Autonomous resource allocation of smart workshop for cloud machining orders

IF 1.7 3区工程技术 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing Pub Date : 2020-10-07 DOI:10.1017/S089006042000044X

Jizhuang Hui, Jingyuan Lei, Kai Ding, Fuqiang Zhang, Jingxiang Lv

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引用次数: 2

Abstract

Abstract In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

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面向云加工订单的智能车间资源自主分配

摘要为了实现云制造环境下智能车间协同加工资源的在线分配，提出了车间协同资源多目标优化模型（MOM-WCR）。考虑到加工时间、加工成本、产品合格率和资源利用率的优化目标，构建了MOM-WCR。基于车间加工任务的时序，将车间协同制造资源集成到MOM-WCR中。采用模糊层次分析法将多目标问题简化为单目标问题。然后，将改进的萤火虫算法与粒子群算法（IFA-PSA）相结合，用于求解MOM-WCR。最后，通过一组连杆加工实验对本文提出的模型进行了验证。结果表明，该模型在云制造环境下车间级资源分配的应用中是可行的，改进的萤火虫算法在解决多目标资源分配问题方面表现出良好的性能。

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来源期刊

Ai Edam-Artificial Intelligence for Engineering Design Analysis and Manufacturing 工程技术-工程：制造

CiteScore

4.40

自引率

14.30%

发文量

审稿时长

>12 weeks

期刊介绍： The journal publishes original articles about significant AI theory and applications based on the most up-to-date research in all branches and phases of engineering. Suitable topics include: analysis and evaluation; selection; configuration and design; manufacturing and assembly; and concurrent engineering. Specifically, the journal is interested in the use of AI in planning, design, analysis, simulation, qualitative reasoning, spatial reasoning and graphics, manufacturing, assembly, process planning, scheduling, numerical analysis, optimization, distributed systems, multi-agent applications, cooperation, cognitive modeling, learning and creativity. AI EDAM is also interested in original, major applications of state-of-the-art knowledge-based techniques to important engineering problems.