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

Year 2026, Volume: 31 Issue: 1 , 369 - 386 , 10.04.2026

Gülçin Canbulut

https://doi.org/10.17482/uumfd.1492897

https://izlik.org/JA27BB63KD

Abstract

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.

Keywords

tedarik zinciri , merkezi olmayan yapı , merkezi yapı , şans kısıtlı programlama , güvenilirlik teorisi

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

https://izlik.org/JA27BB63KD

Abstract

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. 

Keywords

supply chain , decentralized , centralized , chance constraint programming , credibility theory

References

• 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.