IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v274y2024ics0925527324001646.html
   My bibliography  Save this article

Design complexity based flexible order dispatching for additive manufacturing production

Author

Listed:
  • Kim, Kyudong
  • Park, Kijung
  • Jeon, Hyun Woo
  • Kremer, Gül E.

Abstract

Understanding design complexity for additive manufacturing (AM) is essential in AM production planning since conventional make-to-order production for individual AM orders of complex designs can amplify operational uncertainty in an entire AM production system. As a response, this study aims not only to demonstrate the impact of design complexity on AM production but also to propose a novel order dispatching approach based on design complexity that mitigates operational uncertainty in an AM production system. First, a design complexity measure was developed using an information theoretic approach. Next, a discrete-event simulation model to represent an AM production system consisting of parallel AM machines for jet-engine bracket designs was built to identify the impact of design complexity on average order lead time and total production cost through regressions. Finally, a flexible order dispatching rule that reflects operational attitudes toward design complexity was proposed to determine part-processing priorities by tracking both part- and system-level design complexity states in a centralized queue for AM production. The proposed dispatching rule was compared with relevant static dispatching rules to assess its performance in operational efficiency under varied attitudes toward design complexity. The findings from this study clearly showed the negative impact of design complexity on operational performance for AM production. Moreover, the proposed dispatching rule resulted in lead time reduction and balanced lead time performance in AM production against alternative static dispatching strategies. This study demonstrates the importance of design complexity-based flexible operations to properly handle latent uncertainties in an AM production system.

Suggested Citation

  • Kim, Kyudong & Park, Kijung & Jeon, Hyun Woo & Kremer, Gül E., 2024. "Design complexity based flexible order dispatching for additive manufacturing production," International Journal of Production Economics, Elsevier, vol. 274(C).
  • Handle: RePEc:eee:proeco:v:274:y:2024:i:c:s0925527324001646
    DOI: 10.1016/j.ijpe.2024.109307
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527324001646
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2024.109307?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mohammad Rohaninejad & Reza Tavakkoli-Moghaddam & Behdin Vahedi-Nouri & Zdeněk Hanzálek & Shadi Shirazian, 2022. "A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines," International Journal of Production Research, Taylor & Francis Journals, vol. 60(20), pages 6205-6225, October.
    2. Martin Baumers & Chris Tuck & Ricky Wildman & Ian Ashcroft & Richard Hague, 2017. "Shape Complexity and Process Energy Consumption in Electron Beam Melting: A Case of Something for Nothing in Additive Manufacturing?," Journal of Industrial Ecology, Yale University, vol. 21(S1), pages 157-167, November.
    3. Og[breve]uz, Ceyda & Sibel Salman, F. & Bilgintürk YalçIn, Zehra, 2010. "Order acceptance and scheduling decisions in make-to-order systems," International Journal of Production Economics, Elsevier, vol. 125(1), pages 200-211, May.
    4. Jun Kim & Hyun-Jung Kim, 2021. "Parallel machine scheduling with multiple processing alternatives and sequence-dependent setup times," International Journal of Production Research, Taylor & Francis Journals, vol. 59(18), pages 5438-5453, September.
    5. Sivadasan, S. & Efstathiou, J. & Calinescu, A. & Huatuco, L. Huaccho, 2006. "Advances on measuring the operational complexity of supplier-customer systems," European Journal of Operational Research, Elsevier, vol. 171(1), pages 208-226, May.
    6. Juan De Antón & Félix Villafáñez & David Poza & Adolfo López-Paredes, 2023. "A framework for production planning in additive manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 61(24), pages 8674-8691, December.
    7. Colosimo, Bianca Maria & Cavalli, Simona & Grasso, Marco, 2020. "A cost model for the economic evaluation of in-situ monitoring tools in metal additive manufacturing," International Journal of Production Economics, Elsevier, vol. 223(C).
    8. Park, Kijung & Okudan Kremer, Gül E., 2015. "Assessment of static complexity in design and manufacturing of a product family and its impact on manufacturing performance," International Journal of Production Economics, Elsevier, vol. 169(C), pages 215-232.
    9. Ramin Ahmed & H. Sebastian Heese & Michael Kay, 2023. "Designing a manufacturing network with additive manufacturing using stochastic optimisation," International Journal of Production Research, Taylor & Francis Journals, vol. 61(7), pages 2267-2287, April.
    10. Sgarbossa, Fabio & Peron, Mirco & Lolli, Francesco & Balugani, Elia, 2021. "Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand," International Journal of Production Economics, Elsevier, vol. 233(C).
    11. Weller, Christian & Kleer, Robin & Piller, Frank T., 2015. "Economic implications of 3D printing: Market structure models in light of additive manufacturing revisited," International Journal of Production Economics, Elsevier, vol. 164(C), pages 43-56.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Marić, Josip & Opazo-Basáez, Marco & Vlačić, Božidar & Dabić, Marina, 2023. "Innovation management of three-dimensional printing (3DP) technology: Disclosing insights from existing literature and determining future research streams," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Naghshineh, Bardia & Carvalho, Helena, 2022. "The implications of additive manufacturing technology adoption for supply chain resilience: A systematic search and review," International Journal of Production Economics, Elsevier, vol. 247(C).
    3. Ding, Jin & Baumers, Martin & Clark, Elizabeth A. & Wildman, Ricky D., 2021. "The economics of additive manufacturing: Towards a general cost model including process failure," International Journal of Production Economics, Elsevier, vol. 237(C).
    4. Ghuge, Sagar & Akarte, Milind, 2024. "Additive manufacturing service bureau selection: A Bayesian network integrated framework," International Journal of Production Economics, Elsevier, vol. 276(C).
    5. Beltagui, Ahmad & Gold, Stefan & Kunz, Nathan & Reiner, Gerald, 2023. "Special Issue: Rethinking operations and supply chain management in light of the 3D printing revolution," International Journal of Production Economics, Elsevier, vol. 255(C).
    6. Rayna, Thierry & Striukova, Ludmila, 2021. "Assessing the effect of 3D printing technologies on entrepreneurship: An exploratory study," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    7. Germán Herrera Vidal & Jairo R. Coronado-Hernández & Claudia Minnaard, 2023. "Measuring manufacturing system complexity: a literature review," Journal of Intelligent Manufacturing, Springer, vol. 34(7), pages 2865-2888, October.
    8. Foshammer, Jeppe & Søberg, Peder Veng & Helo, Petri & Ituarte, Iñigo Flores, 2022. "Identification of aftermarket and legacy parts suitable for additive manufacturing: A knowledge management-based approach," International Journal of Production Economics, Elsevier, vol. 253(C).
    9. Menezes, Mozart B.C. & Ruiz-Hernández, Diego & Chen, Yen-Tsang, 2021. "On the validity and practical relevance of a measure for structural complexity," International Journal of Production Economics, Elsevier, vol. 240(C).
    10. Wang, Xiuli & Cheng, T.C.E., 2015. "A heuristic for scheduling jobs on two identical parallel machines with a machine availability constraint," International Journal of Production Economics, Elsevier, vol. 161(C), pages 74-82.
    11. Ayman Altuwaim & Abdulelah AlTasan & Abdulmohsen Almohsen, 2023. "Success Criteria for Applying Construction Technologies in Residential Projects," Sustainability, MDPI, vol. 15(8), pages 1-15, April.
    12. Ioannis Falkonakis & Saeid Lotfian & Baran Yeter, 2024. "Multi-Criteria Decision Analysis of an Innovative Additive Manufacturing Technique for Onboard Maintenance," Sustainability, MDPI, vol. 16(9), pages 1-18, April.
    13. Park, Kijung & Okudan Kremer, Gül E., 2015. "Assessment of static complexity in design and manufacturing of a product family and its impact on manufacturing performance," International Journal of Production Economics, Elsevier, vol. 169(C), pages 215-232.
    14. Martin Baumers & Luca Beltrametti & Angelo Gasparre & Richard Hague, 2017. "Informing additive manufacturing technology adoption: total cost and the impact of capacity utilisation," International Journal of Production Research, Taylor & Francis Journals, vol. 55(23), pages 6957-6970, December.
    15. Wang, Xiuli & Zhu, Qianqian & Cheng, T.C.E., 2015. "Subcontracting price schemes for order acceptance and scheduling," Omega, Elsevier, vol. 54(C), pages 1-10.
    16. Turkcan, Hulya & Imamoglu, Salih Zeki & Ince, Huseyin, 2022. "To be more innovative and more competitive in dynamic environments: The role of additive manufacturing," International Journal of Production Economics, Elsevier, vol. 246(C).
    17. Slotnick, Susan A., 2011. "Order acceptance and scheduling: A taxonomy and review," European Journal of Operational Research, Elsevier, vol. 212(1), pages 1-11, July.
    18. Roberto Cordone & Pierre Hosteins & Giovanni Righini, 2018. "A Branch-and-Bound Algorithm for the Prize-Collecting Single-Machine Scheduling Problem with Deadlines and Total Tardiness Minimization," INFORMS Journal on Computing, INFORMS, vol. 30(1), pages 168-180, February.
    19. Kumar, Mukesh & Tsolakis, Naoum & Agarwal, Anshul & Srai, Jagjit Singh, 2020. "Developing distributed manufacturing strategies from the perspective of a product-process matrix," International Journal of Production Economics, Elsevier, vol. 219(C), pages 1-17.
    20. Yun Hui Lin & Yuan Wang & Loo Hay Lee & Ek Peng Chew, 2021. "Robust facility location with structural complexity and demand uncertainty," Flexible Services and Manufacturing Journal, Springer, vol. 33(2), pages 485-507, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:proeco:v:274:y:2024:i:c:s0925527324001646. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.