• CN:11-2187/TH
  • ISSN:0577-6686

机械工程学报 ›› 2025, Vol. 61 ›› Issue (15): 372-384.doi: 10.3901/JME.2025.15.372

• 人机协作装配与调度 • 上一篇    

扫码分享

具有多类别客户约束的多场地复杂装备MRO服务工人调度优化

张丽科1, 梁冲1, 吴军伟2, 李浩1, 贺飞3, 李鹏宇3, 王昊琪1, 文笑雨1   

  1. 1. 郑州轻工业大学河南省机械装备智能制造重点实验室 郑州 450002;
    2. 河南中烟工业有限责任公司 郑州 450003;
    3. 中铁工程装备集团有限公司 郑州 450016
  • 收稿日期:2025-01-07 修回日期:2025-06-09 发布日期:2025-09-28
  • 作者简介:张丽科,男,1990年出生,讲师,硕士研究生导师。主要研究方向为车间调度、智能优化算法、工业数字孪生。E-mail:zhanglike2016@163.com;梁冲,男,2000年出生。主要研究方向为车间调度、智能优化算法。E-mail:17837764022@163.com;吴军伟,男,1975年出生。主要研究方向为生产制造产业链管理。E-mail:496428512@qq.com;李浩(通信作者),男,1981年出生,博士,教授,博士研究生导师。主要研究方向为工业数字孪生、产品设计方法学、智能制造服务和生产调度等E-mail:lihao@zzuli.edu.cn;贺飞,男,1984年出生,博士,正高级工程师。主要研究方向为地下工程装备(隧道掘进机)设计研发。E-mail:tbm666@126.com;李鹏宇,男,1989年出生,高级工程师。主要研究方向为隧道掘进装备信息化、智能化技术研究。E-mail:498485386@qq.com;王昊琪,男,1989年出生,博士,研究员,硕士研究生导师。主要研究方向为工业数字孪生、产品设计方法学、基于模型的系统工程,生产调度等。E-mail:haoqiwang0218@163.com;文笑雨,女,1988年出生,博士,副教授,硕士研究生导师。主要研究方向为车间调度、智能优化算法、工业数字孪生。E-mail:wenxiaoyu@zzuli.edu.cn
  • 基金资助:
    国家自然科学基金(52305560); 河南省自然科学基金优秀青年基金(242300421075); 河南省科技攻关(232102221043、242102241029、242102221042); 河南省高校科技创新人才支持计划(24hhasti048); 郑州轻工业大学博士科研基金(2022BSJJZK04)资助项目。

Optimization of MRO Service Worker Scheduling for Multi-site Complex Equipment with Multi-category Customer Constraints

ZHANG Like1, LIANG Chong1, WU Junwei2, LI Hao1, HE Fei3, LI Pengyu3, WANG Haoqi1, WEN Xiaoyu1   

  1. 1. Henan Provincial Key Laboratory of Intelligent Manufacturing of Mechanical Equipment, Zhengzhou University of Light Industry, Zhengzhou 450002;
    2. China Tobacco Henan Industrial Co., Ltd., Zhengzhou 450003;
    3. China Railway Engineering Equipment Group Co., Ltd., Zhengzhou 450016
  • Received:2025-01-07 Revised:2025-06-09 Published:2025-09-28

摘要: 服务工人作为企业执行MRO(Maintenance,repair and overhaul)服务的核心资源,其调度方案的优劣直接决定了MRO服务的运营效率和成本高低。当前,随着MRO客户需求的个性化与差异化,以及需求场点的高度分散性,以往的调度理论表现出明显的局限性,亟需开展针对性的调度决策理论研究。为此,提出一个涉及多类别客户约束的多场点复杂装备MRO服务工人调度问题。在该问题中,根据客户的质保属性和认可度,将客户划分为三类:质保期内的保内客户、质保期外且具有一定认可度的一般客户,以及无容忍度的临时客户。并以最大化服务总收益和最小化最大完工时间为优化目标构建了其数学模型。其次,基于NSGA-Ⅱ提出一个改进算法——MNSGA来求解问题。为了优化算法性能,设计了基于问题特性的初始化规则和局部搜索算子。最后,基于测试集和实际数据构建多组不同规模的算例,并设计了一系列试验。数值试验结果显示,所提算子能有效提高MNSGA性能。与其他知名算法对比,验证了MNSGA的有效性和竞争力。

关键词: MRO服务, 客户分类, 资源调度, 多目标优化

Abstract: As the core resource for enterprises to implement MRO(Maintenance, Repair and Overhaul) services, the scheduling scheme of service workers directly determines the operation efficiency and cost of MRO services. At present, with the personalization and differentiation of MRO customer needs, along with the geographically dispersed nature of demand sites, the previous scheduling theory shows obvious limitations, and it is urgent to carry out targeted theoretical research on scheduling decision-making. Therefore, a scheduling problem for MRO service workers that involves multi-site complex equipment and takes into account multi-class customer constraints is proposed. Firstly, we categorize customers into three distinct categories based on their warranty attributes and recognition levels: in-warranty customers who are still within the warranty period, general customers who possess a certain level of recognition but are beyond the warranty period, and temporary customers who have zero tolerance for delays. A mathematical model is formulated with the dual objectives of maximizing total service revenue and minimizing the maximum completion time simultaneously. Secondly, an enhanced algorithm named MNSGA is introduced, building upon the NSGA-II algorithm, to tackle the aforementioned problem. To optimize the performance of MNSGA, initialization rules and local search operators have been carefully crafted to align with the unique characteristics of the problem. Lastly, based on the test set and the actual data, multiple sets of examples of different sizes are constructed,and a series of experiments are designed. Numerical results show that the proposed operator can effectively improve the performance of MNSGA. Compared with other well-known algorithms, the effectiveness and competitiveness of MNSGA are verified.

Key words: MRO services, customer segmentation, resource scheduling, multi-objective optimization

中图分类号: