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Determining Decoupling Points in a Supply Chain Networks Using NSGA II Algorithm

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DOI: 
https://doi.org/10.3926/jiem.2158
Abstract (2. Language): 
Purpose: In the model, we used the concepts of Lee and Amaral (2002) and Tang and Chen (2009) and offer a multi-criteria decision-making model that identify the decoupling points to aim to minimize production costs, minimize the product delivery time to customer and maximize their satisfaction. Design/methodology/approach: we encounter with a triple-objective model that metaheuristic method (NSGA II) is used to solve the model and to identify the Pareto optimal points. The max (min) method was used. Findings: Our results of using NSGA II to find Pareto optimal solutions demonstrate good performance of NSGA II to extract Pareto solutions in proposed model that considers determining of decoupling point in a supply network. Originality/value: So far, several approaches to model the future have been proposed, of course, each of them modeled a part of this concept. This concept has been considered more general in the model that defined in follow. In this model, we face with a multi-criteria decision problem that includes minimization of the production costs and product delivery time to customers as well as customer consistency maximization.
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REFERENCES

References: 

Aitken, J., (2000) Agility and Leanness – A Successful and Complimentary Partnership in the Lighting
Industry. Proceedings LRN 2000 Conference. 1-7.
Agarwal, A., Shankar, R., & Tiwari, M.K. (2006). Modeling the metrics of lean, agile and leagile supply
chain: An ANP-based approach. European J. of Operational Research, 173, 211-225.
https://doi.org/10.1016/j.ejor.2004.12.005
Agarwal, A., Shankar, R., & Tiwari, M.K. (2007). Modeling agility of supply chain. Journal of Industrial
Marketing Management, 36, 443-457. https://doi.org/10.1016/j.indmarman.2005.12.004
AL-Fayyaz, A. (2004). Optimization of multi-objective reservoir operation system (Unpublished master's thesis).
National University Of Singapore, Singapore, Singapore. Available online at:
http://www.scholarbank.nus.edu.sg/handle/10635/14339Arjestan, M. (2016). Efficient Optimization of Multi-Objective Redundancy Allocation Problems in
Series-Parallel Systems. Decision Science Letters, 6(3), 307-322. https://doi.org/10.5267/j.dsl.2016.11.004
Cao, M., Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm
performance. Journal of Operations Management, 29(3), 163-180. https://doi.org/10.1016/j.jom.2010.12.008
Christopher, M., (2000). The Agile Supply Chain: Competing in Volatile Markets. Ind. Mark. Man., 29(1),
37-44. https://doi.org/10.1016/S0019-8501(99)00110-8
Christopher, M., & Towill, D. (2001). An Integrated Model for the Design of Agile Supply Chaines.
International Journal of Physical Distribution & Logistics Management, 31(4).
https://doi.org/10.1108/09600030110394914
Deb, K. (2001). Multi-objective Optimization Using Evolutionary Algorithms. Chichester, U.K.: Wiley.
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T.A.M.T. (2002). A fast and elitist multi-objective genetic
algorithm: NSGA-II. Evolutionary Computation, IEEE Transactions, 6(2), 182-197.
https://doi.org/10.1109/4235.996017
DeGroote, S.E., & Marx, T.G. (2013). The impact of IT on supply chain agility and firm performance:
An empirical investigation. International Journal of Information Management, 33, 909-916.
https://doi.org/10.1016/j.ijinfomgt.2013.09.001
Gosling, J., Purvis, L., Naim, M. (2010). Supply chain flexibility as a determinant of supplier selection.
International Journal of Production Economics, 128(1,: 11-21.
Krishnamurthy, R., & Yauch, C.A. (2007). Leagile manufacturing: A proposed corporate infrastructure,
Int. J. Of Operations and Production Management, 27(6), 588-604. https://doi.org/10.1108/01443570710750277
Lee, H.L., & Amaral, J. (2002). Continuous and sustainable improvement through supply chain
performance management. Standford Global Supply Chain Management Forum SGSCMF. 1-14.
Naim, M.M., & Gosling, J. (2011). On leanness, agility and leagile supply chains. International Journal of
Production Economics, 131(1), 342-354. http://doi.org/10.1016/j.ijpe.2010.04.045
Nieuwenhuis, P., & Katsifou, E. (2015). More sustainable automotive production through understanding
decoupling points in leagile manufacturing. Journal of Cleaner Production, 95, 232-241.
https://doi.org/10.1016/j.jclepro.2015.02.084
Olhager, J. (2012). The role of decoupling points in value chain management. In Modelling value. 37-47.
Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2747-7_2Perez, C., Castro, R., Simons, D., & Gimenez, G. (2010) Development of lean supply chains: a case study
of the Catalan pork sector. Supply Chain Management: An International Journal, 15(1), 55-68.
https://doi.org/10.1108/13598541011018120
Srinivas, N., & Deb, K. (1995). Multi-objective function optimization using non-dominated sorting
genetic algorithms. Evolutionary computation, 2(3), 221-248. https://doi.org/10.1162/evco.1994.2.3.221
Sun, X.Y., Ji, P., Sun, L.Y., & Wang, Y.L. (2008). Positioning multiple decoupling points in a supply
network. International Journal of Production Economics, 113(2), 943-956.
https://doi.org/10.1016/j.ijpe.2007.11.012
Tang, D., & Chen, J. (2009). Identification of postponement point in service delivery process: A
description model. Service Systems and Service Management, ICSSSM'09, 6th International Conference on IEEE.
335-339.

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