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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. A. Ben-Tal & A. Nemirovski, 1998. "Robust Convex Optimization," Mathematics of Operations Research, INFORMS, vol. 23(4), pages 769-805, November.
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