Research Article
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Modeling Just-in-Time distribution in a Green Supply Chain

Year 2019, , 93 - 103, 30.04.2019
https://doi.org/10.29249/selcuksbmyd.476247

Abstract

The right-on-time distribution of goods to the end-users plays an
important role in nowadays competitive market. Companies intend to find the
balance between organizational cost and environmental footprint, which is a
challenging practice, as these objectives are usually conflicting. In this
context, from a practical point, managers are willing to find a good compromise
solution to both satisfy economic and environmental goals while they need to
make sure that the products are delivered right-on-time to the demand point,
thereby reducing inventory costs. This study aims to research the
inter-relationship between holding inventory at warehouses and retailers to
satisfy the demand right-on-time, and its impact on costs and carbon emissions.
Three echelon distribution network consisting of manufacturers, warehouses and
retailers is developed and three objectives; i.e., total distribution and
manufacturing cost, total carbon emission associated with storing and handling
of goods at warehouses and retailers, and the sum of backordered goods from
retailers and surpluses of goods at retailers, are considered. The developed
model decides the quantity of products transported from factories to warehouses
and from there, to retailers to satisfy the demand realized at retailers. The
model also decides the sizes of factories and warehouses to be opened. To solve
this multi-objective, green supply chain involving Just-in-time distribution,
we applied fuzzy weighted additive model developed by Tiwari ve diğ. (1987). From a practical point of view, this method
offers a great tool for managers and practitioners as it optimizes multiple
objectives simultaneously. This method allows the managers to adjust the
relative importance ratios for each objective function, which also helps the
managers to truly manage the network performance measures. 

References

  • 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
  • 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
  • Farahani, R. Z., & Elahipanah, M. (2008). A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics, 111(2), 229-243. doi:10.1016/j.ijpe.2006.11.028
  • Ghasimi, S. A., Ramli, R., & Saibani, N. (2014). A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths. Applied Mathematical Modelling, 38(4), 1534-1547. doi:10.1016/j.apm.2013.08.023
  • 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. 2017, 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
  • Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. doi:10.1504/IJSSci.2008.01759
  • 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
  • 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, S., & Sarker, B. R. (2006). Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy. European Journal of Operational Research, 172(1), 179-200. doi:10.1016/j.ejor.2004.10.001
  • Wang, W., Fung, R. Y. K., & Chai, Y. (2004). Approach of just-in-time distribution requirements planning for supply chain management. International Journal of Production Economics, 91(2), 101-107. doi:10.1016/s0925-5273(03)00212-3
  • Zimmer, K. (2002). Supply chain coordination with uncertain just-in-time delivery. International Journal of Production Economics, 77(1), 1-15. doi:https://doi.org/10.1016/S0925-5273(01)00207-9
  • Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.

Yeşil Tedarik Zincirinde Tam Zamanında Dağıtım Modellemesi

Year 2019, , 93 - 103, 30.04.2019
https://doi.org/10.29249/selcuksbmyd.476247

Abstract

Son kullanıcılara malların doğru zamanda dağıtımı günümüz
rekabetçi piyasasında önemli bir rol oynamaktadır. Bu bağlamda, yöneticiler,
ekonomik ve çevresel hedefleri doğrultusunda uygun bir çözüm bulma gayreti
içindeyken aynı zamanda, ürünlerin talep noktalarına tam zamanında teslim
edilmesini ve böylece stok maliyetlerinin düşürülmesini de sağlamalıdırlar. Bu
çalışma, dağıtım noktaları ve perakendecilerdeki stok tutma ile talebi doğru
zamanda karşılamak arasındaki ilişkiyi ve dolayısıyla stok tutma maliyetleri
ile ve karbon emisyonları arasında ilişkiyi araştırmayı hedeflemektedir. Bu
sebeple, fabrika, depo ve perakendecilerden oluşan üç kademeli dağıtım ağı
geliştirilmiş ve üç amaç fonksiyonu; 
toplam dağıtım ve üretim maliyeti, depolarda ve perakendecilerde
ürünlerin depolanması ve elleçlenmesiyle ilişkili toplam karbon emisyonu ve
perakendecilerden ardısmarlanmış (karşılanamamış, sonraya ertelenen, ingilizce:
backordered) ürünlerin ve talep fazlası ürünlerin sayısı olarak belirlenmiştir.
Geliştirilen model, hangi üreticiden, depolara ve oradan perakendecilere,
perakendecilerin talebine cevap vermek için ne kadar miktarlarda taşınacağını,
hangi fabrika ve depoların hangi boyutlarda açılacağını, depolardaki envanter
miktarlarını da belirlemektedir. Bu çok amaçlı tam zamanında dağıtım
modellemesi içeren yeşil tedarik zinciri modelinin çözümü için Tiwari, Dharmar ve Rao (1987) tarafından geliştirilen
bulanık ağırlıklandırma yaklaşımı ilk defa kulanılmıştır. Pratik açıdan,
çelişen ve farklı birimlere sahip amaçların aynı anda optimize edilmesine
olanak verdiği için bu yöntem, yöneticiler ve karar vericiler açısından önem
taşımaktadır. Bu bulanık edinim yöntemi, yöneticilerin her bir hedef işlev için
göreli önemlerini belirlermesine imkan sağlaması da ayrıca önemlidir, çünkü bu
sayede yöneticiler, hedeflerin de birbirlerine göre önem derecelerini kendi
tedarik zincirlerine göre belirleyebilirler.

References

  • 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
  • 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
  • Farahani, R. Z., & Elahipanah, M. (2008). A genetic algorithm to optimize the total cost and service level for just-in-time distribution in a supply chain. International Journal of Production Economics, 111(2), 229-243. doi:10.1016/j.ijpe.2006.11.028
  • Ghasimi, S. A., Ramli, R., & Saibani, N. (2014). A genetic algorithm for optimizing defective goods supply chain costs using JIT logistics and each-cycle lengths. Applied Mathematical Modelling, 38(4), 1534-1547. doi:10.1016/j.apm.2013.08.023
  • 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. 2017, 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
  • Saaty, T. L. (2008). Decision making with the analytic hierarchy process. International Journal of Services Sciences, 1(1), 83-98. doi:10.1504/IJSSci.2008.01759
  • 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
  • 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, S., & Sarker, B. R. (2006). Optimal models for a multi-stage supply chain system controlled by kanban under just-in-time philosophy. European Journal of Operational Research, 172(1), 179-200. doi:10.1016/j.ejor.2004.10.001
  • Wang, W., Fung, R. Y. K., & Chai, Y. (2004). Approach of just-in-time distribution requirements planning for supply chain management. International Journal of Production Economics, 91(2), 101-107. doi:10.1016/s0925-5273(03)00212-3
  • Zimmer, K. (2002). Supply chain coordination with uncertain just-in-time delivery. International Journal of Production Economics, 77(1), 1-15. doi:https://doi.org/10.1016/S0925-5273(01)00207-9
  • Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55.
There are 21 citations in total.

Details

Primary Language Turkish
Journal Section Original Research Articles
Authors

Batuhan Eren Engin 0000-0002-3898-0855

Turan Paksoy

Publication Date April 30, 2019
Submission Date October 30, 2018
Published in Issue Year 2019

Cite

APA Engin, B. E., & Paksoy, T. (2019). Yeşil Tedarik Zincirinde Tam Zamanında Dağıtım Modellemesi. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, 22(1), 93-103. https://doi.org/10.29249/selcuksbmyd.476247

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