Particle Swarm Optimization (PSO) Performance in Solving the Train Location Problem at Transshipment Yard

DOI: 10.4236/oalib.1101024   PDF        902 Downloads   1,272 Views   Citations

Abstract

Particle swarm optimization (PSO) is an evolutionary computation technique; it has shown its effectiveness as an efficient, fast and simple method of optimization. In this paper, the mathematical model represents NP-hard in the strong sense; since any instance of the quadratic assignment problem (QAP), I will implement the particle swarm optimization (PSO) for the quadratic assignment problem (QAP). The results show that the PSO is an appropriate optimization tool for use in determining the train location in the transshipment yard by comparing it with previous studies to know the PSO’s performance.

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Mohamed, A. and Peng, Q. (2014) Particle Swarm Optimization (PSO) Performance in Solving the Train Location Problem at Transshipment Yard. Open Access Library Journal, 1, 1-8. doi: 10.4236/oalib.1101024.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Kellner, M., Boysen, N. and Fliedner, M. (2009) How to Park Freight Trains on Rail-Rail Transshipment Yards. Friedrich-Schiller-Universität Jena, Lehrstuhl für Operations Management, Germany.
[2] Liu, H. and Abraham, A. (2007) A Hybrid Fuzzy Variable Neighborhood Particle Swarm Optimization Algorithm for Solving Quadratic Assignment Problems. Journal of Universal Computer Science, 13, 1032-1054.

  
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