IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i24p4932-d1298531.html
   My bibliography  Save this article

A Robust Flexible Optimization Model for 3D-Layout of Interior Equipment in a Multi-Floor Satellite

Author

Listed:
  • Masoud Hekmatfar

    (Welding and Joining Research Center, School of Industrial Engineering, Iran University of Science and Technology (IUST), Narmak 16846-13114, Iran
    School of Industrial Engineering, Iran University of Science and Technology (IUST), Narmak 16846-13114, Iran)

  • M. R. M. Aliha

    (Welding and Joining Research Center, School of Industrial Engineering, Iran University of Science and Technology (IUST), Narmak 16846-13114, Iran)

  • Mir Saman Pishvaee

    (School of Industrial Engineering, Iran University of Science and Technology (IUST), Narmak 16846-13114, Iran)

  • Tomasz Sadowski

    (Department of Solid Mechanics, Lublin University of Technology, Nadbystrzycka 40 Str., 20-618 Lublin, Poland)

Abstract

Defanging equipment layout in multi-floor satellites consists of two primary tasks: (i) allocating the equipment to the satellite’s layers and (ii) placing the equipment in each layer individually. In reviewing the previous literature in this field, firstly, the issue of assigning equipment to layers is observed in a few articles, and regarding the layout, the non-overlapping constraint has always been a challenge, particularly for components that do not have a circular cross-section. In addition to presenting a heuristic method for allocating equipment to different layers of the satellite, this article presents a robust flexible programming model (RFPM) for the placement of equipment at different layers, taking into account the inherent flexibility of the equipment in terms of placement and the subject of uncertainty. This model is based on the existing uncertainty between the distances between pieces of cuboid equipment, which has not been addressed in any of the previous research, and by comparing its outputs with cases from past studies, we demonstrate a significantly higher efficiency related to placing the equipment and meeting the limit of non-overlapping constraints between the equipment. Finally, it would be possible to reduce the design time in the conceptual and preparatory stages, as well as the satellite’s overall size, while still satisfying other constraints such as stability and thermal limitations, moments of inertia and center of gravity.

Suggested Citation

  • Masoud Hekmatfar & M. R. M. Aliha & Mir Saman Pishvaee & Tomasz Sadowski, 2023. "A Robust Flexible Optimization Model for 3D-Layout of Interior Equipment in a Multi-Floor Satellite," Mathematics, MDPI, vol. 11(24), pages 1-41, December.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:24:p:4932-:d:1298531
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/24/4932/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/24/4932/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yu, Chian-Son & Li, Han-Lin, 2000. "A robust optimization model for stochastic logistic problems," International Journal of Production Economics, Elsevier, vol. 64(1-3), pages 385-397, March.
    2. Klibi, Walid & Martel, Alain & Guitouni, Adel, 2010. "The design of robust value-creating supply chain networks: A critical review," European Journal of Operational Research, Elsevier, vol. 203(2), pages 283-293, June.
    3. Leung, Stephen C.H. & Tsang, Sally O.S. & Ng, W.L. & Wu, Yue, 2007. "A robust optimization model for multi-site production planning problem in an uncertain environment," European Journal of Operational Research, Elsevier, vol. 181(1), pages 224-238, August.
    4. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
    5. N. Chernov & Yu. Stoyan & T. Romanova & A. Pankratov, 2012. "Phi-Functions for 2D Objects Formed by Line Segments and Circular Arcs," Advances in Operations Research, Hindawi, vol. 2012, pages 1-26, May.
    6. Li, Zhenyu & Milenkovic, Victor, 1995. "Compaction and separation algorithms for non-convex polygons and their applications," European Journal of Operational Research, Elsevier, vol. 84(3), pages 539-561, August.
    7. John M. Mulvey & Robert J. Vanderbei & Stavros A. Zenios, 1995. "Robust Optimization of Large-Scale Systems," Operations Research, INFORMS, vol. 43(2), pages 264-281, April.
    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. Mohammaddust, Faeghe & Rezapour, Shabnam & Farahani, Reza Zanjirani & Mofidfar, Mohammad & Hill, Alex, 2017. "Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 632-653.
    2. Roya Soltani & Seyed J Sadjadi, 2014. "Reliability optimization through robust redundancy allocation models with choice of component type under fuzziness," Journal of Risk and Reliability, , vol. 228(5), pages 449-459, October.
    3. Zarrinpoor, Naeme & Fallahnezhad, Mohammad Saber & Pishvaee, Mir Saman, 2018. "The design of a reliable and robust hierarchical health service network using an accelerated Benders decomposition algorithm," European Journal of Operational Research, Elsevier, vol. 265(3), pages 1013-1032.
    4. Mirzapour Al-e-hashem, S.M.J. & Malekly, H. & Aryanezhad, M.B., 2011. "A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty," International Journal of Production Economics, Elsevier, vol. 134(1), pages 28-42, November.
    5. Behzadi, Golnar & O’Sullivan, Michael Justin & Olsen, Tava Lennon & Zhang, Abraham, 2018. "Agribusiness supply chain risk management: A review of quantitative decision models," Omega, Elsevier, vol. 79(C), pages 21-42.
    6. Shishebori, Davood & Yousefi Babadi, Abolghasem, 2015. "Robust and reliable medical services network design under uncertain environment and system disruptions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 268-288.
    7. Hashem Omrani & Farzane Adabi & Narges Adabi, 2017. "Designing an efficient supply chain network with uncertain data: a robust optimization—data envelopment analysis approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(7), pages 816-828, July.
    8. Antonio G. Martín & Manuel Díaz-Madroñero & Josefa Mula, 2020. "Master production schedule using robust optimization approaches in an automobile second-tier supplier," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 28(1), pages 143-166, March.
    9. João Flávio de Freitas Almeida & Samuel Vieira Conceição & Luiz Ricardo Pinto & Ricardo Saraiva de Camargo & Gilberto de Miranda Júnior, 2018. "Flexibility evaluation of multiechelon supply chains," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-27, March.
    10. Gilani, H. & Sahebi, H. & Oliveira, Fabricio, 2020. "Sustainable sugarcane-to-bioethanol supply chain network design: A robust possibilistic programming model," Applied Energy, Elsevier, vol. 278(C).
    11. Xu, Y. & Huang, G.H. & Qin, X.S. & Cao, M.F., 2009. "SRCCP: A stochastic robust chance-constrained programming model for municipal solid waste management under uncertainty," Resources, Conservation & Recycling, Elsevier, vol. 53(6), pages 352-363.
    12. Gilani, Hani & Sahebi, Hadi, 2022. "A data-driven robust optimization model by cutting hyperplanes on vaccine access uncertainty in COVID-19 vaccine supply chain," Omega, Elsevier, vol. 110(C).
    13. Xie, Y.L. & Huang, G.H. & Li, W. & Ji, L., 2014. "Carbon and air pollutants constrained energy planning for clean power generation with a robust optimization model—A case study of Jining City, China," Applied Energy, Elsevier, vol. 136(C), pages 150-167.
    14. Aalaei, Amin & Davoudpour, Hamid, 2017. "A robust optimization model for cellular manufacturing system into supply chain management," International Journal of Production Economics, Elsevier, vol. 183(PC), pages 667-679.
    15. Donya Rahmani & Arash Zandi & Sara Behdad & Arezou Entezaminia, 2021. "A light robust model for aggregate production planning with consideration of environmental impacts of machines," Operational Research, Springer, vol. 21(1), pages 273-297, March.
    16. Ratanakuakangwan, Sudlop & Morita, Hiroshi, 2021. "Hybrid stochastic robust optimization and robust optimization for energy planning – A social impact-constrained case study," Applied Energy, Elsevier, vol. 298(C).
    17. Jabbarzadeh, Armin & Fahimnia, Behnam & Seuring, Stefan, 2014. "Dynamic supply chain network design for the supply of blood in disasters: A robust model with real world application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 70(C), pages 225-244.
    18. Huang, Edward & Goetschalckx, Marc, 2014. "Strategic robust supply chain design based on the Pareto-optimal tradeoff between efficiency and risk," European Journal of Operational Research, Elsevier, vol. 237(2), pages 508-518.
    19. Bairamzadeh, Samira & Saidi-Mehrabad, Mohammad & Pishvaee, Mir Saman, 2018. "Modelling different types of uncertainty in biofuel supply network design and planning: A robust optimization approach," Renewable Energy, Elsevier, vol. 116(PA), pages 500-517.
    20. Shiva Zokaee & Armin Jabbarzadeh & Behnam Fahimnia & Seyed Jafar Sadjadi, 2017. "Robust supply chain network design: an optimization model with real world application," Annals of Operations Research, Springer, vol. 257(1), pages 15-44, October.

    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:gam:jmathe:v:11:y:2023:i:24:p:4932-:d:1298531. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.