KaydedilenlerDerginizi ekleyin

Dergiler / Verimlilik Dergisi / 2026 / Cilt: 60 Sayı: 2

Gıda Üretim Sistemlerinde Verimlilik Artırımı için Bulanık BWM Entegre Hat Dengeleme Optimizasyonu: Konserve Üretimi Uygulaması

Fuzzy BWM Integrated Line Balancing Optimisation for Productivity Improvement in Food Production Systems: Application to Canned Food Production

Çağdaş Yildiz0009-0006-8384-2466
Adem Tüzemen(TOKAT GAZİOSMANPAŞA ÜNİVERSİTESİ, İKTİSADİ VE İDARİ BİLİMLER FAKÜLTESİ, İŞLETME BÖLÜMÜ, ÜRETİM YÖNETİMİ VE PAZARLAMA ANABİLİM DALI)0000-0002-5786-2686
Sayfa
491–514
Yayın Tarihi
2026-04-20
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Bulgular: Hibrit yaklaşım hat verimliliğini %72,4'ten %86,84'e yükselterek %14,44 verimlilik artışı sağlamıştır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Amaç: Bu çalışmanın amacı, gıda üretim sistemlerinde verimliliği artırmak için Bulanık En İyi-En Kötü Yöntemi (BWM) ile hat dengeleme optimizasyonunu birleştirerek çok boyutlu performans iyileşmesi sağlayacak yenilikçi bir yöntem geliştirmektir.
📊
Temel Bulgular & Veri:Dokuz farklı performans kriterini göz önünde bulundurarak, 15 görevin 5 istasyona en uygun şekilde dağıtılmasıyla üretim süreçlerinin verimliliğinin artırılması hedeflenmiştir.
💡
Sonuç & Katkı:Bulgular: Hibrit yaklaşım hat verimliliğini %72,4'ten %86,84'e yükselterek %14,44 verimlilik artışı sağlamıştır.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Üretim Verimliliği · Hat Dengeleme · Bulanık BWM

Özet

Amaç: Bu çalışmanın amacı, gıda üretim sistemlerinde verimliliği artırmak için Bulanık En İyi-En Kötü Yöntemi (BWM) ile hat dengeleme optimizasyonunu birleştirerek çok boyutlu performans iyileşmesi sağlayacak yenilikçi bir yöntem geliştirmektir. Dokuz farklı performans kriterini göz önünde bulundurarak, 15 görevin 5 istasyona en uygun şekilde dağıtılmasıyla üretim süreçlerinin verimliliğinin artırılması hedeflenmiştir. Yöntem: Türkiye'deki orta ölçekli bir gıda üretim tesisinin konserve üretim hattından elde edilen gerçek verilere dayanarak hibrit bir metodoloji oluşturulmuştur. Bulanık BWM ile nesnel kriterlerin ağırlıklandırılması, ağırlıklı toplam puanla istasyon performansının değerlendirilmesi, çok amaçlı matematiksel optimizasyon modeli ve LINGO 18.0 yazılımı kullanılarak yapılan çözümleme gibi dört temel unsurdan oluşan sistematik bir yöntem benimsenmiştir. Bulgular: Hibrit yaklaşım hat verimliliğini %72,4'ten %86,84'e yükselterek %14,44 verimlilik artışı sağlamıştır. Üretim darboğazı 49,6 saniyeden 41,5 saniyeye (%16,3) düşürülmüş, günlük üretim kapasitesi 113 birim (%19,4) artırılmıştır. Düzgünlük indeksinin 11,2'den 1,71'e iyileştirilmesi ile iş yükü dağılımında önemli denge sağlanmıştır. Geliştirilen hibrit metodoloji, gıda üretim sektöründe verimlilik odaklı çok kriterli optimizasyon uygulamalarına özgün katkı sağlamakta ve bulanık BWM'nin hat dengeleme problemlerine sistematik entegrasyonunun etkinliğini kanıtlamaktadır. Özgünlük: Bu çalışma, bulanık BWM'nin hat dengeleme problemlerine sistematik entegrasyonunu gerçekleştirerek gıda üretim süreçlerinin çok boyutlu optimizasyonuna yönelik literatüre metodolojik yenilik katmaktadır.

Abstract

Purpose: This study aims to develop an innovative methodology that provides multi-dimensional performance improvement through hybrid integration of Fuzzy Best-Worst Method (BWM) and assembly line balancing optimization to increase production efficiency in food production systems. The study targets efficiency-oriented optimization of production processes by achieving optimal distribution of 15 tasks to 5 stations considering nine different performance criteria. Methodology: A hybrid methodology was developed using real data obtained from the canning production line of a medium-scale food production company operating in Turkey. A systematic approach consisting of four main components was adopted: objective criterion weighting with Fuzzy BWM, station performance evaluation with weighted sum score, multi-objective mathematical optimization model, and solution with LINGO 18.0 software. Findings: The hybrid approach increased line efficiency from 72.4% to 86.84%, providing a 14.44% efficiency improvement. Production bottleneck was reduced from 49.6 seconds to 41.5 seconds (16.3%), and daily production capacity was increased by 113 units (19.4%). Significant balance in workload distribution was achieved by improving the smoothness index from 11.2 to 1.71. Originality: This study contributes methodological innovation to the literature on multi-dimensional optimization of food production processes by realizing systematic integration of Fuzzy BWM into assembly line balancing problems and proves the effectiveness of hybrid methodologies in industrial efficiency enhancement.

🔖

Bu Makaleye Atıf Yap

Yildiz, Ç., Tüzemen, A. (2026). Gıda Üretim Sistemlerinde Verimlilik Artırımı için Bulanık BWM Entegre Hat Dengeleme Optimizasyonu: Konserve Üretimi Uygulaması. Verimlilik Dergisi, Cilt: 60 Sayı: 2, 491–514. https://doi.org/10.51551/verimlilik.1757188

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (54)

  1. Abakarov, A., Sushkov, Y., Almonacid, S. ve Simpson, R. (2009). “Multiobjective Optimization Approach: Thermal Food Processing”, Journal of Food Science, 74(9), E471-E487. https://doi.org/10.1111/j.1750-3841.2009.01348.x
  2. Abdous, M.A., Delorme, X., Battini, D., Sgarbossa, F. ve Berger-Douce, S. (2018). “Multi-Objective Optimization of Assembly Lines with Workers Fatigue Consideration”, IFAC-PapersOnLine, 51(11), 698-703. https://doi.org/10.1016/j.ifacol.2018.08.400
  3. Abdullah, F.M., Al-Ahmari, A.M. ve Anwar, S. (2023). “A Hybrid Fuzzy Multi-Criteria Decision-Making Model for Evaluating the Influence of Industry 4.0 Technologies on Manufacturing Strategies”, Machines, 11(2), 310. https://doi.org/10.3390/machines11020310
  4. Agrawal, K., Goktas, P., Holtkemper, M., Beecks, C. ve Kumar, N. (2025). “AI-Driven Transformation in Food Manufacturing: A Pathway to Sustainable Efficiency and Quality Assurance”, Frontiers in Nutrition, 12, 1553942. https://doi.org/10.3389/fnut.2025.1553942
  5. Ahmadi, H.B., Kusi-Sarpong, S. ve Rezaei, J. (2017). “Assessing the Social Sustainability of Supply Chains Using Best Worst Method”, Resources, Conservation and Recycling, 126, 99-106. https://doi.org/10.1016/j.resconrec.2017.07.020
  6. Anandharamakrishnan, C. ve Moses, J.A. (2024). Emerging Technologies for the Food Industry: Volume 1: Fundamentals of Food Processing Technology, CRC Press, Boca Raton. https://doi.org/10.1201/9781003413592
  7. Andres, C., Miralles, C. ve Pastor, R. (2008). “Balancing and Scheduling Tasks in Assembly Lines with Sequence-Dependent Setup Times”, European Journal of Operational Research, 187(3), 1212-1223. https://doi.org/10.1016/j.ejor.2006.07.044
  8. Arteaga-Cabrera, E., Ramírez-Márquez, C., Sánchez-Ramírez, E., Segovia-Hernández, J.G., Osorio-Mora, O. ve Gómez-Salazar, J.A. (2025). “Advancing Optimization Strategies in the Food Industry: From Traditional Approaches to Multi-Objective and Technology-Integrated Solutions”, Applied Sciences, 15(7), 3846. https://doi.org/10.3390/app15073846
  9. Atasagun, Y., Döyen, A. ve Can Atasagun, G. (2024). “Traditional and Fuzzy Goal Programming Approaches for Resource Dependent Assembly Line Balancing Problem”, Numerical Algebra, Control and Optimization, 14(1), 214-226. https://doi.org/10.3934/naco.2023003
  10. Battaïa, O. ve Dolgui, A. (2013). “A Taxonomy of Line Balancing Problems and Their Solution Approaches”, International Journal of Production Economics, 142(2), 259-277. https://doi.org/10.1016/j.ijpe.2012.10.020
  11. Battaïa, O. ve Dolgui, A. (2022). “Hybridizations in Line Balancing Problems: A Comprehensive Review on New Trends and Formulations”, International Journal of Production Economics, 250, 108673. https://doi.org/10.1016/j.ijpe.2022.108673
  12. Behzad, M., Zolfani, S.H., Pamucar, D. ve Behzad, M. (2020). “A Comparative Assessment of Solid Waste Management Performance in the Nordic Countries Based on BWM-EDAS”, Journal of Cleaner Production, 266, 122008. https://doi.org/10.1016/j.jclepro.2020.122008
  13. Bilgiç, S., Torğul, B. ve Paksoy, T. (2021). “Sürdürülebilir Enerji Yönetimi İçin BWM Yöntemi İle Yenilenebilir Enerji Kaynaklarının Değerlendirilmesi”, Verimlilik Dergisi, 2, 95-110. https://doi.org/10.51551/verimlilik.663721
  14. Bonab, S.R., Ghoushchi, S.J., Deveci, M. ve Haseli, G. (2023). “Logistic Autonomous Vehicles Assessment Using Decision Support Model Under Spherical Fuzzy Set Integrated Choquet Integral Approach”, Expert Systems with Applications, 214, 119205. https://doi.org/10.1016/j.eswa.2022.119205
  15. Bortolini, M., Faccio, M., Gamberi, M. ve Pilati, F. (2017). “Multi-Objective Assembly Line Balancing Considering Component Picking and Ergonomic Risk”, Computers & Industrial Engineering, 112, 348-367. https://doi.org/10.1016/j.cie.2017.08.029
  16. Bouraima, M.B., Qiu, Y., Ayyildiz, E. ve Yildiz, A. (2023). “Prioritization of Strategies for a Sustainable Regional Transportation Infrastructure by Hybrid Spherical Fuzzy Group Decision-Making Approach”, Neural Computing and Applications, 35(24), 17967-17986. https://doi.org/10.1007/s00521-023-08660-4
  17. Boysen, N., Fliedner, M. ve Scholl, A. (2008). “Assembly Line Balancing: Which Model to Use When?”, International Journal of Production Economics, 111(2), 509-528. https://doi.org/10.1016/j.ijpe.2007.02.026
  18. Boysen, N., Schulze, P. ve Scholl, A. (2022). “Assembly Line Balancing: What Happened in the Last Fifteen Years?”, European Journal of Operational Research, 301(3), 797-814. https://doi.org/10.1016/j.ejor.2021.11.043
  19. Cavalcanti, J.H., Kovacs, T., Ko, A. ve Pocsarovszky, K. (2024). “Production System Efficiency Optimization Through Application of a Hybrid Artificial Intelligence Solution”, International Journal of Computer Integrated Manufacturing, 37(6), 790-807. https://doi.org/10.1080/0951192x.2023.2257661
  20. Debroy, P., Majumder, P., Pramanik, S. ve Seban, L. (2024). “TrF-BWM-Neutrosophic-TOPSIS Strategy Under SVNS Environment Approach and Its Application to Select the Most Effective Water Quality Parameter of Aquaponic System”, Neutrosophic Sets and Systems, 70, 217-251. https://doi.org/10.15388/24-infor583
  21. Derya, T., Atalay, K.D. ve Ucal Sari, I. (2025). “Assembly Line Balancing Using Fuzzy Mathematical Modeling”, Intelligent and Fuzzy Systems (Editör: Kahraman, C. ve diğerleri), Springer, Cham, 332-340. https://doi.org/10.1007/978-3-031-98304-7_38
  22. Gupta, P., Anand, S. ve Gupta, H. (2017). “Developing a Roadmap to Overcome Barriers to Energy Efficiency in Buildings Using Best Worst Method”, Sustainable Cities and Society, 31, 244-259. https://doi.org/10.1016/j.scs.2017.02.005
  23. Hasan, M.G., Ashraf, Z. ve Khan, M.F. (2022). “Multi‐Choice Best‐Worst Multi‐Criteria Decision‐Making Method and Its Applications”, International Journal of Intelligent Systems, 37(2), 1129-1156. https://doi.org/10.1002/int.22663
  24. Haseli, G., Sheikh, R., Ghoushchi, S. J., Hajiaghaei-Keshteli, M., Moslem, S., Deveci, M. ve Kadry, S. (2024). “An Extension of the Best–Worst Method Based on the Spherical Fuzzy Sets for Multi-Criteria Decision-Making”, Granular Computing, 9(2), 40. https://doi.org/10.1007/s41066-024-00462-w
  25. Hassoun, A., Jagtap, S., Trollman, H., Garcia‐Garcia, G., Duong, L.N., Saxena, P. ve diğerleri (2024). “From Food Industry 4.0 to Food Industry 5.0: Identifying Technological Enablers and Potential Future Applications in the Food Sector”, Comprehensive Reviews in Food Science and Food Safety, 23(6), e370040. https://doi.org/10.1111/1541-4337.70040
  26. Hoffmann, T.R. (1963). “Assembly Line Balancing with a Precedence Matrix”, Management Science, 9(4), 551-562. https://doi.org/10.1287/mnsc.9.4.551
  27. Khan, A.S., Khan, R., Saleem, W., Salah, B. ve Alkhatib, S. (2022). “Modeling and Optimization of Assembly Line Balancing Type 2 and E (SLBP-2E) for a Reconfigurable Manufacturing System”, Processes, 10(8), 1582. https://doi.org/10.3390/pr10081582
  28. Kumar, M. ve Ratandhara, H. (2023). “A Simplified and Improved Best-Worst Method for Multi-Criteria Decision-Making: A Novel Approach”, Available at SSRN 4402170. https://doi.org/10.2139/ssrn.4402170
  29. Lind, A., Elango, V., Hanson, L., Högberg, D., Lämkull, D., Mårtensson, P. ve Syberfeldt, A. (2024). “Multi-Objective Optimization of an Assembly Layout Using Nature-Inspired Algorithms and a Digital Human Modeling Tool”, IISE Transactions on Occupational Ergonomics and Human Factors, 12(3), 175-188. https://doi.org/10.1080/24725838.2024.2362726
  30. Liu, Q., Wei, X., Wang, Q., Song, J., Lv, J., Liu, Y. ve Tang, O. (2024). “An Investigation of Mixed-Model Assembly Line Balancing Problem with Uncertain Assembly Time in Remanufacturing”, Computers & Industrial Engineering, 198, 110676. https://doi.org/10.1016/j.cie.2024.110676
  31. Mao, Z., Sun, Y., Fang, K., Huang, D. ve Zhang, J. (2024). “Balancing and Scheduling of Assembly Line with Multi-Type Collaborative Robots”, International Journal of Production Economics, 271, 109207. https://doi.org/10.1016/j.ijpe.2024.109207
  32. Michels, A.S. ve Costa, A.M. (2024). “Model and Heuristics for the Multi-Manned Assembly Line Worker Integration and Balancing Problem”, International Journal of Production Research, 62(24), 8719-8744. https://doi.org/10.1080/00207543.2024.2347572
  33. Mumcu, Y.K. (2024). “Solution Approach Using Heuristic and Artificial Neural Networks Methods in Assembly Line Balancing Problems: A Case Study in the Lighting Industry”, Heliyon, 10(5). https://doi.org/10.1016/j.heliyon.2024.e26950
  34. Naresh, R., Kanagaraj, G., Giri, J., Yu, V.F., Fatehmulla, A. ve Mallik, S. (2025). “Cost-Efficient Design and Optimization of Robotic Assembly Lines Using a Non-Dominated Sorting Genetic Algorithm Framework”, Scientific Reports, 15(1), 9367. https://doi.org/10.1038/s41598-025-86171-z
  35. Nath, P. C., Mishra, A.K., Sharma, R., Bhunia, B., Mishra, B., Tiwari, A. ve diğerleri (2024). “Recent Advances in Artificial Intelligence Towards the Sustainable Future of Agri-Food Industry”, Food Chemistry, 447, 138945. https://doi.org/10.1016/j.foodchem.2024.138945
  36. Naz, S., Ashraf, S. ve Akram, M. (2018). “A Novel Approach to Decision-Making with Pythagorean Fuzzy Information”, Mathematics, 6(6), 95. https://doi.org/10.1037/math6060095
  37. Özdağoğlu, A., Keleş, M. K. ve Işıldak, B. (2020). “Isparta Süleyman Demirel Havalimanını Kullanan Havayolu Firmaları Performanslarının BWM, MAIRCA ve MABAC İle Değerlendirilmesi”, Uluslararası İktisadi ve İdari İncelemeler Dergisi, 29, 175-194. https://doi.org/10.18092/ulikidince.711974
  38. Pant, S., Kumar, A., Ram, M., Klochkov, Y. ve Sharma, H. K. (2022). “Consistency Indices in Analytic Hierarchy Process: A Review”, Mathematics, 10(8), 1206. https://doi.org/10.3390/math10081206
  39. Perrignon, M., Croguennec, T., Jeantet, R. ve Emily, M. (2024). “The Multi-Objective Data-Driven Approach: A Route to Drive Performance Optimization in the Food Industry”, Trends in Food Science & Technology, 152, 104697. https://doi.org/10.1016/j.tifs.2024.104697
  40. Rabbani, M., Montazeri, M., Farrokhi-Asl, H. ve Rafiei, H. (2016). “A Multi-Objective Genetic Algorithm for a Mixed-Model Assembly U-Line Balancing Type-I Problem Considering Human-Related Issues, Training, and Learning”, Journal of Industrial Engineering International, 12(4), 485-497. https://doi.org/10.1007/s40092-016-0158-6
  41. Ramli, A.N. ve Ab. Rashid, M.F.F. (2024). “Assembly Line Balancing with Energy Consumption Optimization Using Substituted Tiki-Taka Algorithm”, Process Integration and Optimization for Sustainability, 8(4), 1065-1079. https://doi.org/10.1007/s41660-024-00413-7
  42. Ren, J., Liang, H. ve Chan, F.T. (2017). “Urban Sewage Sludge, Sustainability, and Transition for Eco-City: Multi-Criteria Sustainability Assessment of Technologies Based on Best-Worst Method”, Technological Forecasting and Social Change, 116, 29-39. https://doi.org/10.1016/j.techfore.2016.10.070
  43. Rezaei, J. (2015). “Best-Worst Multi-Criteria Decision-Making Method”, Omega, 53, 49-57. https://doi.org/10.1016/j.omega.2014.11.009
  44. Rezaei, J. (2016). “Best-Worst Multi-Criteria Decision-Making Method: Some Properties and a Linear Model”, Omega, 64, 126-130. https://doi.org/10.1016/j.omega.2015.12.001
  45. Saaty, T.L. (1980). “The Analytic Hierarchy Process”, McGraw-Hill, New York.
  46. Sağbaş, A., Çapraz, H.O. ve Karakurt, N. (2018). “Yalın Üretime Geçiş Sürecinde Seri Üretim Hattında Üretim Sistemi Optimizasyonu”, Verimlilik Dergisi, 2, 7-27.
  47. Salimi, N. ve Rezaei, J. (2018). “Evaluating Firms' R&D Performance Using Best Worst Method”, Evaluation and Program Planning, 66, 147-155. https://doi.org/10.1016/j.evalprogplan.2017.10.002
  48. Salveson, M.E. (1955). “The Assembly-Line Balancing Problem”, Transactions of the American Society of Mechanical Engineers, 77(6), 939-947. https://doi.org/10.1111/1.4014559
  49. Sotskov, Y.N. (2023). “Assembly and Production Line Designing, Balancing and Scheduling with Inaccurate Data: A Survey and Perspectives”, Algorithms, 16(2), 100. https://doi.org/10.3390/a16020100
  50. Torabi, S. A., Giahi, R. ve Sahebjamnia, N. (2016). “An Enhanced Risk Assessment Framework for Business Continuity Management Systems”, Safety Science, 89, 201-218. https://doi.org/10.1016/j.ssci.2016.06.015
  51. Van Erp, T., Carvalho, N.G.P., Gerolamo, M.C., Gonçalves, R., Rytter, N.G.M. ve Gladysz, B. (2024). “Industry 5.0: A New Strategy Framework for Sustainability Management and Beyond”, Journal of Cleaner Production, 461, 142271. https://doi.org/10.1016/j.jclepro.2024.142271
  52. Wu, Y., Zheng, S., Wang, J. ve Liu, Q. (2022). “An Integrated Decision-Making Framework Based on Many-Objective Brain Storming Optimization for Urban Drainage System Design”, IEEE Access, 10, 93502-93512. https://doi.org/10.1109/access.2022.3204123
  53. Zacharia, P.T., Xidias, E.K. ve Nearchou, A.C. (2024). “The Fuzzy Human-Robot Collaboration Assembly Line Balancing Problem”, Computers & Industrial Engineering, 187, 109774. https://doi.org/10.1016/j.cie.2023.109774
  54. Zhang, X., Fathollahi-Fard, A.M., Tian, G., Yaseen, Z.M., Pham, D.T., Zhao, Q. ve Wu, J. (2024). “Human-Robot Collaboration in Mixed-Flow Assembly Line Balancing Under Uncertainty: An Efficient Discrete Bees Algorithm”, Journal of Industrial Information Integration, 41, 100676. https://doi.org/10.1016/j.jii.2024.100676