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Hierarchical Scheduling for Multi-Constrained Flexible Job Shop Based on Heuristic and Intelligent Optimization Algorithms

Under the intelligent manufacturing production scheduling environment, in order to give sufficient consideration to the production scheduler's preference and experiential knowledge and to optimize the scheduling results, a hierarchical scheduling algorithm supporting manual decision has been developed to meet various constraints. Based on scheduler's experiential knowledge and preference, constraints and boundary conditions are set artificially to the solving problem, reducing the search space of optimization algorithm. First at the first level, under the combined action of constraint rule and manual intervention meaning in the way of setting constraint with human-machine interaction, a constraint-meeting result can be rapidly constructed based on heuristic algorithm. Then at the second level, a modified segmented encoding based particle swarm genetic algorithm (MSE-PSGA)with priority encoding taking into full account the relation between operation property and machine selection, has been used to optimize the target of minimizing makespan, realizing the hierarchical scheduling with the combination of interactive scheduling and automatic scheduling. Finally, the computational results from heuristic algorithm, PSO, GA and MSE-PSGA are compared. For specific application cases the operating process of hierarchical scheduling has been illustrated, proving its feasibility and effectiveness.