DIGITAL TWIN-ORIENTED COLLABORATIVE OPTIMIZATION OF PROCESS PLANNING AND SCHEDULING IN A FLEXIBLE JOB SHOP

Process planning and scheduling are two crucial components in flexible manufacturing systems. To address the challenge of information interaction and sharing during the process planning and scheduling stage of parts, a digital twin-oriented approach is proposed. The objective is to optimize the maximum makespan while accommodating fluctuations in the job shop site. Firstly, in the process planning stage, an enhanced genetic algorithm is employed to generate multiple near-optimal process routes. These routes are coded using a four-level coding system, enhancing the efficiency of the planning process. Then, in the production scheduling stage, a hybrid particle swarm optimization algorithm is constructed, considering the characteristics of multi-process routes and the status of shop production resources and production systems. To improve local search ability, various neighborhood structures are utilized. Finally, the proposed method is evaluated through production example simulations and compared with genetic algorithm and particle swarm optimization. The results demonstrate that this method has a quicker convergence rate, shorter execution time, and higher computation precision, which is not only remarkable but also practical for addressing the collaborative optimization of process planning and scheduling in discrete manufacturing systems.