Job Shop problem solution with an intelligent agent

In this paper, is defines a new and effective methodology based on intelligent agents for production sequencing Job Shop Environments; especially for small and medium enterprises in the metal mechanics sector, where these techniques have not been employed , due to the high resistance to change. This work is developed in two phases. In the first one, the different techniques used are defined. In the second step, statistical tests are executed in order to determine the approximation percentage solutions’ to the optimal or sub optimal solution. This work’s results show that the intelligent agents techniques don’t produce an optimal result every single time; but in few seconds, these techniques, can find sub optimal solutions with an approximation of 97.81% and 90.43%, to the optimal or sub optimal solution, in the variables total time process and total idle time, respectively. This contrasts with the little effectiveness of traditional techniques