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Applying Nonlinear MODM Model to Supply Chain Management with Quantity Discount Policy under Complex Fuzzy Environment

Journal Name:

  • Journal of Industrial Engineering and Management

Publication Year:

  • 2014
DOI: 
http://dx.doi.org/10.3926/jiem.1079

Key Words:

Author NameUniversity of Author
Abstract (2. Language): 
Purpose: The aim of this paper is to deal with the supply chain management (SCM) with quantity discount policy under the complex fuzzy environment, which is characterized as the bifuzzy variables. By taking into account the strategy and the process of decision making, a bifuzzy nonlinear multiple objective decision making (MODM) model is presented to solve the proposed problem. Design/methodology/approach: The bi-fuzzy variables in the MODM model are transformed into the trapezoidal fuzzy variables by the DMs's degree of optimism α1 and α2, which are de-fuzzified by the expected value index subsequently. For solving the complex nonlinear model, a multi-objective adaptive particle swarm optimization algorithm (MO-APSO) is designed as the solution method. Findings: The proposed model and algorithm are applied to a typical example of SCM problem to illustrate the effectiveness. Based on the sensitivity analysis of the results, the bifuzzy nonlinear MODM SCM model is proved to be sensitive to the possibility level α1. Practical implications: The study focuses on the SCM under complex fuzzy environment in SCM, which has a great practical significance. Therefore, the bi-fuzzy MODM model and MOAPSO can be further applied in SCM problem with quantity discount policy. Originality/value: The bi-fuzzy variable is employed in the nonlinear MODM model of SCM to characterize the hybrid uncertain environment, and this work is original. In addition, the hybrid crisp approach is proposed to transferred to model to an equivalent crisp one by the DMs's degree of optimism and the expected value index. Since the MODM model consider the bi-fuzzy environment and quantity discount policy, so this paper has a great practical significance.
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REFERENCES

References: 

Al-e-hashem, S., Malekly, H., & Aryanezhad, M. (2011). A multi-objective robust optimization
model for multi-product multi-site aggregate production planning in a supply chain under
uncertainty. International Journal of Production Economics, 134, 28-42.
http://dx.doi.org/10.1016/j.ijpe.2011.01.027
Altiparmak, F., Gen, M., Lin, L., & Paksoy, T. (2006). A genetic algorithm approach for multiobjective
optimization of supply chain networks. Computers & Industrial Engineering, 51(1),
196-215. http://dx.doi.org/10.1016/j.cie.2006.07.011
Arikan, F. (2013). A fuzzy solution approach for multi objective supplier selection. Expert
Systems with Applications, 40(3), 947-952. http://dx.doi.org/10.1016/j.eswa.2012.05.051
Chen, C., & Lee, W. (2004). Multi-objective optimization of multi-echelon supply chain
networks with uncertain product demands and prices. Computers & Chemical Engineering,
28(6-7), 1131-1144. http://dx.doi.org/10.1016/j.compchemeng.2003.09.014
Coello, C., & Lechuga, M. (2002). MOPSO: A proposal for multiple objective particle swarm
optimization. In Proceedings of the 2002 Congress on Evolutionary Computation, 1051-1056.
Dubois, D., & Prade, H. (1988). Possibility Theory: An Approach to Computerized Processing of
Uncertainty. New York: Plenum. http://dx.doi.org/10.1007/978-1-4684-5287-7
Garg, H., & Sharma, S. (2013). Multi-objective reliability-redundancy allocation problem using
particle swarm optimization. Computers & Industrial Engineering, 64(1), 247-255.
http://dx.doi.org/10.1016/j.cie.2012.09.015
Giannoccaro, I., Pontrandolfo, P., & Scozzi, B. (2003). A fuzzy echelon approach for inventory
management in supply chains. European Journal of Operational Research, 149(1), 185-196.
http://dx.doi.org/10.1016/S0377-2217(02)00441-1
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE
Conference on Neural Networks, 1942-1948. Piscataway: IEEE Service Center.
Kennedy, J., & Eberhart, R. (2001). Swarm Intelligence. Morgan Kaufmann.
Knowles, J., & Corne, D. (2000). Approximating the nondominated front using the pareto
archived evolution strategy. Evolution Computation, 8, 149-172.
http://dx.doi.org/10.1023/A:1013771608623
Liu, B. (2002). Toward fuzzy optimization without mathematical ambiguity. Fuzzy Optimization
and Decision Making, 1(1), 43-63. http://dx.doi.org/10.1023/A:1013771608623
Liu, Y., & Xu, J. (2006). A class of bifuzzy model and its application to single-period inventory
problem. World Journal of Modelling and Simulation, 2(2), 109-118.
Luhandjula, M. (1987). Multiple objective programming problems with possibility coefficients.
Fuzzy Sets and Systems, 21, 135-145. http://dx.doi.org/10.1016/0165-0114(87)90159-X
Nahmias, S. (1978). Fuzzy variables, Fuzzy Sets and Systems, 1, 97-110.
http://dx.doi.org/10.1016/0165-0114(78)90011-8
Shi, Y., & Eberhart, R. (1998). Particle swarm optimization. In Proc. IEEE Int. Conf. on Neural
Networks, 69-73.
Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2000). Designing and Managing the Supply
Chain. New York: Irwin McGraw-Hill.
Tabrizi, B., & Razmi, J. (2013). Introducing a mixed-integer non-linear fuzzy model for risk
management in designing supply chain networks, Journal of Manufacturing Systems, 32(2),
295-307. http://dx.doi.org/10.1016/j.jmsy.2012.12.001
Tavakkoli-Moghaddam, R., Azarkish, M., & Sadeghnejad-Barkousaraie A. (2011). A new hybrid
multi-objective Pareto archive PSO algorithm for a bi-objective job shop scheduling problem.
Expert Systems with Applications, 38(9), 10812-10821. http://dx.doi.org/10.1016/j.eswa.2011.02.050
Torabi, S., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple
objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214.
http://dx.doi.org/10.1016/j.fss.2007.08.010
Wang, J., & Shu, Y. (2008). Fuzzy decision modeling for supply chain management. Fuzzy Sets
and Systems, 150(1), 107-127. http://dx.doi.org/10.1016/j.fss.2004.07.005
Wei, C., Liang, G., & Wang, M. (2007). A comprehensive supply chain management project
selection framework under fuzzy environment. International Journal of Project Management,
25(6), 627-636. http://dx.doi.org/10.1016/j.ijproman.2007.01.010
Xu, J., & Liu, Y. (2008). Multi-objective decision making model under fuzzy random
environment and its application to inventory problems. Information Sciences, 178,
2899-2914. http://dx.doi.org/10.1016/j.ins.2008.03.003
Xu, J., & Yan, F. (2011). A multi-objective decision making model for the vendor selection
problem in a bifuzzy environment. Expert Systems with Applications, 38(8), 9684-9695.
http://dx.doi.org/10.1016/j.eswa.2011.01.168
Xu, J., & Zhou, X. (2011). Fuzzy-Like Multiple Objective Decision Making. Berlin Heidelberg:
Springer-Verlag.
Yan, L. (2009). Risk Curve and Bifuzzy Portfolio Selection. Journal of Mathematics Research,
1(2), 193-198. http://dx.doi.org/10.5539/jmr.v1n2p193
Zadeh. L. (1965). Fuzzy sets. Information and Control, 8, 338-353.
http://dx.doi.org/10.1016/S0019-9958(65)90241-X
Zadeh. L. (1978). Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets and Systems, 1,
3-28. http://dx.doi.org/10.1016/0165-0114(78)90029-5
Zhang, G., Shao, X., Li, P., & Gao, L. (2009). An effective hybrid particle swarm optimization
algorithm for multi-objective flexible job-shop scheduling problem. Computers & Industrial
Engineering, 56(4), 1309-1318. http://dx.doi.org/10.1016/j.cie.2008.07.021

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