极小化等待时间的热处理批调度模型与PSO解

机加车间的工件动态到达热处理车间后因受到批处理设备合批等的约束不能及时得到加工,基于工件动态到达的热处理车间,以最小化工件等待时间期望为目标,建立批调度模型,根据工件到达时间实现了粒子群算法微粒的编码以及对工件的分批,通过仿真实验得到结论:缩短工件的加工时间,则在热处理车间内,可以减小工件等待时间期望;降低工件数规模,工件会密集到达热处理环节,从而减短工件等待时间;工件的等待时间期望的大小与工件规模数量有关,工件数规模较小时,大尺寸工件的等待时间期望优于小尺寸工件,规模较大时,则相反。最后,对比分析了本文改进的粒子群算法的效果,发现改进的粒子群算法最优。

Batch scheduling model of heat treatment with minimization waiting time and the solution with PSO

After the workpiece of adding workshop reaches the heat treatment workshop dynamically, it cannot be processed immediately because of the constraint of batch processing equipment. In the paper, the heat-treatment workshop scheduling under workpieces with dyna-mic arrival time was considered, the batch scheduling model with expected waiting time of workpieces for the scheduling goal was built and solved by making use of particle swarm optimizationl algorithm. In the process of solving, particle swarm algorithm of particle coding and batch of workpieces was came true according to the arrivals time of workpiece, the conclusion was achieved by simulation experiment:the shorter the processing time of workpieces, the less the expe-cted waiting time of workpieces;under the circumstance of tending to be a small quantity of workpieces, waiting time expectation for large size workpieces is superior to smallsize workpieces, under the circumstance of tending to be a large quantity of workpieces, waiting time expectation for small size workpieces is superior to large size workpieces. Finally, we make a comparison with ant colony algorithm, algorithm with FCFS and standard particle swarm algorithm. The simulation results show that the results of the particle swarm algorithm is the optimal.