Abstract
This paper presents a new class of fuzzy two-stage supply chain problems, in which transportation costs and demands are characterized by fuzzy variables with known possibility distributions. Since fuzzy parameters are often with infinite supports, the conventional optimization algorithms cannot be used to solve the proposed supply chain problem directly. To avoid this difficulty, an approximation method is developed to turn the original supply chain problem into a finite dimensional one. Generally, the approximating supply chain problem is neither convex nor linear. So, to solve the approximating supply chain problem, we design a hybrid algorithm by integrating approximation method, neural network (NN) and particle swarm optimization (PSO). Finally, one numerical example is presented to demonstrate the effectiveness of the designed algorithm.
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Wang, G., Liu, Y., Zheng, M. (2009). Fuzzy Two-Stage Supply Chain Problem and Its Intelligent Algorithm. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5552. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01510-6_3
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DOI: https://doi.org/10.1007/978-3-642-01510-6_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01509-0
Online ISBN: 978-3-642-01510-6
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