The hybridization of metaheuristics with other techniques for optimization has been one of the most interesting trends in recent years. The focus of research on metaheuristics has also notably shifted from an algorithm-oriented point of view to a problem-oriented one. Many researchers focus on solving a problem at hand as best as possible rather than promoting a certain metaheuristic. This has led researchers to try combining different algorithmic components in order to design algorithms that are more powerful than the ones resulting from the implementation of a pure metaheuristic. In this thesis, a linear programming assisted genetic algorithm is developed for solving a flexible job-shop scheduling problem with lot streaming. The genetic algorithm searches over both discrete and continuous variables in the problem/ solution space. Linear programming model is used to further refine promising solutions in the initial population and during the genetic search process by determining the optimal values of the continuous variables corresponding to the values of the integer variables of these promising solutions. Numerical examples showed that the hybridization of the genetic algorithm with the linear programming greatly improves its convergence behavior