考虑能耗约束的并行机组批调度

发布时间：2018-06-19 08:47

本文选题：并行机 + 组批调度　；参考：《中南大学学报(自然科学版)》2017年08期

【摘要】：研究并行批处理机的组批调度问题,考虑炉容相同、功率不同的非等同并行机的总能耗约束,考虑工件尺寸和到达时间不同,以最小化最大完工时间为目标建立混合整数规划模型。并行机组批调度问题属于NP-hard问题,采用先组批后调度的两阶段方式求解。组批阶段采用基于FFLPT和BFLPT的启发式规则,调度阶段设计带邻域搜索的粒子群-遗传混合算法对模型进行求解。以轧辊生产企业并行热处理设备为研究案例进行模型和算法验证,分析不同能耗约束下最大完工时间优化值,并比较算法的优化性能。实验结果表明:本文算法提高标准遗传算法的收敛速度,且优于2种启发式算法;能耗与最大完工时间之间存在冲突关系,通过本文的模型和算法得到能耗与最大完工时间的近似Pareto前沿面,可为企业的实际生产提供指导。
[Abstract]:In this paper, the problem of batch scheduling of parallel batch processors is studied. The total energy consumption constraints of parallel machines with the same furnace capacity and different power are considered, and the size and arrival time of the workpiece are considered. A mixed integer programming model is established to minimize the maximum completion time. The parallel unit batch scheduling problem belongs to NP-hard problem. The heuristic rules based on FFLPT and BFLPT are used in the group batch phase and the particle swarm and genetic hybrid algorithm with neighborhood search is designed in the scheduling phase to solve the model. Taking the parallel heat treatment equipment of roll manufacturing enterprise as a case study, the model and algorithm were verified, and the optimal value of the maximum completion time under different energy consumption constraints was analyzed, and the optimization performance of the algorithm was compared. The experimental results show that the proposed algorithm improves the convergence speed of the standard genetic algorithm and is superior to the two heuristic algorithms, and there is a conflict relationship between the energy consumption and the maximum completion time. The approximate Pareto frontier of energy consumption and maximum completion time can be obtained by the model and algorithm in this paper, which can provide guidance for the actual production of enterprises.
【作者单位】：同济大学电子与信息工程学院;
【基金】：国家自然科学基金资助项目(71690234,61273046)~~
【分类号】：F273;TP18

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