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An Approach for Integrating Valuable Flexibility During Conceptual Design of Networks


  • Y. G. Melese

    () (Delft University of Technology)

  • P. W. Heijnen

    (Delft University of Technology)

  • R. M. Stikkelman

    (Delft University of Technology)

  • P. M. Herder

    (Delft University of Technology)


Energy and industrial networks such as pipeline-based carbon capture and storage infrastructures and (bio)gas infrastructures are designed and developed in the presence of major uncertainties. Conventional design methods are based on deterministic forecasts of most likely scenarios and produce networks that are optimal under those scenarios. However, future design requirements and operational environments are uncertain and networks designed based on deterministic forecasts provide sub-optimal performance. This study introduces a method based on the flexible design approach and the concept of real options to deal with uncertainties during conceptual design of networks. The proposed method uses a graph theoretical network model and Monte Carlo simulations to explore candidate designs, and identify and integrate flexibility enablers to pro-actively deal with uncertainties. Applying the method on a hypothetical network, it is found that integrating flexibility enablers (real options) such as redundant capacity and length can help to enhance the long term performance of networks. When compared to deterministic rigid designs, the flexible design enables cost effective expansions as uncertainty unfolds in the future.

Suggested Citation

  • Y. G. Melese & P. W. Heijnen & R. M. Stikkelman & P. M. Herder, 2017. "An Approach for Integrating Valuable Flexibility During Conceptual Design of Networks," Networks and Spatial Economics, Springer, vol. 17(2), pages 317-341, June.
  • Handle: RePEc:kap:netspa:v:17:y:2017:i:2:d:10.1007_s11067-016-9328-8
    DOI: 10.1007/s11067-016-9328-8

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    References listed on IDEAS

    1. Desai, Jitamitra & Sen, Suvrajeet, 2010. "A global optimization algorithm for reliable network design," European Journal of Operational Research, Elsevier, vol. 200(1), pages 1-8, January.
    2. Mian Li & Steven Gabriel & Yohan Shim & Shapour Azarm, 2011. "Interval Uncertainty-Based Robust Optimization for Convex and Non-Convex Quadratic Programs with Applications in Network Infrastructure Planning," Networks and Spatial Economics, Springer, vol. 11(1), pages 159-191, March.
    3. Byung Chung & Tao Yao & Chi Xie & Andreas Thorsen, 2011. "Robust Optimization Model for a Dynamic Network Design Problem Under Demand Uncertainty," Networks and Spatial Economics, Springer, vol. 11(2), pages 371-389, June.
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    5. P. W. Heijnen & A. Ligtvoet & R. M. Stikkelman & P. M. Herder, 2014. "Maximising the Worth of Nascent Networks," Networks and Spatial Economics, Springer, vol. 14(1), pages 27-46, March.
    6. 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.
    7. Fangxia Zhao & Jianjun Wu & Huijun Sun & Ziyou Gao & Ronghui Liu, 2016. "Population-driven Urban Road Evolution Dynamic Model," Networks and Spatial Economics, Springer, vol. 16(4), pages 997-1018, December.
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