Bozulabilir ürünler ve tedarikçi belirsizliği altında sürdürülebilir kapalı döngü tedarik zincirlerinde tedarikçi seçimi ve sipariş tahsisi

Öz

Arka Plan—Yoğun rekabet ortamı ve yaşanan küresel değişimlerin etkisiyle günümüz iş dünyasında, etkili ve sürdürülebilir tedarik zinciri yönetimi (TZY) stratejik bir zorunluluk haline gelmiştir. Organizasyonlar, geri dönüşüm ve yeniden kullanım uygulamalarını süreçlerine entegre ederken malzeme ve finansal akışların verimliliğini artırmak için Kapalı Döngü Tedarik Zincirlerine (KDTZ) yönelmektedir. Sağlık sektörü, bozulabilir ürünlerin yönetiminde özellikle operasyonel verimlilik ile çevresel sürdürülebilirliği dengeleyen hassas bir KDTZ’ne ihtiyaç duymaktadır.

Amaç—Bu çalışmada, sürdürülebilir bir tedarik zinciri ağ tasarımını kolaylaştırmak amacıyla KDTZ kapsamında sipariş tahsisine yönelik özgün bir optimizasyon modeli önerilmektedir. Bu çalışma; tedarikçi değerlendirme süreçlerini gözeterek kapasite belirsizliklerini yönetmeyi ve bozulabilir ürünlerin depolama sürelerini izleyip sipariş tahsisini ve raf ömrü dolan ürünlerin geri dönüşüm merkezlerine sevkiyatını optimize etmeyi amaçlamaktadır.

Yöntem—Araştırmada çok aşamalı hibrit bir metodoloji kullanılmıştır. Matematiksel model olarak, talep ve kapasite dalgalanmalarının yarattığı zorlukları ele almak için senaryo tabanlı bir Karma Tamsayılı Doğrusal Programlama (MILP) modeli geliştirilmiştir. Söz konusu modelde tedarikçi değerlendirmesini sipariş tahsisi aşamasında gözlemleyebilmek adına tedarikçi seçimi problemi de ikinci aşamada dikkate alınmıştır. Tedarikçi değerlendirmesinde kriter çerçevesi; Yalın, Çevik, Dayanıklı ve Yeşil (LARG) ilkelerinin yanı sıra pandemiye özgü hususlar temel alınarak gerçekleştirilmiştir. Karar verme algoritması olarak, objektif kriterlerin ağırlıklandırılmasında Tam Tutarlılık Yöntemi (FUCOM) kullanılmıştır. Uzman görüşlerinin mesleki deneyimlerine dayalı olarak değerlendirilmesinde Sezgisel Bulanık Kümeler (IFS) kullanılmıştır. Tedarikçileri değerlendirmek ve sıralamak için ise Karmaşık Orantılı Değerlendirme (COPRAS) yönteminden yararlanılmıştır. Uygulama olarak modelin doğrulaması, Türkiye'deki bir hastanenin ağ tasarımını içeren bir vaka çalışması ile gerçekleştirilmiştir.

Bulgular—Tedarikçi puanları, ürün raf ömrü ve kapasite gibi faktörler üzerinde yapılan kapsamlı bir duyarlılık analiziyle desteklenen bulgular, KDTZ’nin performansını optimize etmede tedarikçi seçiminin ve bozulabilirlik takibinin önemli olduğunu göstermektedir. Önerilen model, bozulabilir ürünlerin envanterini yöneterek son kullanma tarihinden önce zamanında müdahale edilmesini sağlayacak şekilde tasarlanmıştır. LARG tabanlı tedarikçi puanlarının MILP modeline entegrasyonunun, ağın yanıt verebilirliği üzerinde etkili olduğu gözlemlenmiştir.

Sonuç—Bu çalışma, sürdürülebilir sağlık hizmetleri operasyonlarının sağlanmasında stratejik tedarikçi seçiminin ve etkili envanter yönetiminin kritik rolünü vurgulamaktadır. Sunulan kapsamlı yaklaşım, karar vericilerin pandemi sonrası dünyada uzun vadeli sürdürülebilirliği ve operasyonel çevikliği sürdürebilmeleri için bir çerçeve sunmaktadır.

Anahtar Kelimeler

• Karma tamsayılı doğrusal programlama
• Çok kriterli karar verme
• Bozulabilirlik
• Tedarikçi seçimi
• Tedarikçi belirsizliği

Etik Beyan

Yazar, bu makalede sunulan araştırmanın etik standartlara uygun olduğunu beyan eder. Bu çalışmada insan katılımcılar veya hayvanlar yer almamıştır.

Supplier selection and order allocation in sustainable closed-loop supply chains with product perishability and supplier uncertainty

Öz

Context—In the contemporary business environment, characterized by intense competition and rapid global shifts, effective and sustainable supply chain management (SCM) has become a paramount necessity. It is evident that organizations are increasingly relying on Closed-Loop Supply Chains (CLSCs) to enhance the efficiency of material and financial flows while incorporating recycling and reuse practices. The healthcare sector requires a nuanced approach to managing perishable products, especially balancing operational efficiency with environmental sustainability.

Objective—The present study proposes a novel optimization model for order allocation within a CLSC to facilitate the design of a sustainable supply chain network. The objective of this research is to manage uncertainties in supplier capacity while monitoring the storage duration of perishable goods to optimize their dispatch to recycling centers.

Method—The research employs a multi-stage hybrid methodology. As a mathematical model, a scenario-based Mixed-Integer Linear Programming (MILP) model has been developed to address the challenges posed by demand and capacity fluctuations. As a criteria framework, the evaluation of suppliers is conducted based on the Lean, Agile, Resilient, and Green (LARG) principles, along with considerations specific to the pandemic. As a decision-making algorithm, the Full Consistency Method (FUCOM) is used for objective criteria weighting. Intuitionistic Fuzzy Sets (IFS) are used to evaluate expert opinions based on their professional experience. The Complex Proportional Assessment (COPRAS) method is utilized to evaluate and rank suppliers. As an application, the model's validation is achieved through a case study involving the network design of a hospital in Türkiye.

Results—The findings, supported by an extensive sensitivity analysis on factors such as supplier scores, product shelf life, and capacity, indicate that supplier selection is the most pivotal factor in optimizing the performance of a closed-loop supply chain. The proposed model has been designed to manage the inventory of perishable goods, ensuring timely intervention before expiration. The integration of LARG-based supplier scores into the MILP model has been demonstrated to significantly enhance the responsiveness of the network.

Conclusion—This study emphasizes the pivotal role of strategic supplier selection and effective inventory management in ensuring sustainable healthcare operations. The comprehensive approach presented offers a resilient framework for decision-makers to maintain long-term sustainability and operational agility in a post-pandemic world.

Anahtar Kelimeler

• MILP
• Order allocation
• Perishability
• Supplier selection
• Supplier uncertainty

Etik Beyan

The author declares that the research presented in this manuscript complies with ethical standards. No human participants or animals were involved in this study.

Kaynakça

1. T. M. Choi, “Fighting against COVID-19: what operations research can help and the sense-and-respond framework”, Annals of Operations Research, 360(1), 97-113, 2026, https://doi.org/10.1007/s10479-021-03973-w.
2. F. Amimo, B. Lambert, A. Magit, M. Hashizume, “A review of prospective pathways and impacts of COVID-19 on the accessibility, safety, quality, and affordability of essential medicines and vaccines for universal health coverage in Africa”, Globalization and Health, 17(1), 42, 2021. https://doi.org/10.1186/s12992-021-00666-8.
3. A. Majumdar, M. Shaw, S. K. Sinha, “COVID-19 debunks the myth of socially sustainable supply chain: A case of the clothing industry in South Asian countries”, Sustainable Production and Consumption, 24, 150–155, 2020. https://doi.org/10.1016/j.spc.2020.07.001.
4. F. Naz, A. Kumar, A. Majumdar, R. Agrawal, “Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research”, Operations Management Research, 15(1-2), 378-398, 2022. https://doi.org/10.1007/s12063-021-00208-w.
5. R. D. Tordecilla, A. A. Juan, J. R. Montoya-Torres, C. L. Quintero-Araujo, J. Panadero, “Simulation-optimization methods for designing and assessing resilient supply chain networks under uncertainty scenarios: A review”, Simulation Modeling Practice and Theory, 106, 102166, 2021. https://doi.org/10.1016/j.simpat.2020.102166.
6. P. Suryawanshi, P. Dutta, “Optimization models for supply chains under risk, uncertainty, and resilience: A state-of-the-art review and future research directions”, Transportation Research Part E-Logistics and Transportation Review, 157, 102553, 2022. https://doi.org/10.1016/j.tre.2021.102553.
7. M. Rauf, M. Imran, J. Mumtaz, S. Javed, A. Saeed, “Multi-period supply chain optimization with contango and backwardation effects using an improved hybrid genetic algorithm”, Decision Science Letters, 14(3), 587–602, 2025. https://doi.org/10.5267/j.dsl.2025.5.001.
8. U. Illahi, M. S. Mir, “Maintaining efficient logistics and supply chain management operations during and after coronavirus (COVID-19) pandemic: learning from the past experiences”, Environment Development and Sustainability, 23(8), 11157–11178, 2021. https://doi.org/10.1007/s10668-020-01115-z.

9. R. Sharma, A. Shishodia, S. Kamble, A. Gunasekaran, A. Belhadi, “Agriculture supply chain risks and COVID-19: mitigation strategies and implications for the practitioners”, International Journal of Logistics Research and Applications, 27(11), 2351-2377, 2024. https://doi.org/10.1080/13675567.2020.1830049.
10. E. Taghizadeh, S. Venkatachalam, R. B. Chinnam, “A dynamic resilience management framework for deep-tier supply networks”, Cleaner Logistics and Supply Chain, 13, 100174, 2024. https://doi.org/10.1016/j.clscn.2024.100174.
11. H. Carvalho, S. Azevedo, V. Cruz-Machado, “Trade-offs among Lean, Agile, Resilient and Green Paradigms in Supply Chain Management: A Case Study Approach”, J. Xu, J. Fry, B. Lev, A. Hajiyev, Eds., Proceedings of the Seventh International Conference on Management Science and Engineering Management, 1st Edition, Springer Berlin, Heidelberg, 2014, 953–968. https://doi.org/10.1007/978-3-642-40081-0_81.
12. J. M. Bloemhof, M. Soysal, “Sustainable Food Supply Chain Design”, Y. Bouchery, C. Corbett, J. Fransoo, T. Tan, Eds., Sustainable Supply Chains, 1st Edition, Springer Cham, 2017, 395–412. https://doi.org/10.1007/978-3-319-29791-0_18.
13. S. G. Azevedo, H. Carvalho, V. Cruz-Machado, “LARG index A benchmarking tool for improving the leanness, agility, resilience and greenness of the automotive supply chain”, Benchmarking-An International Journal, 23(6), 1472–1499, 2016. https://doi.org/10.1108/BIJ-07-2014-0072.
14. R. D. Raut, S. K. Mangla, V. S. Narwane, M. Dora, M. Q. Liu, “Big Data Analytics as a mediator in Lean, Agile, Resilient, and Green (LARG) practices effects on sustainable supply chains”, Transportation Research Part E-Logistics and Transportation Review, 145, 102170, 2021. https://doi.org/10.1016/j.tre.2020.102170.
15. F. Lanzalonga, S. Likavec, P. P. Biancone, “Balancing profit and sustainability: Assessing the impact of European Union environmental policies on the automotive sector” Research in Transportation Business and Management, 60, 101359, 2025. https://doi.org/10.1016/j.rtbm.2025.101359.
16. V. D. R. Guide, L. N. Van Wassenhove, “The evolution of closed-loop supply chain research”, Operations Research, 57(1), 10–18, 2009. https://doi.org/10.1287/opre.1080.0628.
17. K. S. Moghaddam, “Supplier selection and order allocation in closed-loop supply chain systems using hybrid Monte Carlo simulation and goal programming”, International Journal of Production Research, 53(20), 6320–6338, 2015. https://doi.org/10.1080/00207543.2015.1054452.
18. M. Mohammadzadeh, A. A. Khamseh, M. Mohammadi, “A multi-objective integrated model for closed-loop supply chain configuration and supplier selection considering uncertain demand and different performance levels”, Journal of Industrial and Management Optimization, 13(2), 1041–1064, 2017. https://doi.org/10.3934/jimo.2016061.
19. S. Ahmadi, S. H. Amin, “An integrated chance-constrained stochastic model for a mobile phone closed-loop supply chain network with supplier selection”, Journal of Cleaner Production, 226, 988–1003, 2019. https://doi.org/10.1016/j.jclepro.2019.04.132.
20. K. Govindan, J. D. Darbari, V. Agarwal, P. C. Jha, “Fuzzy multi-objective approach for optimal selection of suppliers and transportation decisions in an eco-efficient closed loop supply chain network”, Journal of Cleaner Production, 165, 1598–1619, 2017. https://doi.org/10.1016/j.jclepro.2017.06.180.
21. S. H. Amin, G. Zhang, “An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach”, Expert Systems with Applications, 39(8), 6782–6791, 2012. https://doi.org/10.1016/j.eswa.2011.12.056.
22. G. Cavone, M. Dotoli, N. Epicoco, D. Morelli, C. Seatzu, “Design of Modern Supply Chain Networks Using Fuzzy Bargaining Game and Data Envelopment Analysis”, IEEE Transactions on Automation Science and Engineering, 17(3), 1221–1236, 2020. https://doi.org/10.1109/TASE.2020.2977452.
23. S. J. Ghoushchi, I. Hushyar, “Designing a closed-loop supply chain network and providing a multi-objective mathematical model to select a third-party logistics company and supplier simultaneously”, International Journal of Industrial Engineering-Theory Applications and Practice, 27(2), 245-275, 2020.
24. A. K. Nasr, M. Tavana, B. Alavi, H. Mina, “A novel fuzzy multi-objective circular supplier selection and order allocation model for sustainable closed-loop supply chains”, Journal of Cleaner Production, 287, 124994, 2021. https://doi.org/10.1016/j.jclepro.2020.124994.
25. S. Rouhani, S. H. Amin, L. Wardley, “A novel multi-objective robust possibilistic flexible programming to design a sustainable apparel closed-loop supply chain network”, Journal of Environmental Management, 365, 121496, 2024. https://doi.org/10.1016/j.jenvman.2024.121496.
26. N. Varshney, S. Gupta, A. Ahmed, “Addressing uncertainty in closed-loop supply chain networks: a multi-objective approach to integrated production and transportation problems Closed-loop supply chain networks”, Journal of Modelling in Management, 19(6), 1849–1882, 2024. https://doi.org/10.1108/JM2-01-2024-0011.
27. S. B. Ebrahimi, “A stochastic multi-objective location-allocation-routing problem for tire supply chain considering sustainability aspects and quantity discounts”, Journal of Cleaner Production, 198, 704–720, 2018. https://doi.org/10.1016/j.jclepro.2018.07.059.
28. S. M. Kisomi, M. Solimanpur, A. Doniavi, “An integrated supply chain configuration model and procurement management under uncertainty: A set-based robust optimization methodology”, Applied Mathematical Modeling, 40(17–18), 7928–7947, 2016. https://doi.org/10.1016/j.apm.2016.03.047.
29. S. Rezaei, I. Ghalehkhondabi, M. Rafiee, S. N. Zanganeh, “Supplier selection and order allocation in CLSC configuration with various supply strategies under disruption risk”, Opsearch, 57(3), 908–934, 2020. https://doi.org/10.1007/s12597-020-00445-w.
30. A. J. Ruiz-Torres, F. Mahmoodi, S. Ohmori, A. Hlali, “Suppliers portfolio and returner incentive decisions in closed-loop remanufacturing systems under multiple stochastic scenarios”, International Journal of Sustainable Engineering, 15(1), 210–227, 2022. https://doi.org/10.1080/19397038.2022.2110330.
31. M. Momenitabar, Z. D. Ebrahimi, M. Arani, J. Mattson, “Robust possibilistic programming to design a closed-loop blood supply chain network considering service-level maximization and lateral resupply”, Annals of Operations Research., 328(1), 859–901, 2023. https://doi.org/10.1007/s10479-022-04930-x.
32. G. Ghiani, G. Laporte, R. Musmanno, Introduction to Logistics Systems Management, 2nd Edition, John Wiley & Sons, 2013.
33. S. Chopra, P. Meindl, Supply chain management: strategy, planning, and operation, 6th Edition, Pearson, 2016.
34. M. Soysal, J. M. Bloemhof-Ruwaard, R. Haijema, J. G. A. J. Van Der Vorst, “Modeling an Inventory Routing Problem for perishable products with environmental considerations and demand uncertainty”, International Journal of Production Economics, 164, 118–133, 2015. https://doi.org/10.1016/j.ijpe.2015.03.008.
35. S. Erişkan, “Tedari̇kçi̇ seçi̇mi̇ ve entegre kapalı döngü tedari̇k zi̇nci̇ri̇ ağ tasarımı problemleri̇ i̇çi̇n bi̇r karar destek si̇stemi̇”, Doktora Tezi, Burdur Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü, Burdur, Türkiye, 2023.
36. A. Çınar, Ö. Uygun, “Sezgisel Bulanık AHP Yöntemiyle Yeşil Tedarikçi Seçimi”, Zeki Sistemler Teori ve Uygulamaları Dergisi, 2(2), 24–31, 2019. https://doi.org/10.38016/jista.515881.
37. K. T. Atanassov, “Intuitionistic fuzzy sets”, Fuzzy Sets and Systems, 20(1), 87–96, 1986. https://doi.org/10.1016/S0165-0114(86)80034-3.
38. Z. B. Kanık, S. Erişkan, M. Soysal, M. Ömürgönülşen, “A hybrid approach based on qualitative and quantitative techniques for analyzing last-mile parcel delivery”, Opsearch, early access, 2025. https://doi.org/10.1007/s12597-025-01017-6.
39. H. W. Liu, G. J. Wang, “Multi-criteria decision-making methods based on intuitionistic fuzzy sets”, European Journal of Operational Research, 179(1), 220–233, 2007. https://doi.org/10.1016/j.ejor.2006.04.009.
40. M. S. Yurtyapan, “Kurumsal kaynak planlaması yazılım tercihinde Macbeth karar verme metodunun aralık gri sayı yaklaşımı ile geliştirilmesi”, Yüksek Lisans Tezi, Süleyman Demirel Üniversitesi Fen Bilimleri Enstitüsü, Isparta, Türkiye. 2019.
41. B. D. Rouyendegh, A. Yildizbasi, P. Üstünyer, “Intuitionistic Fuzzy TOPSIS Method for Green Supplier Selection Problem”, Soft Computing, 24(3), 2215–2228, 2020. https://doi.org/10.1007/s00500-019-04054-8.
42. M. S. Yurtyapan, E. Aydemir, “ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method”, Grey Systems Theory & Application, 12(1), 78–100, 2022. https://doi.org/10.1108/GS-01-2021-0002.
43. H. Z. Kine, Z. Shiferaw, G. Gebresenbet, L. Tavasszy, D. Ljungberg, “GIS based multi-criteria decision-making approach for dry port location analysis: The case of Ethiopia”, Research in Transportation Business and Management, 60, 101370, 2025. https://doi.org/10.1016/j.rtbm.2025.101370.
44. D. Pamucar, I. Badi, K. Sanja, R. Obradovic, “A Novel Approach for the Selection of Power-Generation Technology Using a Linguistic Neutrosophic CODAS Method: A Case Study in Libya”, Energies, 11(9), 2489, 2018. https://doi.org/10.3390/en11092489.
45. Y. Şahin, K. Karagül, “FUCOM ve COPRAS Yöntemleriyle ERP Yazılımı Seçimi,” 4th International Conference on Applied Engineering and Natural Sciences, Konya, Türkiye, 10–13 November 2022.
46. F. Ecer, “FUCOM sübjektif ağırlıklandırma yöntemi ile rüzgâr çiftliği yer seçimini etkileyen faktörlerin analizi”, Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 27(1), 24–34, 2021. https://doi.org/10.5505/pajes.2020.93271.
47. D. Pamucar, Z. Stevic, S. Sremac, “A new model for determining weight coefficients of criteria in mcdm models: Full consistency method (fucom),” Symmetry-Basel, 10(9), 393, 2018. https://doi.org/10.3390/sym10090393.
48. G. Fancello, M. Carta, P. Serra, “Data Envelopment Analysis for the assessment of road safety in urban road networks: A comparative study using CCR and BCC models”, Case Studies on Transport Policy, 8(3), 736–744, 2020. https://doi.org/10.1016/j.cstp.2020.07.007.
49. E. Kazimieras Zavadskas, A. Kaklauskas, V. Šarka, “The new method of multicriteria complex proportional assessment of projects”, Technological and Economic Development of Economy, 1(3), 131–139, 1994, https://www.researchgate.net/publication/285902199.
50. S. Hezer, E. Gelmez, E. Özceylan, “Comparative analysis of TOPSIS, VIKOR and COPRAS methods for the COVID-19 Regional Safety Assessment”, Journal of Infection and Public Health, 14(6), 775–786, 2021. https://doi.org/10.1016/j.jiph.2021.03.003.
51. B. Matic, S. Jovanovic, D. K. Das, E. K. Zavadskas, Z. Stevic, S. Sremac, M. Marinkovic, “A new hybrid MCDM model: sustainable supplier selection in a construction company”, Symmetry-Basel, 11(3), 353, 2019. https://doi.org/10.3390/sym11030353.
52. S. Seker, F. B. Baglan, N. Aydin, M. Deveci, W. P. Ding, “Risk assessment approach for analyzing risk factors to overcome pandemic using interval-valued q-rung orthopair fuzzy decision-making method”, Applied Soft Computing, 132, 109891, 2023. https://doi.org/10.1016/j.asoc.2022.109891.
53. Y. H. Zheng, Z. S. Xu, Y. He, H. C. Liao, “Severity assessment of chronic obstructive pulmonary disease based on hesitant fuzzy linguistic COPRAS method,” Applied Soft Computing, 69, 60–71, 2018. https://doi.org/10.1016/j.asoc.2018.04.035.
54. M. Soysal, “Closed-loop Inventory Routing Problem for returnable transport items”, Transportation Research Part D-Transport and Environment, 48, 31–45, 2016. https://doi.org/10.1016/j.trd.2016.07.001.
55. A. Afrasiabi, M. Tavana, D. Di Caprio, “An extended hybrid fuzzy multi-criteria decision model for sustainable and resilient supplier selection”, Environmental Science and Pollution Research, 29(25), 37291-37314, 2022. https://doi.org/10.1007/s11356-021-17851-2.
56. A. Fallahpour, M. Yazdani, A. Mohammed, K. Y. Wong, “Green sourcing in the era of industry 4.0: towards green and digitalized competitive advantages”, Industrial Management & Data Systems, 121(9), 1997–2025, 2021. https://doi.org/10.1108/IMDS-06-2020-0343.
57. N. Goker, “A novel integrated intuitionistic fuzzy decision aid for agile outsourcing provider selection: a COVID-19 pandemic-based scenario analysis”, Soft Computing, 25(21), 13723–13740, 2021. https://doi.org/10.1007/s00500-021-06037-0.
58. M. Ilyas, S. Carpitella, E. Zoubir, “Designing supplier selection strategies under COVID-19 constraints for industrial environments”, Procedia CIRP, 100, 589–594, 2021. https://doi.org/10.1016/j.procir.2021.05.128.
59. G. M. Magableh, M. Z. Mistarihi, “Applications of MCDM approach (ANP-TOPSIS) to evaluate supply chain solutions in the context of COVID-19”, Heliyon, 8(3), e09062, 2022. https://doi.org/10.1016/j.heliyon.2022.e09062.
60. S. Pratap, Y. Daultani, A. Dwivedi, F. L. Zhou, “Supplier selection and evaluation in e-commerce enterprises: a data envelopment analysis approach”, Benchmarking-An International Journal, 29(1), 325–341, 2022. https://doi.org/10.1108/BIJ-10-2020-0556.
61. Z. L. Shang, X. Y. Yang, D. Barnes, C. Wu, “Supplier selection in sustainable supply chains: Using the integrated BWM, fuzzy Shannon entropy, and fuzzy MULTIMOORA methods”, Expert Systems with Applications, 195, 116567, 2022. https://doi.org/10.1016/j.eswa.2022.116567.
62. C. M. U-Dominic, I. J. Orji, M. Okwu, V. M. Mbachu, “The Impact of Covid-19 Pandemic on Sustainable Supplier Selection Process”, Advancing Industrial Engineering in Nigeria through Teaching, Research and Innovation, Department of Industrial and Production Engineering, University of Ibadan, 2020, https://www.researchgate.net/publication/350637845.
63. A. Mohammed, M. Yazdani, A. Oukil E. D. R. S. Gonzalez, “A Hybrid MCDM Approach towards Resilient Sourcing”, Sustainability, 13(5), 2695, 2021. https://doi.org/10.3390/su13052695.
64. Z. Wang, F. Y. Xiao, Z. H. Cao, “Uncertainty measurements for Pythagorean fuzzy set and their applications in multiple-criteria decision making”, Soft Computing, 26(19), 9937–9952, 2022. https://doi.org/10.1007/s00500-022-07361-9.
65. I. J. Orji, F. Ojadi, “Investigating the COVID-19 pandemic’s impact on sustainable supplier selection in the Nigerian manufacturing sector”, Computers and Industrial Engineering, 160, 107588, 2021. https://doi.org/10.1016/j.cie.2021.107588.
66. Y. F. Shao, D. Barnes C. Wu, “Sustainable supplier selection and order allocation for multinational enterprises considering supply disruption in COVID-19 era”, Australian Journal of Management, 48(2), 284–322, 2023. https://doi.org/10.1177/03128962211066953.
67. S. H. H. Petrudi, H. B. Ahmadi, A. Rehman, J. J. H. Liou, “Assessing suppliers considering social sustainability innovation factors during COVID-19 disaster”, Sustainable Production and Consumption, 27, 1869–1881, 2021. https://doi.org/10.1016/j.spc.2021.04.026.
68. T. Chen, Y. C. Wang, “A calibrated piecewise-linear FGM approach for travel destination recommendation during the COVID-19 pandemic”, Applied Soft Computing, 109, 107535, 2021. https://doi.org/10.1016/j.asoc.2021.107535.
69. M. Tavana, A. R. Abtahi, D. Di Caprio, R. Hashemi, R. Yousefi-Zenouz, “An integrated location-inventory-routing humanitarian supply chain network with pre- and post-disaster management considerations”, Socio-Economic Planning Science, 64, 21–37, 2018. https://doi.org/10.1016/j.seps.2017.12.004.
70. L. Poursoltan, S. M. Seyed-Hosseini, A. Jabbarzadeh, “Green Closed-Loop Supply Chain Network under the COVID-19 Pandemic”, Sustainability, 13(16), 9407, 2021. https://doi.org/10.3390/su13169407.
71. A. Mahmoudi, S. A. Javed, A. Mardani, “Gresilient supplier selection through Fuzzy Ordinal Priority Approach: decision-making in post-COVID era”, Operations Management Research, 15(1–2), 208–232, 2022. https://doi.org/10.1007/s12063-021-00178-z.