Association rules mining between service demands and remanufacturing services

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 : 2020-10-26 DOI:10.1017/S0890060420000396

Wenbin Zhou, Xuhui Xia, Zelin Zhang, Lei Wang

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引用次数: 5

Abstract

Abstract The potential relationship between service demands and remanufacturing services (RMS) is essential to make the decision of a RMS plan accurately and improve the efficiency and benefit. In the traditional association rule mining methods, a large number of candidate sets affect the mining efficiency, and the results are not easy for customers to understand. Therefore, a mining method based on binary particle swarm optimization ant colony algorithm to discover service demands and remanufacture services association rules is proposed. This method preprocesses the RMS records, converts them into a binary matrix, and uses the improved ant colony algorithm to mine the maximum frequent itemset. Because the particle swarm algorithm determines the initial pheromone concentration of the ant colony, it avoids the blindness of the ant colony, effectively enhances the searchability of the algorithm, and makes association rule mining faster and more accurate. Finally, a set of historical RMS record data of straightening machine is used to test the validity and feasibility of this method by extracting valid association rules to guide the design of RMS scheme for straightening machine parts.

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服务需求与再制造服务之间的关联规则挖掘

摘要服务需求与再制造服务之间的潜在关系是准确制定再制造服务计划、提高再制造服务效率和效益的关键。在传统的关联规则挖掘方法中，大量的候选集影响了挖掘效率，并且结果不容易被客户理解。为此，提出了一种基于二元粒子群优化蚁群算法挖掘服务需求和再制造服务关联规则的方法。该方法对RMS记录进行预处理，将其转化为二值矩阵，并利用改进的蚁群算法挖掘最大频繁项集。由于粒子群算法确定蚁群初始信息素浓度，避免了蚁群的盲目性，有效增强了算法的可搜索性，使得关联规则挖掘更快、更准确。最后，利用一组矫直机RMS历史记录数据，通过提取有效的关联规则来验证该方法的有效性和可行性，指导矫直机零件RMS方案的设计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.