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.

中文翻译：

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服务需求与再制造服务的关联规则挖掘

服务需求与再制造服务（RMS）之间的潜在关系对于准确制定再制造服务计划、提高效率和效益至关重要。在传统的关联规则挖掘方法中，大量的候选集影响挖掘效率，结果不易被客户理解。为此，提出了一种基于二元粒子群优化蚁群算法的服务需求发现和再制造服务关联规则的挖掘方法。该方法对RMS记录进行预处理，将其转换为二进制矩阵，并使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了蚁群的初始信息素浓度，避免了蚁群的盲目性，有效地增强了算法的可搜索性，使关联规则挖掘更快、更准确。最后，利用一组矫直机的历史RMS记录数据，通过提取有效的关联规则来指导矫直机零件RMS方案的设计，验证了该方法的有效性和可行性。