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The coal logistics demand in this paper is refer to the demand of coal transportation, mainly including: the railway, highway and waterway freight volume of coal. In consideration of the small and the nonlinear history sample, this paper combines the support vector regression machine (support vector regression, SVR) and Particle Swarm Optimization algorithm, (Particle Swarm Optimization, PSO) to propose PSO-SVR coal logistics demand forecasting model which is suitable for the learning of small samples. Taking Coal railway freight volume for example, the paper first select influence factors and coal railway freight volumes from 1995 to 2011 as the learning samples to establish the "influence factors -coal railway freight volume" SVR model and then use the particle swarm algorithm to optimize model parameters, Finally, it forecasts the coal railway freight volume. The results show that the prediction accuracy of PSO-SVR model is superior to the BP neural network model.

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Nowadays, goods are no longer necessarily traded at a fixed price, and instead buyers and sellers negotiate among themselves to reach a deal that maximizes the payoffs of both parties. Genetic-agent-based automated negotiation system has become increasingly important since the advent of electronic commerce and the rapid development of agent technology and Genetic Algorithm (GA). In this paper, the performance features and working principle of Agent technology and GA are analyzed and summarized; a genetic-agent-based model that can efficiently carry out bilateral negotiations with multi-attributes and ensure the fairness in a negotiation. The model also considers the efficiency of a negotiation and the extensibility toward multilateral negotiations. Through the application of this model, we can find that the trading program is being optimized with the increase in the number of iterative, the satisfaction is continuously improved and the purpose of a win-win situation finally reaches.

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