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A Bi-objective Closed Loop Supply Chain with Different Machinery Options: Application of Fuzzy Weighted Additive Approach

Year 2019, Volume: 7 Issue: 2, 309 - 320, 01.06.2019
https://doi.org/10.15317/Scitech.2019.201

Abstract

Supply chain management is
an emerging area that drawing increasing attention of academics and
practitioners for decades. In recent years, SCM's focal point has begun to
emerge as a sustainable flow management, in which economic, social and
environmental aspects such as energy consumption, carbon emissions are jointly
addressed. This study focused on formulating and solving a bi-objective
multi-period closed loop supply chain network design problem. The model
determines the production and distribution strategies, while minimizing two
objectives simultaneously; the total supply chain cost and the carbon emissions
generated by plants operating through different machinery types. While the
initial purchase cost of older and more outdated machinery is lower than newer ones,
older machinery emits greater amount of carbon per hour as opposed to newer
machinery while operating at even greater cost per hour. Besides, the number of
products produced in an hour is also higher in newer machinery. We adopted a
fuzzy weighted additive approach to solve the bi-objective optimization model.
The results confirm that investing in newer technologies in manufacturing comes
with great result for both economic and environmental causes, reducing the unit
cost and carbon emission per product throughout the manufacturing periods.

References

  • Altiparmak, F., Gen, M., Lin, L., Paksoy, T., 2006, "A genetic algorithm approach for multi-objective optimization of supply chain networks", Computers & Industrial Engineering, 51(1), 196-215. doi:https://doi.org/10.1016/j.cie.2006.07.011
  • Amid, A., Ghodsypour, S. H., O’Brien, C., 2009, "A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply Chain", International Journal of Production Economics, 121(2), 323-332. doi:http://dx.doi.org/10.1016/j.ijpe.2007.02.040
  • Arikan, F., 2013, "A fuzzy solution approach for multi objective supplier selection", Expert Systems with Applications, 40(3), 947-952. doi:10.1016/j.eswa.2012.05.051
  • Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., van der Vorst, J. G. A. J., 2017, "Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain", International Journal of Production Economics, 183, 409-420. doi:https://doi.org/10.1016/j.ijpe.2016.08.012
  • Chan, F. T. S., Jha, A., Tiwari, M. K., 2016, "Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-II with heuristics algorithm", Applied Soft Computing, 38, 978-987. doi:10.1016/j.asoc.2015.10.067
  • de Groot, H. L. F., Verhoef, E. T., Nijkamp, P., 2001, "Energy saving by firms: decision-making, barriers and policies", Energy Economics, 23(6), 717-740. doi:https://doi.org/10.1016/S0140-9883(01)00083-4
  • Fahimnia, B., Sarkis, J., Eshragh, A., 2015, "A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis", Omega, 54, 173-190. doi:10.1016/j.omega.2015.01.014
  • Fernando, Y., Wah, W. X., 2017, "The impact of eco-innovation drivers on environmental performance: Empirical results from the green technology sector in Malaysia", Sustainable Production and Consumption, 12, 27-43. doi:https://doi.org/10.1016/j.spc.2017.05.002
  • Iida, T., 2012, "Coordination of cooperative cost-reduction efforts in a supply chain partnership", European Journal of Operational Research, 222(2), 180-190. doi:https://doi.org/10.1016/j.ejor.2012.03.029
  • Jaffe, A. B., Newell, R. G., Stavins, R. N., 2005, "A tale of two market failures: Technology and environmental policy", Ecological Economics, 54(2), 164-174. doi:https://doi.org/10.1016/j.ecolecon.2004.12.027
  • Kavitha, C. a. V., C., 2013, "Multi Objective Fuzzy Linear Programming Technique for Weighted Additive Model for Supplier Selection in Supply Chain Management", International Journal of Applied Mathematics and Informatics.
  • Mehlawat, M. K., Kumar, S., 2017, "A multiobjective optimization model for optimal supplier selection in multiple sourcing environment", 26, 18.
  • Pan, W., Wang, F., Guo, Y., Liu, S., 2015, "A Fuzzy Multiobjective Model for Supplier Selection under Considering Stochastic Demand in a Supply Chain", Mathematical Problems in Engineering, 2015, 8. doi:10.1155/2015/174585
  • Sadeghi Rad, R., Nahavandi, N., 2018, "A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount", Journal of Cleaner Production, 196, 1549-1565. doi:https://doi.org/10.1016/j.jclepro.2018.06.034
  • Seifbarghy, M., Pourebrahim Gilkalayeh, A., Alidoost, M., 2011, "A Comprehensive Fuzzy Multiobjective Supplier Selection Model under Price Brakes and Using Interval Comparison Matrices", Journal of Industrial and Systems Engineering, 4(4), 224-244.
  • Shaw, K., Shankar, R., Yadav, S. S., 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.
  • Soleimani, H., Govindan, K., Saghafi, H., Jafari, H., 2017, "Fuzzy multi-objective sustainable and green closed-loop supply chain network design", Computers & Industrial Engineering, 109, 191-203. doi:https://doi.org/10.1016/j.cie.2017.04.038
  • Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., Gholamnejad, S., 2016, "A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry", Journal of Cleaner Production, 113, 662-673. doi:10.1016/j.jclepro.2015.10.074
  • Teng, M.-J., Wu, S.-Y., Chou, S. J.-H., 2014, "Environmental Commitment and Economic Performance – Short-Term Pain for Long-Term Gain", Environmental Policy and Governance, 24(1), 16-27. doi:doi:10.1002/eet.1634
  • Tiwari, R. N., Dharmar, S., Rao, J. R., 1987, " Fuzzy goal programming — An additive model", Fuzzy Sets and Systems, 24(1), 27-34. doi:https://doi.org/10.1016/0165-0114(87)90111-4
  • Wang, F., Lai, X., Shi, N., 2011, "A multi-objective optimization for green supply chain network design", Decision Support Systems, 51(2), 262-269. doi:https://doi.org/10.1016/j.dss.2010.11.020
  • Zimmermann, H.-J., 1978, "Fuzzy programming and linear programming with several objective functions", Fuzzy Sets and Systems, 1(1), 45-55.

İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI

Year 2019, Volume: 7 Issue: 2, 309 - 320, 01.06.2019
https://doi.org/10.15317/Scitech.2019.201

Abstract

Tedarik zinciri yönetimi,
küreselleşme çağının başlangıcından beri akademisyenlerin ve uygulayıcıların
artan ilgisini çekmeye devam etmiştir. Son yıllarda, tedarik zinciri
yönetiminin odak noktası, enerji tüketimi, karbon emisyonları gibi ekonomik,
sosyal ve çevresel yönlerin ortaklaşa ele alındığı sürdürülebilir akış yönetimi
olmaya başlamıştır. Bu çalışmada, çok dönemli kapalı döngü tedarik zinciri ağ
tasarım probleminin optimizasyonu için iki amaçlı karmaşık tamsayılı doğrusal
programlama modelinin formüle edilmesi ve çözülmesi gerçekleştirilmiştir. Model,
farklı makine tiplerinde faaliyet gösteren tesislerin toplam operasyon maliyeti
ve toplam karbon emisyonları olmak üzere iki ayrı amacın minimizasyonunu
hedeflerken, üretim ve dağıtım stratejilerini belirlemekte ve yeni veya eski
tip makinelerin kullanımına da karar vermektedir. Daha eski ve güncel olmayan
makinelerin ilk satın alma maliyeti, yeni ve güncellenmiş makinelere göre daha
düşük olmasına rağmen, eski makineler, saat başına daha yüksek maliyetle
çalışırken yeni makinelere göre saat başına daha fazla karbon salmaktadır.
Ayrıca, bir saat içinde üretilen ürünlerin sayısı, yani üretkenlik, yeni
makinelerde daha üstündür. Bu iki amaçlı kapalı döngü tedarik zinciri modelinin
çözümü için bulanık ağırlıklandırma yaklaşımı kullanılmıştır. Sonuçlar, üretimde
yeni nesil teknolojilere yatırım yapılmasının hem ekonomik hem de çevresel amaçlara
ulaşmak için önemli olduğunu göstermektedir.

References

  • Altiparmak, F., Gen, M., Lin, L., Paksoy, T., 2006, "A genetic algorithm approach for multi-objective optimization of supply chain networks", Computers & Industrial Engineering, 51(1), 196-215. doi:https://doi.org/10.1016/j.cie.2006.07.011
  • Amid, A., Ghodsypour, S. H., O’Brien, C., 2009, "A weighted additive fuzzy multiobjective model for the supplier selection problem under price breaks in a supply Chain", International Journal of Production Economics, 121(2), 323-332. doi:http://dx.doi.org/10.1016/j.ijpe.2007.02.040
  • Arikan, F., 2013, "A fuzzy solution approach for multi objective supplier selection", Expert Systems with Applications, 40(3), 947-952. doi:10.1016/j.eswa.2012.05.051
  • Banasik, A., Kanellopoulos, A., Claassen, G. D. H., Bloemhof-Ruwaard, J. M., van der Vorst, J. G. A. J., 2017, "Closing loops in agricultural supply chains using multi-objective optimization: A case study of an industrial mushroom supply chain", International Journal of Production Economics, 183, 409-420. doi:https://doi.org/10.1016/j.ijpe.2016.08.012
  • Chan, F. T. S., Jha, A., Tiwari, M. K., 2016, "Bi-objective optimization of three echelon supply chain involving truck selection and loading using NSGA-II with heuristics algorithm", Applied Soft Computing, 38, 978-987. doi:10.1016/j.asoc.2015.10.067
  • de Groot, H. L. F., Verhoef, E. T., Nijkamp, P., 2001, "Energy saving by firms: decision-making, barriers and policies", Energy Economics, 23(6), 717-740. doi:https://doi.org/10.1016/S0140-9883(01)00083-4
  • Fahimnia, B., Sarkis, J., Eshragh, A., 2015, "A tradeoff model for green supply chain planning:A leanness-versus-greenness analysis", Omega, 54, 173-190. doi:10.1016/j.omega.2015.01.014
  • Fernando, Y., Wah, W. X., 2017, "The impact of eco-innovation drivers on environmental performance: Empirical results from the green technology sector in Malaysia", Sustainable Production and Consumption, 12, 27-43. doi:https://doi.org/10.1016/j.spc.2017.05.002
  • Iida, T., 2012, "Coordination of cooperative cost-reduction efforts in a supply chain partnership", European Journal of Operational Research, 222(2), 180-190. doi:https://doi.org/10.1016/j.ejor.2012.03.029
  • Jaffe, A. B., Newell, R. G., Stavins, R. N., 2005, "A tale of two market failures: Technology and environmental policy", Ecological Economics, 54(2), 164-174. doi:https://doi.org/10.1016/j.ecolecon.2004.12.027
  • Kavitha, C. a. V., C., 2013, "Multi Objective Fuzzy Linear Programming Technique for Weighted Additive Model for Supplier Selection in Supply Chain Management", International Journal of Applied Mathematics and Informatics.
  • Mehlawat, M. K., Kumar, S., 2017, "A multiobjective optimization model for optimal supplier selection in multiple sourcing environment", 26, 18.
  • Pan, W., Wang, F., Guo, Y., Liu, S., 2015, "A Fuzzy Multiobjective Model for Supplier Selection under Considering Stochastic Demand in a Supply Chain", Mathematical Problems in Engineering, 2015, 8. doi:10.1155/2015/174585
  • Sadeghi Rad, R., Nahavandi, N., 2018, "A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount", Journal of Cleaner Production, 196, 1549-1565. doi:https://doi.org/10.1016/j.jclepro.2018.06.034
  • Seifbarghy, M., Pourebrahim Gilkalayeh, A., Alidoost, M., 2011, "A Comprehensive Fuzzy Multiobjective Supplier Selection Model under Price Brakes and Using Interval Comparison Matrices", Journal of Industrial and Systems Engineering, 4(4), 224-244.
  • Shaw, K., Shankar, R., Yadav, S. S., 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.
  • Soleimani, H., Govindan, K., Saghafi, H., Jafari, H., 2017, "Fuzzy multi-objective sustainable and green closed-loop supply chain network design", Computers & Industrial Engineering, 109, 191-203. doi:https://doi.org/10.1016/j.cie.2017.04.038
  • Talaei, M., Farhang Moghaddam, B., Pishvaee, M. S., Bozorgi-Amiri, A., Gholamnejad, S., 2016, "A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry", Journal of Cleaner Production, 113, 662-673. doi:10.1016/j.jclepro.2015.10.074
  • Teng, M.-J., Wu, S.-Y., Chou, S. J.-H., 2014, "Environmental Commitment and Economic Performance – Short-Term Pain for Long-Term Gain", Environmental Policy and Governance, 24(1), 16-27. doi:doi:10.1002/eet.1634
  • Tiwari, R. N., Dharmar, S., Rao, J. R., 1987, " Fuzzy goal programming — An additive model", Fuzzy Sets and Systems, 24(1), 27-34. doi:https://doi.org/10.1016/0165-0114(87)90111-4
  • Wang, F., Lai, X., Shi, N., 2011, "A multi-objective optimization for green supply chain network design", Decision Support Systems, 51(2), 262-269. doi:https://doi.org/10.1016/j.dss.2010.11.020
  • Zimmermann, H.-J., 1978, "Fuzzy programming and linear programming with several objective functions", Fuzzy Sets and Systems, 1(1), 45-55.
There are 22 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Articles
Authors

Batuhan Eren Engin

Turan Paksoy

Publication Date June 1, 2019
Published in Issue Year 2019 Volume: 7 Issue: 2

Cite

APA Engin, B. E., & Paksoy, T. (2019). İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, 7(2), 309-320. https://doi.org/10.15317/Scitech.2019.201
AMA Engin BE, Paksoy T. İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI. sujest. June 2019;7(2):309-320. doi:10.15317/Scitech.2019.201
Chicago Engin, Batuhan Eren, and Turan Paksoy. “İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7, no. 2 (June 2019): 309-20. https://doi.org/10.15317/Scitech.2019.201.
EndNote Engin BE, Paksoy T (June 1, 2019) İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7 2 309–320.
IEEE B. E. Engin and T. Paksoy, “İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI”, sujest, vol. 7, no. 2, pp. 309–320, 2019, doi: 10.15317/Scitech.2019.201.
ISNAD Engin, Batuhan Eren - Paksoy, Turan. “İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi 7/2 (June 2019), 309-320. https://doi.org/10.15317/Scitech.2019.201.
JAMA Engin BE, Paksoy T. İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI. sujest. 2019;7:309–320.
MLA Engin, Batuhan Eren and Turan Paksoy. “İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI”. Selçuk Üniversitesi Mühendislik, Bilim Ve Teknoloji Dergisi, vol. 7, no. 2, 2019, pp. 309-20, doi:10.15317/Scitech.2019.201.
Vancouver Engin BE, Paksoy T. İKİ AMAÇLI FARKLI MAKİNE SEÇENEKLİ KAPALI DÖNGÜ TEDARİK ZİNCİRİ OPTİMİZASYONU: BULANIK ÇÖZÜM TEKNİĞİ UYGULAMASI. sujest. 2019;7(2):309-20.

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