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Science and Technology of Nuclear Installations
Volume 2014, Article ID 286826, 9 pages
http://dx.doi.org/10.1155/2014/286826
Research Article

PSO Based Optimization of Testing and Maintenance Cost in NPPs

1Software Development Center, State Nuclear Power Technology Corporation, Beijing 102209, China
2School of Nuclear Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Received 11 July 2014; Revised 13 November 2014; Accepted 13 November 2014; Published 9 December 2014

Academic Editor: Alejandro Clausse

Copyright © 2014 Qiang Chou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Testing and maintenance activities of safety equipment have drawn much attention in Nuclear Power Plant (NPP) to risk and cost control. The testing and maintenance activities are often implemented in compliance with the technical specification and maintenance requirements. Technical specification and maintenance-related parameters, that is, allowed outage time (AOT), maintenance period and duration, and so forth, in NPP are associated with controlling risk level and operating cost which need to be minimized. The above problems can be formulated by a constrained multiobjective optimization model, which is widely used in many other engineering problems. Particle swarm optimizations (PSOs) have proved their capability to solve these kinds of problems. In this paper, we adopt PSO as an optimizer to optimize the multiobjective optimization problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. Numerical results have demonstrated the efficiency of our proposed algorithm.