首页|面向航天元器件检测订单的调度方法

面向航天元器件检测订单的调度方法

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针对航天元器件检测品种多、批量小,检测任务集中、试验流程差异大、数据复用难度大的特点,建立面向航天元器件检测的订单调度框架,对多品类双特性资源进行差异化调度处理.构建以订单平均耗时与检测总成本最小化为目标的多目标优化模型,并提出一种基于局部优化的改进MOEA/D算法.同时考虑了多段式实数编码解码方案,结合以解方案可行程度为基础的局部优化算子与自适应惩罚函数,保证了种群中个体的质量与多样性.最后,以某航天元器件检测单位实际业务为案例进行方法验证,对比了所提算法与改进NSGA-Ⅱ算法、经典MOEA/D算法、NSGA-Ⅲ算法的优化效果,验证了所提方案在解决此类问题上的优越性.
Scheduling method oriented to aerospace components inspection order
Facing the characteristics of aerospace component inspection with many varieties,small batches,central-ized inspection tasks,large differences in test procedures and difficulty in data reuse,a scheduling framework for multi-category dual-feature detection resources was established.A multi-objective optimization model with the mini-mum average time consumption and the minimum total cost as the optimization goals was constructed,and an im-proved Multi-Objective Optimization Evolutionary algorithm based on Decomposition(MOEA/D)algorithm based on local optimization was proposed.The algorithm considered multi-stage real number encoding and decoding rules,and combined with local optimization operators and adaptive penalty functions based on the feasibility of the solution to ensure the quality and diversity of individuals in the population.The improved MOEA/D algorithm was used in actual cases and compared with other algorithms,and the effectiveness and superiority in solving such problems were verified.

component inspectionscheduling optimizationmulti-objective optimizationmulti-objective optimization evolutionary algorithm based on decomposition

冯业为、党炜、康至娟、康晓明

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中国科学院大学,北京 100049

中国科学院空间应用工程与技术中心,北京 100094

国科赛思(北京)科技有限公司,北京 100094

中国科学院大学经济与管理学院,北京 100190

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元器件检测 调度优化 多目标优化 基于分解的多目标进化算法

中国科学院关键技术人才资助项目

2024

计算机集成制造系统
中国兵器工业集团第210研究所

计算机集成制造系统

CSTPCD北大核心
影响因子:1.092
ISSN:1006-5911
年,卷(期):2024.30(1)
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