Araştırma Makalesi
BibTex RIS Kaynak Göster

Bulanık Maliyet Parametreleri Altında Tedarik Zinciri Yönetimi için Şans Kısıtlı Programlama Modeli

Yıl 2026, Cilt: 31 Sayı: 1 , 369 - 386 , 10.04.2026
https://doi.org/10.17482/uumfd.1492897
https://izlik.org/JA27BB63KD

Öz

Tedarik zinciri yönetiminde koordinasyon, sistemin etkinliği açısından en kritik konulardan biridir. Koordinasyon düzeyine bağlı olarak tedarik zincirleri merkezi veya merkezi olmayan (desantralize) yapılar olarak sınıflandırılabilir. Merkezi modelde tek bir karar verici, tüm tedarik zincirinin performansını optimize etmeyi amaçlayarak toplam maliyetleri en aza indirmeye ve sistem genelinde verimliliği artırmaya çalışır. Buna karşılık merkezi olmayan modelde zincirin her bir üyesi kendi kârını maksimize etmeye yönelik bağımsız kararlar alır. Bu çalışmada, bulanık üretim maliyeti parametreleri altında faaliyet gösteren tedarikçi ve perakendeciden oluşan iki aşamalı bir tedarik zinciri yapısı incelenmiştir. Belirsizliğin ele alınabilmesi amacıyla bulanık şans kısıtlı programlama (Fuzzy Chance-Constrained Programming – FCCP) yöntemi kullanılarak hem merkezi hem de merkezi olmayan yapılar için toplam kârı maksimize eden optimal sipariş miktarları belirlenmiştir. Literatürde çoğu çalışma bu iki yapıyı ayrı ayrı ele alırken, bu araştırmada FCCP yöntemi güvenilirlik (credibility) teorisi ile kullanılarak her iki koordinasyon mekanizmasını karşılaştırmalı bir çerçevede inceleyen bütünleşik bir yaklaşım sunulmuştur. Ayrıca merkezi olmayan modelde, bağımsız karar vericilerin çatışan hedeflerini temsil edebilmek amacıyla hedef programlama yapısı kullanılmıştır. Önerilen bu bütünleşik yaklaşım, belirsizlik altında tedarik zinciri koordinasyonuna yönelik karar verme süreçlerine hem yöntemsel yenilik hem de uygulamaya dönük önemli katkılar sağlamaktadır.

Kaynakça

  • Arık, O. A. (2019). Credibility-based chance constrained programming for project scheduling with fuzzy activity durations. An International Journal of Optimization and Control: Theories & Applications, 9(2), 208–215. https://doi.org/10.11121/ijocta.01.2019.00631
  • Arshinder, A., Kanda, S. and Deshmukh, G. (2007) Coordination in supply chains: An evaluation using fuzzy logic, Production Planning & Control, 18(8), 420–435. https://doi.org/10.1080/09537280701430994.
  • Bilgen, B. (2010) Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem, Expert Systems with Applications, 37(6), 4488–4495. https://doi.org/10.1016/j.eswa.2009.12.062.
  • Charles, V., Gupta, S. and Ali, İ. (2018) Fuzzy goal programming approach for solving multiobjective supply chain network problems under probabilistic and fuzzy uncertainty, Annals of Operations Research, 269(1–2), 489–512. https://doi.org/10.1007/s10479-017-2524-1.
  • Charnes, A. and Cooper, W.W. (1959) Chance-constrained programming, Management Science, 6(1), 73–79. https://doi.org/10.1287/mnsc.6.1.73.
  • Choi, T.Y. and Guo, S. (2023) Manufacturing firms’ credibility towards customers and operational performance: The counteracting roles of corruption and ICT readiness, International Journal of Production Research, 61(15), 5172–5190. https://doi.org/10.1080/00207543.2023.2169666
  • Gholami, Z., Jolai, F. and Torabi, S.A. (2022) Robust-fuzzy optimization approach in design of sustainable lean supply chain network under uncertainty, Computational and Applied Mathematics, 41(4), 136. https://doi.org/10.1007/s40314-022-01936-w
  • Giri, B.C. and Sarker, B.R. (2019) Coordinating A Multi-Echelon Supply Chain Under Production Disruption and Price-Sensitive Stochastic Demand, Journal of Industrial And Management Optimization, 15,1631-1651. https://doi.org/10.3934/jimo.2018115.
  • Huang, X. (2010) Portfolio analysis: From probabilistic to credibilistic and uncertain approaches, Studies in Fuzziness and Soft Computing, vol. 250. https://doi.org/10.1007/978-3-642-11214-0.
  • Klir, G.J. and Yuan, B. (1995) Fuzzy sets and fuzzy logic: Theory and applications, Prentice Hall PTR, Upper Saddle River, NJ.
  • Lee, H.L. and Billington, C. (1993) Material management in decentralized supply chains, Operations Research, 41(3), 835–847. https://doi.org/10.1287/opre.41.5.835.
  • Liu, B. (2007) Uncertainty theory, 2nd edn, Springer, Berlin. https://doi.org/10.1007/978-3-540-73165-8.
  • Liu, B. and Iwamura, K. (1996) Chance constrained programming with fuzzy parameters, Fuzzy Sets and Systems, 94(2), 227–237. https://doi.org/10.1016/0165-0114(95)00204-4.
  • Liu, B. and Liu, Y.K. (2002) Expected value of fuzzy variable and fuzzy expected value model, IEEE Transactions on Fuzzy Systems, 10(4), 445–450.https://doi.org/10.1109/TFUZZ.2002.800692.
  • Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F. and Özceylan,E. (2022) Hybrid fuzzy and data-driven robust optimization for resilience and sustainable healthcare supply chains with a VMI approach, Environmental Science and Pollution Research, 29(12), 17645–17663. https://doi.org/10.1007/s11356-021-17053-3.
  • Lu, K., Lao,H. and Zavadskas,E.K. (2020) An overview of fuzzy techniques in supply chain management: Bibliometrics, methodologies, applications and future directions, Annals of Operations Research, 290(1), 1–30. https://doi.org/10.1007/s10479-018-2981-5.
  • Petrovic, D., Roy, R. and Petrovic, R. (1999) Supply chain modelling using fuzzy sets, International Journal of Production Economics, 59(1–3), 443–453. https://doi.org/10.1016/S0925-5273(98)00076-9.
  • Raza, S.A. and Govindaluri, S.M. (2019) Greening and price differentiation coordination in a supply chain with partial demand information and cannibalization, Journal of Cleaner Production, 229(1), 706–726. https://doi.org/10.1016/j.jclepro.2019.04.057.
  • Ryu, K. and Yücesan, E. (2010) A fuzzy newsvendor approach to supply chain coordination, European Journal of Operational Research, 200(2), 421–438. https://doi.org/10.1016/j.ejor.2009.01.037.
  • Safari, M. and Sahraeian, R. (2023) A chance-constraint optimization model for a multiechelon multi-product closed-loop supply chain considering brand diversity: An accelerated Benders decomposition algorithm, Computers & Operations Research, 149, 106130. https://doi.org/10.1016/j.cor.2022.106130
  • Sahin, B., Yazir, D., Hamid, A. A., and Abdul Rahman, N. S. F. (2021) Maritime supply chain optimization by using fuzzy goal programming, Algorithms, 14(8), 234. https://doi.org/10.3390/a14080234.
  • Taleizadeh, A.A., Cardenas-Barron, L.E. and Sohani,R. (2019) Coordinating The SupplierRetailer Supply Chain Under Noise Effect with Bundling and Inventory Strategies, Journal of Industrial And Management Optimization, 15, 1701-1727. https://doi.org/10.3934/jimo.2018153.
  • Tuan, D.H. and Chiadamrong, N. (2021) A fuzzy credibility-based chance-constrained optimization model for multiple-objective aggregate production planning in a supply chain under an uncertain environment, Engineering Journal, 25(4), 1–19. https://doi.org/10.4186/ej.2021.25.4.1.
  • Xu, Z., Zhang, Y. and Liu, B. (2024) Supply chain management based on uncertainty theory: A bibliometric analysis and future prospects, Annals of Operations Research. https://doi.org/10.1007/s10700-024-09435-9
  • Xue, F., Tang, W. and Zhao, R. (2008) The expected value of a function of a fuzzy variable with a continuous membership function, Computers & Mathematics with Applications, 55(6), 1215–1224. https://doi.org/10.1016/j.camwa.2007.05.025.
  • Wu, H., Chen, J. and Li, X. (2024) Chance-constrained optimization for a green multimodal routing problem with soft time window under twofold uncertainty, Axioms, 13(3), 200. https://doi.org/10.3390/axioms13030200
  • Zadeh, L.A. (1978) Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1(1), 3–28. https://doi.org/10.1016/0165-0114(78)90029-5.

CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS

Yıl 2026, Cilt: 31 Sayı: 1 , 369 - 386 , 10.04.2026
https://doi.org/10.17482/uumfd.1492897
https://izlik.org/JA27BB63KD

Öz

Coordination is one of the most critical challenges in supply chain management. Depending on the level of coordination, supply chains can be categorized as either centralized or decentralized. In a centralized model, a single decision-maker seeks to optimize the performance of the entire supply chain by minimizing overall costs and maximizing system-wide efficiency. In contrast, in a decentralized model, each member independently pursues its own profit objectives. This study examines a two-stage supply chain structure consisting of a supplier and a retailer under fuzzy production cost parameters. To address the uncertainty, the fuzzy chance-constrained programming (FCCP) method is applied to determine the optimal order quantities that maximize the total profit in both centralized and decentralized settings. Unlike most previous studies that analyze these structures separately, this research provides a comparative framework integrating FCCP with credibility theory across both coordination mechanisms. In addition, the decentralized model incorporates a goal programming structure to capture the conflicting objectives of independent members. This unified approach offers both methodological novelty and practical insights for decision-making under uncertainty in supply chain coordination. 

Kaynakça

  • Arık, O. A. (2019). Credibility-based chance constrained programming for project scheduling with fuzzy activity durations. An International Journal of Optimization and Control: Theories & Applications, 9(2), 208–215. https://doi.org/10.11121/ijocta.01.2019.00631
  • Arshinder, A., Kanda, S. and Deshmukh, G. (2007) Coordination in supply chains: An evaluation using fuzzy logic, Production Planning & Control, 18(8), 420–435. https://doi.org/10.1080/09537280701430994.
  • Bilgen, B. (2010) Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem, Expert Systems with Applications, 37(6), 4488–4495. https://doi.org/10.1016/j.eswa.2009.12.062.
  • Charles, V., Gupta, S. and Ali, İ. (2018) Fuzzy goal programming approach for solving multiobjective supply chain network problems under probabilistic and fuzzy uncertainty, Annals of Operations Research, 269(1–2), 489–512. https://doi.org/10.1007/s10479-017-2524-1.
  • Charnes, A. and Cooper, W.W. (1959) Chance-constrained programming, Management Science, 6(1), 73–79. https://doi.org/10.1287/mnsc.6.1.73.
  • Choi, T.Y. and Guo, S. (2023) Manufacturing firms’ credibility towards customers and operational performance: The counteracting roles of corruption and ICT readiness, International Journal of Production Research, 61(15), 5172–5190. https://doi.org/10.1080/00207543.2023.2169666
  • Gholami, Z., Jolai, F. and Torabi, S.A. (2022) Robust-fuzzy optimization approach in design of sustainable lean supply chain network under uncertainty, Computational and Applied Mathematics, 41(4), 136. https://doi.org/10.1007/s40314-022-01936-w
  • Giri, B.C. and Sarker, B.R. (2019) Coordinating A Multi-Echelon Supply Chain Under Production Disruption and Price-Sensitive Stochastic Demand, Journal of Industrial And Management Optimization, 15,1631-1651. https://doi.org/10.3934/jimo.2018115.
  • Huang, X. (2010) Portfolio analysis: From probabilistic to credibilistic and uncertain approaches, Studies in Fuzziness and Soft Computing, vol. 250. https://doi.org/10.1007/978-3-642-11214-0.
  • Klir, G.J. and Yuan, B. (1995) Fuzzy sets and fuzzy logic: Theory and applications, Prentice Hall PTR, Upper Saddle River, NJ.
  • Lee, H.L. and Billington, C. (1993) Material management in decentralized supply chains, Operations Research, 41(3), 835–847. https://doi.org/10.1287/opre.41.5.835.
  • Liu, B. (2007) Uncertainty theory, 2nd edn, Springer, Berlin. https://doi.org/10.1007/978-3-540-73165-8.
  • Liu, B. and Iwamura, K. (1996) Chance constrained programming with fuzzy parameters, Fuzzy Sets and Systems, 94(2), 227–237. https://doi.org/10.1016/0165-0114(95)00204-4.
  • Liu, B. and Liu, Y.K. (2002) Expected value of fuzzy variable and fuzzy expected value model, IEEE Transactions on Fuzzy Systems, 10(4), 445–450.https://doi.org/10.1109/TFUZZ.2002.800692.
  • Lotfi, R., Kargar, B., Rajabzadeh, M., Hesabi, F. and Özceylan,E. (2022) Hybrid fuzzy and data-driven robust optimization for resilience and sustainable healthcare supply chains with a VMI approach, Environmental Science and Pollution Research, 29(12), 17645–17663. https://doi.org/10.1007/s11356-021-17053-3.
  • Lu, K., Lao,H. and Zavadskas,E.K. (2020) An overview of fuzzy techniques in supply chain management: Bibliometrics, methodologies, applications and future directions, Annals of Operations Research, 290(1), 1–30. https://doi.org/10.1007/s10479-018-2981-5.
  • Petrovic, D., Roy, R. and Petrovic, R. (1999) Supply chain modelling using fuzzy sets, International Journal of Production Economics, 59(1–3), 443–453. https://doi.org/10.1016/S0925-5273(98)00076-9.
  • Raza, S.A. and Govindaluri, S.M. (2019) Greening and price differentiation coordination in a supply chain with partial demand information and cannibalization, Journal of Cleaner Production, 229(1), 706–726. https://doi.org/10.1016/j.jclepro.2019.04.057.
  • Ryu, K. and Yücesan, E. (2010) A fuzzy newsvendor approach to supply chain coordination, European Journal of Operational Research, 200(2), 421–438. https://doi.org/10.1016/j.ejor.2009.01.037.
  • Safari, M. and Sahraeian, R. (2023) A chance-constraint optimization model for a multiechelon multi-product closed-loop supply chain considering brand diversity: An accelerated Benders decomposition algorithm, Computers & Operations Research, 149, 106130. https://doi.org/10.1016/j.cor.2022.106130
  • Sahin, B., Yazir, D., Hamid, A. A., and Abdul Rahman, N. S. F. (2021) Maritime supply chain optimization by using fuzzy goal programming, Algorithms, 14(8), 234. https://doi.org/10.3390/a14080234.
  • Taleizadeh, A.A., Cardenas-Barron, L.E. and Sohani,R. (2019) Coordinating The SupplierRetailer Supply Chain Under Noise Effect with Bundling and Inventory Strategies, Journal of Industrial And Management Optimization, 15, 1701-1727. https://doi.org/10.3934/jimo.2018153.
  • Tuan, D.H. and Chiadamrong, N. (2021) A fuzzy credibility-based chance-constrained optimization model for multiple-objective aggregate production planning in a supply chain under an uncertain environment, Engineering Journal, 25(4), 1–19. https://doi.org/10.4186/ej.2021.25.4.1.
  • Xu, Z., Zhang, Y. and Liu, B. (2024) Supply chain management based on uncertainty theory: A bibliometric analysis and future prospects, Annals of Operations Research. https://doi.org/10.1007/s10700-024-09435-9
  • Xue, F., Tang, W. and Zhao, R. (2008) The expected value of a function of a fuzzy variable with a continuous membership function, Computers & Mathematics with Applications, 55(6), 1215–1224. https://doi.org/10.1016/j.camwa.2007.05.025.
  • Wu, H., Chen, J. and Li, X. (2024) Chance-constrained optimization for a green multimodal routing problem with soft time window under twofold uncertainty, Axioms, 13(3), 200. https://doi.org/10.3390/axioms13030200
  • Zadeh, L.A. (1978) Fuzzy sets as a basis for a theory of possibility, Fuzzy Sets and Systems, 1(1), 3–28. https://doi.org/10.1016/0165-0114(78)90029-5.
Toplam 27 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Endüstri Mühendisliği, Üretim ve Endüstri Mühendisliği (Diğer)
Bölüm Araştırma Makalesi
Yazarlar

Gülçin Canbulut 0000-0001-7106-4528

Gönderilme Tarihi 30 Mayıs 2024
Kabul Tarihi 26 Ocak 2026
Yayımlanma Tarihi 10 Nisan 2026
DOI https://doi.org/10.17482/uumfd.1492897
IZ https://izlik.org/JA27BB63KD
Yayımlandığı Sayı Yıl 2026 Cilt: 31 Sayı: 1

Kaynak Göster

APA Canbulut, G. (2026). CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, 31(1), 369-386. https://doi.org/10.17482/uumfd.1492897
AMA 1.Canbulut G. CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS. UUJFE. 2026;31(1):369-386. doi:10.17482/uumfd.1492897
Chicago Canbulut, Gülçin. 2026. “CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 31 (1): 369-86. https://doi.org/10.17482/uumfd.1492897.
EndNote Canbulut G (01 Nisan 2026) CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 31 1 369–386.
IEEE [1]G. Canbulut, “CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS”, UUJFE, c. 31, sy 1, ss. 369–386, Nis. 2026, doi: 10.17482/uumfd.1492897.
ISNAD Canbulut, Gülçin. “CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi 31/1 (01 Nisan 2026): 369-386. https://doi.org/10.17482/uumfd.1492897.
JAMA 1.Canbulut G. CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS. UUJFE. 2026;31:369–386.
MLA Canbulut, Gülçin. “CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS”. Uludağ Üniversitesi Mühendislik Fakültesi Dergisi, c. 31, sy 1, Nisan 2026, ss. 369-86, doi:10.17482/uumfd.1492897.
Vancouver 1.Gülçin Canbulut. CHANCE CONSTRAINT PROGRAMMING MODEL FOR SUPPLY CHAIN MANAGEMENT WITH FUZZY COST PARAMETERS. UUJFE. 01 Nisan 2026;31(1):369-86. doi:10.17482/uumfd.1492897

DUYURU:

30.03.2021- Nisan 2021 (26/1) sayımızdan itibaren TR-Dizin yeni kuralları gereği, dergimizde basılacak makalelerde, ilk gönderim aşamasında Telif Hakkı Formu yanısıra, Çıkar Çatışması Bildirim Formu ve Yazar Katkısı Bildirim Formu da tüm yazarlarca imzalanarak gönderilmelidir. Yayınlanacak makalelerde de makale metni içinde "Çıkar Çatışması" ve "Yazar Katkısı" bölümleri yer alacaktır. İlk gönderim aşamasında doldurulması gereken yeni formlara "Yazım Kuralları" ve "Makale Gönderim Süreci" sayfalarımızdan ulaşılabilir. (Değerlendirme süreci bu tarihten önce tamamlanıp basımı bekleyen makalelerin yanısıra değerlendirme süreci devam eden makaleler için, yazarlar tarafından ilgili formlar doldurularak sisteme yüklenmelidir).  Makale şablonları da, bu değişiklik doğrultusunda güncellenmiştir. Tüm yazarlarımıza önemle duyurulur.

Bursa Uludağ Üniversitesi, Mühendislik Fakültesi Dekanlığı, Görükle Kampüsü, Nilüfer, 16059 Bursa. Tel: (224) 294 1907, Faks: (224) 294 1903, e-posta: mmfd@uludag.edu.tr