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中国管理科学

中国管理科学 ›› 2023, Vol. 31 ›› Issue (4): 239-249.doi: 10.16381/j.cnki.issn1003-207x.2020.0780

• 论文 • 上一篇    下一篇

基于3D打印技术的备件生产与运输协同调度问题研究

何珮洋1, 李昆鹏2, 田倩南3, 4, 5   

  1. 1.华北水利水电大学管理与经济学院，河南 郑州450046；2.华中科技大学管理学院，湖北 武汉430074；3.湖北经济学院湖北物流发展研究中心，湖北 武汉430205；4.湖北经济学院湖北企业文化研究中心，湖北 武汉430205；5.湖北经济学院碳排放权交易省部共建协同中心，湖北 武汉 430205
  • 收稿日期:2020-04-30 修回日期:2021-02-02 出版日期:2023-04-20 发布日期:2023-05-06
  • 通讯作者: 田倩南 (1989-)，女(汉族)，河南驻马店人，湖北经济学院湖北物流发展研究中心，副教授，博士，研究方向：供应链与物流管理，Email:Tiqn07@hbue.edu.cn. E-mail:Tiqn07@hbue.edu.cn
  • 基金资助:
    国家自然科学基金资助项目 (72001072, 71974056)；河南省新文科研究与改革实践项目(2021JGLX068, 2021JGLX070)；湖北省高等学校优秀中青年科技创新项目(T2022024);湖北省教育厅哲学社会科学研究项目(20Q120)；华北水利水电大学高层次人才科研启动项目(202209002)

The Integrated Production and Transportation Scheduling Problem Based on 3D Printing Technology

HE Pei-yang1, LI Kun-peng2, TIAN Qian-nan3, 4, 5   

  1. 1. School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China; 2. School of Management, Huazhong University of Science & Technology, Wuhan 430074, China;3. Hubei Logistics Development Research Center, Hubei University of Economics, Wuhan 430205, China; 4. Hubei Corporate Culture Research Center, Hubei University of Economics, Wuhan 430205, China; 5. Collaborative Innovation Center for Emissions Trading system Co-constructed by the Province and Ministry, Hubei University of Economics, Wuhan 430205, China
  • Received:2020-04-30 Revised:2021-02-02 Online:2023-04-20 Published:2023-05-06
  • Contact: 田倩南 E-mail:Tiqn07@hbue.edu.cn

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摘要： 基于3D打印的智能制造技术已经成为推动备件供应链转型升级的重要手段。在此背景下，本文研究“随时需要随时生产+即时配送”新型模式下的计划性维修备件的供应链协同问题。生产与运输协同调度问题在制造业领域普遍存在，属于NP-Hard问题。本文研究带时间窗的生产与运输协同调度问题，建立混合整数规划模型，根据Dantzig-Wolfe分解原理分别建立主问题和子问题数学优化模型，并采用改进的分支定价算法进行求解。在求解过程中，首先，构造可行解，基于可行路径调用CPLEX优化软件对主问题求解；其次，针对子问题的求解，根据研究问题的属性设计占优原则和加速策略，并对求得的非整数解进行分支；最后，通过对多组规模算例进行测试，数值实验结果表明：1)验证了所建立模型和改进算法的有效性；2)通过求解时间对比可知所使用加速策略能够将算法效率提高10倍左右；3)通过将生产和运输协同决策与实际运作中的分阶段决策结果对比，本文设计的方案可以将目标函数值平均优化50.33%。本研究不仅能够有效解决新型备件生产模式下的生产与运输协同调度问题，而且可以为企业实际运营决策提供科学依据，实现降本增效的目标。

关键词: 3D打印；备件供应链管理；时间窗；生产与运输协同调度；分支定价算法

Abstract: Intelligent manufacturing technology based on 3D printing has become an important means to promote the transformation and upgrading of spare parts supply chain. In this context, the supply chain coordination problem of scheduled maintenance spare parts is studied under the new mode of “make to order+delivery just in time”. The integrated production and transportation scheduling problem is NP-Hard in manufacturing industry. In this paper, the integrated production and transportation scheduling problem with time windows is studied, the integer programming model is established, the master problem and sub-problem are formulated respectively according to the Dantzig-Wolfe decomposition principle, and an improved branch-and-price algorithm is proposed. In the process of solving the problem, the feasible solution is firstly constructed and the CPLEX optimization software is used to solve the master problem. Secondly, the dominant rules and the acceleration strategies are designed to solve the sub-problem according to the characteristic of the problem, and the non-integer solutions are branched. Finally, the numerical experiment results show that the accuracy of the established model and the improved algorithm are verified. The comparison of CPU time shows that the acceleration strategy can improve the efficiency of the algorithm by about 10 times. By comparing the collaborative decisions between production and transportation with the decisions in actual operation, the scheme designed in this paper can optimize the objective function value by 50.33% on average. This study can not only effectively solve the integrated production and transportation scheduling problem under the new spare parts production mode, but also provide scientific basis for the actual operation decision of enterprises, and realize how to decrease cost and increase efficiency.

Key words: 3D printing; spare parts supply chain management; time windows; integrated production and transportation scheduling; branch-and-price algorithm

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