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Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing

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  • Jianming Zhang
  • Xifan Yao
  • Yun Li

Abstract

With the increasing prosperity of additive manufacturing, the 3D-printing shop scheduling problem has presented growing importance. The scheduling of such a shop is imperative for saving time and cost, but the problem is hard to solve, especially for simultaneous multi-part assignment and placement. This paper develops an improved evolutionary algorithm for application to additive manufacturing, by combining a genetic algorithm with a heuristic placement strategy to take into account the allocation and placement of parts integrally. The algorithm is designed also to enhance the optimisation efficiency by introducing an initialisation method based on the characteristics of the 3D printing process through the development of corresponding time calculation model. Experiments show that the developed algorithm can find better solutions compared with state-of-the-art algorithms such as simple genetic algorithm, particle swarm optimisation and heuristic algorithms.

Suggested Citation

  • Jianming Zhang & Xifan Yao & Yun Li, 2020. "Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(8), pages 2263-2282, April.
    • Handle: RePEc:taf:tprsxx:v:58:y:2020:i:8:p:2263-2282
    DOI: 10.1080/00207543.2019.1617447
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    Citations

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    Cited by:

    1. Altekin, F. Tevhide & Bukchin, Yossi, 2022. "A multi-objective optimization approach for exploring the cost and makespan trade-off in additive manufacturing," European Journal of Operational Research, Elsevier, vol. 301(1), pages 235-253.
    2. Harshad Sonar & Vivek Khanzode & Milind Akarte, 2022. "Additive Manufacturing Enabled Supply Chain Management: A Review and Research Directions," Vision, , vol. 26(2), pages 147-162, June.
    3. Gahm, Christian & Uzunoglu, Aykut & Wahl, Stefan & Ganschinietz, Chantal & Tuma, Axel, 2022. "Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning," European Journal of Operational Research, Elsevier, vol. 296(3), pages 819-836.
    4. Nascimento, Paulo Jorge & Silva, Cristóvão & Antunes, Carlos Henggeler & Moniz, Samuel, 2024. "Optimal decomposition approach for solving large nesting and scheduling problems of additive manufacturing systems," European Journal of Operational Research, Elsevier, vol. 317(1), pages 92-110.
    5. Fowler, John W. & Mönch, Lars, 2022. "A survey of scheduling with parallel batch (p-batch) processing," European Journal of Operational Research, Elsevier, vol. 298(1), pages 1-24.
    6. Yizhe Yang & Bingshan Liu & Haochen Li & Xin Li & Gong Wang & Shan Li, 2023. "A nesting optimization method based on digital contour similarity matching for additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2825-2847, August.
    7. Alessandro Druetto & Erica Pastore & Elena Rener, 2023. "Parallel batching with multi-size jobs and incompatible job families," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 440-458, July.
    8. Jose M. Framinan & Paz Perez-Gonzalez & Victor Fernandez-Viagas, 2023. "An overview on the use of operations research in additive manufacturing," Annals of Operations Research, Springer, vol. 322(1), pages 5-40, March.
    9. Xifan Yao & Nanfeng Ma & Jianming Zhang & Kesai Wang & Erfu Yang & Maurizio Faccio, 2024. "Enhancing wisdom manufacturing as industrial metaverse for industry and society 5.0," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 235-255, January.

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