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不确定条件下基于区间灰数的柔性车间调度

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为了获得柔性车间在不确定条件下的最优调度方案,提出了基于区间灰数和操作顺序自适应遗传算法的车间调度方法。考虑了加工时间模糊、机床维护等不确定条件,建立了以加工时间区间灰数最小为目标的优化模型。在求解算法上,根据染色体聚集度自适应调整遗传操作顺序,保持了算法在不同情况下的进化能力,从而提出了基于操作顺序自适应遗传算法的调度方法。以某车间的生产调度案例为例,经仿真验证,与遗传算法、精英保留遗传算法和候鸟算法等相比,操作顺序自适应遗传算法的完工时间区间灰数最小,为[74,84]min;且调度方案满足生产顺序约束和时间约束,是可行的调度方案。实验结果表明,操作顺序自适应遗传算法在车间调度中是有效可行的。
Flexible workshop scheduling based on interval grey number under uncertain conditions
In order to obtain the optimal scheduling scheme for flexible workshops under uncertain conditions,a workshop schedu-ling method based on interval grey number and operation order adaptive genetic algorithm was proposed.Taking into account un-certain conditions such as fuzzy processing time and machine maintenance,an optimization model was established with the goal of minimizing the grey number in the processing time interval.In terms of solving algorithms,the genetic operation order was adap-tively adjusted based on chromosome aggregation,maintaining the evolutionary ability of the algorithm in different situations,thus proposing a scheduling method based on the operation order adaptive genetic algorithm.Taking the production scheduling case of a certain workshop as an example,simulation verification shows that compared with genetic algorithms,elite retained genetic algo-rithms,migratory bird algorithms,etc.,the operation sequence adaptive genetic algorithm has the smallest grey number in the com-pletion time interval,which is[74,84]min;and the scheduling plan meets the constraints of production sequence and time,making it a feasible scheduling plan.The experimental results show that the operation sequence adaptive genetic algorithm is ef-fective and feasible in workshop scheduling.

uncertain conditionsflexible workshop schedulinginterval grey numbergenetic algorithmadaptive operation se-quence

刘智飞、李国林

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中国石油大学(华东)石油工业训练中心,青岛 266580

中国石油大学(华东)控制科学与工程学院,青岛 266580

不确定条件 柔性车间调度 区间灰数 遗传算法 操作顺序自适应

山东省技术创新引导计划项目

ZX20210500001

2024

现代制造工程
北京机械工程学会 北京市机械工业局技术开发研究所

现代制造工程

CSTPCD北大核心
影响因子:0.374
ISSN:1671-3133
年,卷(期):2024.(1)
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