A metaheuristic is an iterative process that guides and updates the operations of subordinate heuristics to efficiently produce better quality solutions. It is used in cases where exact methods are not sufficient to provide a solution so that the method of manipulation of a single solution or a collection of solutions at each iteration is deployed. This paper addresses the application of generic algorithm for a parallel machine flow line scheduling problem using the algorithm proposed for minimizing the makespan. Makespan is an important requirement to achieve effective production from process planning. As the problem chosen is NP-hard, genetic algorithm is adopted as it is one of the proven methods to search for a feasible optimal solution to the chosen objective function. The methodology is based on creating a group of random solutions for the randomly generated samples and applying the operators of cross over and mutation to improve the solutions till an acceptable fitness level is reached. The computational experiments deployed indicate that the proposed methodology and procedures are helping to arriving at better solutions faster.
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