik faaliyetlerinde süreçsel etkinliğin başarı dinamikleri. Hiperyayın, 1. Baskı, ISBN:978-605-281-561-8, İstanbul. Pamučar, D., & Ćirović, G. (2015). The selection of transport and handling resources in logistics centers using Multi-Attributive Border Approximation area Comparison (MABAC). Expert systems with applications, 42(6), 3016-3028.
22. Pekkaya, M., & Keleş, N. (2021). Determining criteria interaction and criteria priorities in freight village location selection process: the experts’ perspective in Turkey, Asia Pacific Journal of Marketing and Logistics, https://doi.org/10.1108/APJML-05-2021-0338.
23. Petrović, G., Pavlović, J., Madić, M., & Marinković, D. (2022). Optimal synthesis of loader drive mechanisms: a group robust decision-making rule generation approach. Machines, 10(5), 329.
24. Popović, G., Karabašević, D., & Stanujkić, D. (2021). Multiple-criteria framework for cloud service selection. Proceedings of the 3rd Virtual International Conference Path to a Knowledge Society-Managing Risks and Innovation, PaKSoM, November 15-16, 2021, pp.377-382, Complex System Research Center, Serbia.
25. Rani, P., Mishra, A. R., Saha, A., Hezam, I. M., & Pamucar, D. (2022). Fermatean fuzzy Heronian mean operators and MEREC‐based additive ratio assessment method: An application to food waste treatment technology selection. International Journal of Intelligent Systems, 37(3), 2612-2647.
26. Sapkota, G., Das, S., Sharma, A., & Ghadai, R. K. (2022). Comparison of various multi-criteria decision methods for the selection of quality hole produced by ultrasonic machining process. Materials Today: Proceedings. 58 (2), 702-708, https://doi.org/10.1016/j.matpr.2022.02.221.
27. Sarıçalı, G., & Kundakcı, N. (2017). Forklift alternatiflerinin KEMIRA-M yöntemi ile değerlendirilmesi. Optimum Ekonomi ve Yönetim Bilimleri Dergisi, 4(1), 35-53.
28. Shang, Z., Yang, X., Barnes, D., & Wu, C. (2022). Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods. Expert Systems with Applications, 195, 116567.
29. Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
30. Simić, V., Ivanović, I., Đorić, V., & Torkayesh, A. E. (2022). Adapting urban transport planning to the COVID-19 pandemic: An integrated fermatean fuzzy model. Sustainable Cities and Society, 103669.
31. Torkayesh, S. E., Amiri, A., Iranizad, A., & Torkayesh, A. E. (2020). Entropy based EDAS decision making model for neighborhood selection: A case study in Istanbul. Journal of Industrial Engineering and Decision Making, 1(1), 1-11.
32. Toslak, M., Aktürk, B., & Ulutaş, A. (2022). MEREC ve WEDBA yöntemleri ile bir lojistik firmasının yıllara göre performansının değerlendirilmesi. Avrupa Bilim ve Teknoloji Dergisi, (33), 363-372.
33. Trung, D. D., & Thinh, H. X. (2021). A multi-criteria decision-making in turning process using the MAIRCA, EAMR, MARCOS and TOPSIS methods: A comparative study. Advances in Production Engineering & Management, 16(4), 443-456.
34. Tuzkaya, G., Özgen, D., & Gülsün, B. (2011). Malzeme taşıma sistemi alternatiflerinin değerlendirilmesinde bulanık-PROMETHEE yaklaşımı. Doğuş Üniversitesi Dergisi, 12(1), 144-155.
35. Ulutaş, A., & Çelik, D. (2019). Transpalet seçimi probleminin AHP ve EDAS yöntemleri ile değerlendirilmesi. Business & Management Studies: An International Journal, 7(2), 668-686, http://dx.doi.org/10.15295/bmij.v7i2.1028.
36. Ulutaş, A., Stanujkic, D., Karabasevic, D., Popovic, G., & Novaković, S. (2022). Pallet truck selection with MEREC and WISP-S methods. Strategic Management-International Journal of Strategic Management and Decision Support Systems in Strategic Management, 1-7, 10.5937/StraMan2200013U.
37. Verma, P. K., Kumar, R., & Goindi, G. S. (2021). Evaluation of material handling using MCDM techniques: A case study. In Advances in Production and Industrial Engineering (pp. 389-401). Springer, Singapore.
38. Vesković, S., Milinković, S., Abramović, B., & Ljubaj, I. (2020). Determining criteria significance in selecting reach stackers by applying the fuzzy PIPRECIA method. Operational Research in Engineering Sciences: Theory and Applications, 3(1), 72-88.
39. Wang, R., Li, X., & Li, C. (2021). Optimal selection of sustainable battery supplier for battery swapping station based on Triangular fuzzy entropy-MULTIMOORA method. Journal of Energy Storage, 34, 102013.
40. Yazdani, M., Torkayesh, A. E., Santibanez-Gonzalez, E. D., & Otaghsara, S. K. (2020). Evaluation of renewable energy resources using integrated Shannon Entropy—EDAS model. Sustainable Operations and Computers, 1, 35-42.
41. Zhao, J., Ji, G., Tian, Y., Chen, Y., & Wang, Z. (2018). Environmental vulnerability assessment for mainland China based on entropy method. Ecological Indicators, 91, 410-422.