目的 针对涂布机烘箱生产装配效率低、跨域协同能力不足的问题,本文提出基于改进麻雀搜索算法的涂布烘箱云制造服务组合优化方法,旨在提升烘箱制造生产效率与产业协同水平。方法 首先系统梳理烘箱零件结构与零件制造子任务分解体系,其次构建融合服务质量与综合能耗的双目标烘箱制造服务组合优化数学模型,最后提出基于多种改进策略的麻雀搜索算法,实现模型的高效求解,获取最优制造服务组合。结果 在包含10个子任务的烘箱云制造服务组合优化实例中,所提方法得到的最优组合服务质量较次优算法平均提升了8.47%;基准函数性能测试显示,其优化效果较次优算法提升了约19.25%。结论 基准函数与不同规模的真实案例的实验结果验证了本文模型与方法在解决烘箱云制造服务组合优化问题上的可行性与优越性,能有效提升烘箱制造的生产效率与产业协同能力。
Abstract
The work aims to present an optimized cloud manufacturing service composition approach for coater ovens based on an improved sparrow search algorithm, to address the low production and assembly efficiency of coater ovens and insufficient cross-domain collaboration capabilities and improve the production efficiency and industrial collaboration capabilities in oven manufacturing. The research systematically sorted out the structure of oven parts and the decomposition system of parts manufacturing sub-tasks, and established a two-objective optimization model integrating service quality and comprehensive energy consumption. An enhanced sparrow search algorithm incorporating multiple improvement strategies was then proposed to achieve efficient model solving and obtain the optimal manufacturing service composition. Experimental results demonstrated that in a cloud manufacturing service composition optimization case involving 10 subtasks, the optimal solution achieved an average service quality improvement of 8.47% compared with suboptimal algorithms. Benchmark function performance tests showed an approximately 19.25% improvement in optimization effectiveness. These results validate the feasibility and superiority of the proposed model and method in solving coater oven cloud manufacturing service composition optimization problems, effectively enhancing production efficiency and industrial collaboration capabilities in oven manufacturing.
关键词
涂布机 /
烘箱 /
云制造 /
组合优化 /
麻雀搜索算法
Key words
coater /
oven /
cloud manufacturing /
composition optimization /
sparrow search algorithm(SSA)
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基金
国家重点研发计划项目（2023YFB3308800）; 渭南市重点研发计划项目（2024ZDYFJH-767）
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