Under restructuring of electric power industry and changing traditional vertically integrated electric utility structure to competitive, market clearing price (MCP) prediction models are essential for all generation company (GenCos). In this paper, a hybrid model is presented to predict hourly

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The increasing value of facilities, on the one hand, and the complexity of the equipment used in them, on the other, have increased the importance of planning for the maintenance of facilities, especially for companies which their facilities are located in different locations. In this paper, a new hybrid model has been presented to optimize facility maintenance scheduling by a combination of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Monte Carlo Simulation for organizing facilities which are in different locations as well as determining the optimum number of crews with three different skills of mechanical, electrical and simple workers. The main contributions of this paper include: (a) optimizing the number of crew by different skills in the first stage. (b) evaluation of fitness value for each solution through the Monte Carlo Simulation Model. (c) scheduling by consideration different failure rates for different facilities in different locations. In order to evaluate the performance of the proposed model, the model has been compared with Golpira's model, the results of which have shown that it is possible to reduce the cost by just over 39% and reduce MTBF by over half.