ESNEK TEDARİK ZİNCİRİ AĞ TASARIMI PROBLEMİ İÇİN BULANIK BÜTÜNLEŞİK BİR YAKLAŞIM

Year 2020, , 770 - 789, 30.09.2020

Fatma Demircan Keskin

https://doi.org/10.21076/vizyoner.661481

Cited By: 1

Abstract

Tedarik zinciri kesintileri, içsel ve dışsal faktörlere bağlı olarak ortaya çıkabilmekte ve tüm tedarik zinciri üyeleri için ciddi kayıplar doğurabilmektedir. Bu kesintiler ile başa çıkabilmek için bütüncül ve proaktif yaklaşımlar izlenerek esnek tedarik zincirleri oluşturmak gerekmektedir. Bu çalışmada esnek tedarik zinciri ağ tasarımı problemi, en çok uygulanan esnek tedarik zinciri oluşturma stratejilerinden tedarikçi güçlendirme ve yedek tedarikçi kullanma stratejilerini de göz önünde bulundurularak çeşitli kesinti senaryoları altında bulanık ortamda ele alınmıştır. Problemin çözümü için iki aşamalı bütünleşik bir yaklaşım önerilmiştir. Yaklaşımın ilk aşaması, Bulanık Analitik Hiyerarşi Prosesi ile tedarikçilerin değerlendirilmesini içermektedir. İkinci aşama ise tedarik zinciri ağ tasarımı için bir Bulanık Çok-Amaçlı Doğrusal Programlama modelinin oluşturulmasını kapsamaktadır. Yaklaşımın uygulaması, gerçekçi olarak üretilen bir problem üzerinde yapılmıştır. Elde edilen sonuçlar ve yaklaşımın uygulanabilirliği ile ilgili değerlendirmeler sunulmuştur.

Keywords

Bulanık Kümeler, Çok-Amaçlı Doğrusal Programlama, Çok Kriterli Karar Verme, Esneklik, Tedarik Zinciri Ağ Tasarımı

A FUZZY INTEGRATED APPROACH FOR RESILIENT SUPPLY CHAIN NETWORK DESIGN PROBLEM

Abstract

Supply chain disruptions can occur depending on internal and external factors and cause significant losses for all supply chain members. In order to cope with these disruptions, it is necessary to form resilient supply chain networks by pursuing holistic and proactive approaches. In the study, a resilient supply chain network design (SCND) problem is addressed under different disruption scenarios in a fuzzy environment by taking two of the most applied supply chain resilience strategies into account, namely the fortification of suppliers and using backup suppliers strategies. A two-stage integrated approach is proposed to solve the handled problem. The first stage includes the suppliers' evaluation process using the Fuzzy Analytic Hierarchy Process (F-AHP). A fuzzy Multi-Objective Linear Programming (F-MLP) model is developed to design the supply chain network in the second stage. The application of this approach is carried out on a realistic hypothetical problem and the results obtained and applicability of the proposed approach are discussed.

Keywords

Fuzzy Sets, Multi-Objective Linear Programming, Multi-Criteria Decision Making, Resilience, Supply Chain Network Design

References

• Alikhani, R., Torabi, S. A. and Altay, N. (2019). Strategic supplier selection under sustainability and risk criteria, International Journal of Production Economics, 208, 69-82.
• Amindoust, A. (2018). A resilient-sustainable based supplier selection model using a hybrid intelligent method, Computers & Industrial Engineering, 126, 122-135.
• Awasthi, A., Govindan, K. and Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach, International Journal of Production Economics, 195, 106-117.
• Bhamra, R., Dani, S. and Burnard, K. (2011). Resilience: the concept, a literature review and future directions, International Journal of Production Research, 49(18), 5375-5393.
• Chang, D. Y. (1996). Applications of the extent analysis method on fuzzy AHP, European Journal of Operational Research, 95(3), 649-655.
• Chiou, T. Y., Chan, H. K., Lettice, F. and Chung, S. H. (2011). The influence of greening the suppliers and green innovation on environmental performance and competitive advantage in Taiwan, Transportation Research Part E: Logistics and Transportation Review, 47(6), 822-836.
• Christopher, M. and Peck, H. (2004). Building the resilient supply chain, The International Journal of Logistics Management, 15(2), 1-14.
• Fahimnia, B. and Jabbarzadeh, A. (2016). Marrying supply chain sustainability and resilience: A match made in heaven, Transportation Research Part E: Logistics and Transportation Review, 91, 306-324.
• Hasani, A. and Khosrojerdi, A. (2016). Robust global supply chain network design under disruption and uncertainty considering resilience strategies: A parallel memetic algorithm for a real-life case study, Transportation Research Part E: Logistics and Transportation Review, 87, 20-52.
• Hosseini, S. and Barker, K. (2016). A Bayesian network model for resilience-based supplier selection, International Journal of Production Economics, 180, 68-87.
• Hosseini, S., Morshedlou, N., Ivanov, D., Sarder, M. D., Barker, K. and Al Khaled, A. (2019). Resilient supplier selection and optimal order allocation under disruption risks, International Journal of Production Economics, 213, 124-137
• Ivanov, D., Pavlov, A., Dolgui, A., Pavlov, D. and Sokolov, B. (2016). Disruption-driven supply chain (re)-planning and performance impact assessment with consideration of pro-active and recovery policies, Transportation Research Part E: Logistics and Transportation Review, 90, 7-24.
• Jabbarzadeh, A., Fahimnia, B. and Sabouhi, F. (2018). Resilient and sustainable supply chain design: sustainability analysis under disruption risks, International Journal of Production Research, 56(17), 5945-5968.
• Jabbarzadeh, A., Fahimnia, B., Sheu, J. B. and Moghadam, H. S. (2016). Designing a supply chain resilient to major disruptions and supply/demand interruptions, Transportation Research Part B: Methodological, 94, 121-149.
• Juttner, U. and Maklan, S. (2011). Supply chain resilience in the global financial crisis: an empirical study, Supply Chain Management: An International Journal, 16(4), 246-259.
• Kamalahmadi, M. and Parast, M. M. (2016). A review of the literature on the principles of enterprise and supply chain resilience: Major findings and directions for future research, International Journal of Production Economics, 171, 116-133.
• Khalili, S. M., Jolai, F. and Torabi, S. A. (2017). Integrated production–distribution planning in two-echelon systems: a resilience view, International Journal of Production Research, 55(4), 1040-1064.
• Lee, A. H. (2009). A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks, Expert Systems with Applications, 36(2), 2879-2893.
• Liang, T. F. (2006). Distribution planning decisions using interactive fuzzy multi-objective linear programming, Fuzzy Sets and Systems, 157(10), 1303-1316.
• Mavrotas, G. (2009). Effective implementation of the ε-constraint method in multi-objective mathematical programming problems, Applied Mathematics and Computation, 213(2), 455-465.
• Mohammed, A., Harris, I. and Govindan, K. (2019a). A hybrid MCDM-FMOO approach for sustainable supplier selection and order allocation, International Journal of Production Economics. 217, 171-184.
• Mohammed, A., Harris, I., Soroka, A. and Nujoom, R. (2019b). A hybrid MCDM-fuzzy multi-objective programming approach for a G-Resilient supply chain network design, Computers & Industrial Engineering, 127, 297-312.
• Nooraie, S. V. and Parast, M. M. (2015). A multi-objective approach to supply chain risk management: Integrating visibility with supply and demand risk, International Journal of Production Economics, 161, 192-200.
• Pettit, T. J., Fiksel, J. and Croxton, K. L. (2010). Ensuring supply chain resilience: development of a conceptual framework, Journal of Business Logistics, 31(1), 1-21.
• Pishvaee, M. S. and Razmi, J. (2012). Environmental supply chain network design using multi-objective fuzzy mathematical programming, Applied Mathematical Modelling, 36(8), 3433-3446.
• Ponis, S. T. and Koronis, E. (2012). Supply Chain Resilience? Definition of concept and its formative elements, The Journal of Applied Business Research, 28(5), 921-935.
• Ponomarov, S. Y. and Holcomb, M. C. (2009). Understanding the concept of supply chain resilience, The International Journal of Logistics Management, 20(1), 124-143.
• PrasannaVenkatesan, S. and Goh, M. (2016). Multi-objective supplier selection and order allocation under disruption risk, Transportation Research Part E: Logistics and Transportation Review, 95, 124-142.
• Purvis, L., Spall, S., Naim, M. and Spiegler, V. (2016). Developing a resilient supply chain strategy during ‘boom’and ‘bust’, Production Planning & Control, 27(7-8), 579-590.
• Rajesh, R. and Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach, Journal of Cleaner Production, 86, 343-359.
• Sabouhi, F., Pishvaee, M. S. and Jabalameli, M. S. (2018). Resilient supply chain design under operational and disruption risks considering quantity discount: A case study of pharmaceutical supply chain, Computers & Industrial Engineering, 126, 657-672.
• Sadghiani, N. S., Torabi, S. A. and Sahebjamnia, N. (2015). Retail supply chain network design under operational and disruption risks, Transportation Research Part E: Logistics and Transportation Review, 75, 95-114.
• Scholten, K. and Schilder, S. (2015). The role of collaboration in supply chain resilience, Supply Chain Management: An International Journal, 20(4), 471-484.
• Scholten, K., Sharkey Scott, P. and Fynes, B. (2014). Mitigation processes–antecedents for building supply chain resilience, Supply Chain Management: An International Journal, 19(2), 211-228.
• Shaw, K., Shankar, R., Yadav, S. S. and Thakur, L. S. (2012). Supplier selection using fuzzy AHP and fuzzy multi-objective linear programming for developing low carbon supply chain, Expert Systems with Applications, 39(9), 8182-8192.
• Sheffi, Y. and Rice Jr, J. B. (2005). A supply chain view of the resilient enterprise, MIT Sloan Management Review, 47(1), 41.
• Snyder, L. V., Atan, Z., Peng, P., Rong, Y., Schmitt, A. J. and Sinsoysal, B. (2016). OR/MS models for supply chain disruptions: A review, IIE Transactions, 48(2), 89-109.
• Tang, C. S. (2006). Perspectives in supply chain risk management, International Journal of Production Economics, 103(2), 451-488.
• Tang, C. and Tomlin, B. (2008). The power of flexibility for mitigating supply chain risks, International Journal of Production Economics, 116(1), 12-27.
• Torabi, S. A. and Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning, Fuzzy Sets and Systems, 159(2), 193-214.
• Torabi, S. A., Baghersad, M. and Mansouri, S. A. (2015). Resilient supplier selection and order allocation under operational and disruption risks, Transportation Research Part E: Logistics and Transportation Review, 79, 22-48.
• Vahidi, F., Torabi, S. A. and Ramezankhani, M. J. (2018). Sustainable supplier selection and order allocation under operational and disruption risks, Journal of Cleaner Production, 174, 1351-1365.
• Wieland, A. and Marcus Wallenburg, C. (2012). Dealing with supply chain risks: Linking risk management practices and strategies to performance, International Journal of Physical Distribution & Logistics Management, 42(10), 887-905.
• Yoon, J., Talluri, S., Yildiz, H. and Ho, W. (2018). Models for supplier selection and risk mitigation: a holistic approach, International Journal of Production Research, 56(10), 3636-3661.
• Zahiri, B., Zhuang, J. and Mohammadi, M. (2017). Toward an integrated sustainable-resilient supply chain: A pharmaceutical case study, Transportation Research Part E: Logistics and Transportation Review, 103, 109-142.