In a world, which goes quickly, the company is subjected to the market evolution. Also and to cope with it, the system of production is directed towards families of products and not a single type of product. This aptitude requires a great flexibility as well material as organizational.The problems associated with FMS technology is relatively complexes compared to traditional production systems. This is the reason why the problems scheduling in these systems are NP complete. Therefore, there is no algorithm able to solve these problems exactly.The objective of this work is to solve the problem of scheduling in a flexible production system by the adaptation of the genetic algorithm and the hybrid genetic algorithmusing the simple local search and the annealing simulate -in order to deduce the best Meta heuristic, which provides the best result of makespan. General TermsScheduling, flexible production system, genetic algorithm.

The industries must preserve a rate of constant productivity; however, weaknesses appear at the level of production system which engenders high manufacturing costs. Scheduling is considered the most significant issue in the production system, the solution to that problem need complex methods to solve it. The goal of this paper is to establish three hybridization categories of the evolutionary methods ABC and PSO to solve multi-objective flow shop scheduling problem: Synchronous parallel hybridization using the weighted sum method of the fitness function, sequential hybridization using or not using the weighted sum method of the fitness function, and asynchronous parallel hybridization using the weighted sum method of the fitness function. Then to test these methods in an automotive multi-objective flow shop and to perform an in-depth comparison for verifying how the multi hybridization and the hybridization categories influence the resolution of multiobjective flow shop scheduling problems. The results are consistent with other studies that have shown that the multi hybridization improve the effectiveness of the algorithm.