Abstract The purpose of this study is to reduce the maintenance time, cost, and scheduling distance and to determine the maintenance priority of planned shop visit products. This study introduces the concept of maintenance map, establishes the evaluation indicators system of maintenance map from the perspective of organizational quality-specific immune, and puts forward two key dimensions named maintenance plan and technical support of maintenance map. Based on the theoretical framework, construct a heuristic decision model of planned shop visit products based on similar reasoning, set the maintenance services data of Harbin Dongan Engine Co., Ltd., as research objects, and use the concrete schemes and cases to solve and carry out an empirical analysis of the heuristic decision of planned shop visit products based on similar reasoning with the help of the ant colony algorithm; the empirical analysis results indicate that maintenance map and evaluation indicators system are the fundamental basis of the heuristic decision based on similar reasoning, the combinations of similar reasoning and ant colony algorithm can achieve the optimal heuristic decision of planned shop visit products, which have effectiveness, feasibility, and operability. This research will be conductive to give out the heuristic decision of scheduling schemes of planned shop visit products, which will be beneficial to enhance maintenance efficiency and quality, promote learning effects and learning pattern paths, and reduce maintenance scheduling distances of planned shop visit products from the aspects of theoretical framework guidance, empirical system enlightenment, and conceptual paradigm reference.

中文翻译：

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基于相似推理方法的计划访店产品启发式决策：基于组织质量特异性免疫的视角

摘要 本研究的目的是减少维修时间、成本和调度距离，并确定计划回访产品的维修优先级。本研究引入维修地图的概念，从组织质量特定免疫的角度建立维修地图评价指标体系，提出维修计划和维修地图技术支持两个关键维度。基于理论框架，构建基于相似推理的计划回访产品启发式决策模型，以哈尔滨东安发动机有限公司维修服务数据为研究对象，并利用具体的方案和案例，借助蚁群算法求解基于相似推理的计划光顾产品的启发式决策，并进行实证分析；实证分析结果表明，维修图和评价指标体系是基于相似推理的启发式决策的基本依据，相似推理与蚁群算法的结合可以实现计划回访产品的最优启发式决策，具有有效性，可行性和可操作性。本研究将有助于给出计划回访产品的调度方案的启发式决策，有利于提高维护效率和质量，促进学习效果和学习模式路径，