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Ant colony algorithms are computational methods inspired from the behavior of real ant colonies. In this paper our interest is focused on the adaptation of an optimization algorithm called API based on the foraging behavior model of primitive ants’ population called Pachycondyla apicalis to solve real time alternative routings selection problem in a Flexible Manufacturing System (FMS) with and without presence of breakdowns. The FMS consists of seven machining centers, a loading and an unloading stations, and six different part types. Owing to the presence of identical machining centers, the parts have alternative routings. The scheduling decisions has been established in terms of how the parts are routed through various machines in the system.

Objective:Solving optimization problems is a key point in the constant improving of productivity in the industries. Introduction:When traditional methods fail, it is natural to look towards some approximate resolution methods. Memetic algorithm with population management is a Metaheuristic that has been conceived in the last years, and proved its power in the resolution of the difficult optimization problems. Material and Methods:In this paper, our interest is focused on the adaptation of an optimization algorithm called memetic Algorithm with Population Management based on the strategy of management of population to avoid slow or premature convergence and to carry out the excellent executions to solve real time alternative routings selection problem in a Flexible Manufacturing System (FMS), that consists of seven machining centres, a loading and an unloading area, and six different part types which have alternative routings. Results:Simulation results based on two performance indicators which are the production rate, cycle time, work in process and machines utilization rates show that the proposed algorithm performs the best compared to the genetic algorithm. Conclusion:Then we will make a comparison between this algorithm and the genetic algorithm previously used to solve the same problem to get an idea on the most efficient methods for this problem and choose the most effective.