Production scheduling of additively manufactured metal parts

被引：0

作者：

Ying, Kuo-Ching ^{[1
]}

Lin, Shih-Wei ^{[2
,3
,4
]}

Pourhejazy, Pourya ^{[5
]}

Lee, Fei-Huan ^{[1
,6
]}

机构：

[1] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei 10608, Taiwan

[2] Chang Gung Univ, Dept Informat Management, Taoyuan 333, Taiwan

[3] Keelung Chang Gung Mem Hosp, Dept Emergency Med, Keelung 204, Taiwan

[4] Ming Chi Univ Technol, Dept Ind Engn & Management, New Taipei City 243, Taiwan

[5] Arctic Univ Norway, Dept Ind Engn, UiT, Lodve Langesgate 2, N-8514 Narvik, Norway

[6] Everlight Elect Co LTD, IT Div, 6-8 Zhonghua Rd, New Taipei City 238, Taiwan

来源：

CIRP JOURNAL OF MANUFACTURING SCIENCE AND TECHNOLOGY | 2025年 / 57卷

关键词：

Additive manufacturing; 3D printing; Production planning; Laser Powder Bed Fusion (PBF-LB/M); Optimization; Sustainable Development Goals: SDG 9; BEAM SEARCH ALGORITHMS; MAKESPAN;

D O I：

10.1016/j.cirpj.2025.01.005

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

The production of metal products is one of the main areas where supply chains benefit from adopting additive manufacturing (AM). Optimizing the production process facilitates the widespread adoption of AM by improving know-how and reducing costs. This study offers a twofold contribution to facilitate the implementation of Additive Manufacturing Scheduling Problems (AMSPs) for producing metal parts. First, two mathematical formulations are proposed to enable the use of commercial solvers to optimize small- and medium-sized AMSPs. Second, a highly competitive solution algorithm called Tweaked Iterative Beam Search (TIBS) is developed to find (near-) optimal solutions to industry-scale problems. A total of 225 instances of various workloads are considered for numerical experiments, and the algorithm's performance is evaluated, comparing it with the baselines. In 165 small and medium-sized instances, TIBS yielded 71 optimal solutions and 106 best-found solutions. For large-scale cases, all of the best-found solutions were obtained by TIBS. The statistical results support the significance of the outcomes in the optimization performance.

引用

页码：100 / 115

页数：16

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