Policy-Combination Oriented Optimization for Supply Chain Simulation

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Supply chain simulation is able to capture the dynamics and uncertainties in all kinds of supply chains and provide quantitative performance evaluations. In order to address the time consuming evaluation and large search space issues in supply chain simulation, this paper proposes a policy-combination oriented optimization approach to conduct decision makings. The approach begins with reducing the search space by relaxing the goal of optimization, and then refers to meta-heuristic searching methods to solve the main bi-level optimization problem. Lastly the key parameters are fine-tuned with what-if analysis. A case study demonstrates the effectiveness and efficiency of the proposed approach, and compares it with other alternative approaches available in practice.

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646-651

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February 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] R.G. Ingalls, 1998. The Value of Simulation in Modeling Supply Chains. In: Proceedings of the 1998 Winter Simulation Conference. pp.1371-1375.

Google Scholar

[2] M. Jahangirian, T. Eldabi, A. Naseer, L.K. Stergioulas, and T. Young, 2010. Simulation in Manufacturing and Business: AReview. European Journal ofOperational Research 203 (1), pp.1-13.

DOI: 10.1016/j.ejor.2009.06.004

Google Scholar

[3] W. Tan, Y. Chai, W. Wang, Y. Liu, 2012. General Modeling and Simulation for Enterprise Operational Decision-making Problem: A Policy-combination Perspective. Simulation Modelling Practice and Theory21 (1), pp.1-20.

DOI: 10.1016/j.simpat.2011.09.008

Google Scholar

[4] Y. Ho, Q. Zhao, Q. Jia, 2007. Ordinal Optimization: Soft Optimization for Hard Problems. Springer.

Google Scholar

[5] R. Eberhart, J. Kennedy, 1995. A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micro Machine and Human Science. pp.39-43.

DOI: 10.1109/mhs.1995.494215

Google Scholar

[6] S. Kirkpatrick, C.D. Gelatt, M.P. Vecchi, 1983. Optimization by Simulated Annealing. Science 220 (4598), pp.671-680.

DOI: 10.1126/science.220.4598.671

Google Scholar

[7] Y. Shi, R. Eberhart, 1998. A Modified Particle Swarm Optimizer. In: The1998 IEEE International Conference on Evolutionary Computation Proceedings. pp.69-73.

DOI: 10.1109/icec.1998.699146

Google Scholar