Dynamic integrated process planning, scheduling and due date assignment

Integrating process planning, scheduling and due date assignment functions is a matter of current and many studies in the literature. The study on the integration of these three functions is aimed at creating more efficient production plans and schedules in manufacturing environments. However, in the real-world production processes unpredictable changes, disturbances and machine break downs may occur. These setbacks are sometimes a machine break down, sometimes including a new order as soon as the order is canceled may be glitch. The process plans that are best in the factories must be continuously monitored and updated in the face of difficulties that may occur. Because unpredictable process failures may disrupt best process plans and the resulting process plans, and schedules may be less efficient or may not be in the solutions area. For these reasons, developing a new model would be an important work to create process plans that can take precautions and adapt to dynamic changes and complexity. The model is complex, and the solution of the problem is difficult because of the number and structure of the difficulties that occur dynamically. Therefore, there is no study in this field in the literature. In this thesis study, the integration problem has been studied deciding the process planning, scheduling and due date assignment which has been taken into consideration dynamically. Genetic algorithms, simulated annealing algorithm, tabu search algorithm and particle swarm optimization algorithm with the combination of hybrid meta-heuristic algorithms will be used effectively in the solution of the problem.