Multiobjective Advanced Planning and Scheduling Using Iterative Genetic Algorithm

Every flexible manufacturing system demands minimization of production completion time, which reduces the constraints and minimize the production quality with increase in final cost and efficiency. Minimization of production completion time basically deals with two objectives i.e. minimizing machine idle time and minimizing earliness-tardiness penalties. The minimization of these two objectives must be done by considering all constraints i.e. precedence, available machines, machine transition, machine set up, machine capacity, large inventories, etc. This can be achieved through Advanced Planning and Scheduling (APS). A multiobjective genetic algorithm with iterative search is presented to find the optimal solutions for APS problem. This algorithm minimize above two objectives with satisfaction of all constraints. APS mixed integer programming model is developed using Matlab 12 which uses an iterative search technique to improve an efficiency of the system and produces the optimal solution within a short period of time.