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Optimizing dynamic investment decisions for railway systems protection

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  • Starita, Stefano
  • Scaparra, Maria Paola

Abstract

Past and recent events have shown that railway infrastructure systems are particularly vulnerable to natural catastrophes, unintentional accidents and terrorist attacks. Protection investments are instrumental in reducing economic losses and preserving public safety. A systematic approach to plan security investments is paramount to guarantee that limited protection resources are utilized in the most efficient manner. In this paper, we present an optimization model to identify the railway assets which should be protected to minimize the impact of worst case disruptions on passenger flows. We consider a dynamic investment problem where protection resources become available over a planning horizon. The problem is formulated as a bilevel mixed-integer model and solved using two different decomposition approaches. Random instances of different sizes are generated to compare the solution algorithms. The model is then tested on the Kent railway network to demonstrate how the results can be used to support efficient protection decisions.

Suggested Citation

  • Starita, Stefano & Scaparra, Maria Paola, 2016. "Optimizing dynamic investment decisions for railway systems protection," European Journal of Operational Research, Elsevier, vol. 248(2), pages 543-557.
    • Handle: RePEc:eee:ejores:v:248:y:2016:i:2:p:543-557
    DOI: 10.1016/j.ejor.2015.07.025
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    Cited by:

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    5. Ben Yaghlane, Asma & Azaiez, M. Naceur, 2017. "Systems under attack-survivability rather than reliability: Concept, results, and applications," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1156-1164.
    6. Annunziata Esposito Amideo & Stefano Starita & Maria Paola Scaparra, 2019. "Assessing Protection Strategies for Urban Rail Transit Systems: A Case-Study on the Central London Underground," Sustainability, MDPI, vol. 11(22), pages 1-21, November.
    7. Sreekumaran, Harikrishnan & Hota, Ashish R. & Liu, Andrew L. & Uhan, Nelson A. & Sundaram, Shreyas, 2021. "Equilibrium strategies for multiple interdictors on a common network," European Journal of Operational Research, Elsevier, vol. 288(2), pages 523-538.
    8. Jabarzare, Ziba & Zolfagharinia, Hossein & Najafi, Mehdi, 2020. "Dynamic interdiction networks with applications in illicit supply chains," Omega, Elsevier, vol. 96(C).
    9. Stefano Starita & M. Paola Scaparra & Jesse R. O’Hanley, 2017. "A dynamic model for road protection against flooding," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(1), pages 74-88, January.
    10. Karakose, Gokhan & McGarvey, Ronald G., 2018. "Capacitated path-aggregation constraint model for arc disruption in networks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 225-238.
    11. Mingming Zheng & Hanzhang Zuo & Zitong Zhou & Yuhan Bai, 2023. "Recovery Strategies for Urban Rail Transit Network Based on Comprehensive Resilience," Sustainability, MDPI, vol. 15(20), pages 1-17, October.
    12. Girish Ch. Dey & Mamata Jenamani, 2019. "Optimizing fortification plan of capacitated facilities with maximum distance limits," OPSEARCH, Springer;Operational Research Society of India, vol. 56(1), pages 151-173, March.
    13. Lei Wang & Qing Liu & Tongle Yin, 2018. "Decision-making of investment in navigation safety improving schemes with application of cumulative prospect theory," Journal of Risk and Reliability, , vol. 232(6), pages 710-724, December.
    14. Yaghlane, Asma Ben & Azaiez, M. Naceur & Mrad, Mehdi, 2019. "System survivability in the context of interdiction networks," Reliability Engineering and System Safety, Elsevier, vol. 185(C), pages 362-371.
    15. Jiang, J. & Liu, X., 2018. "Multi-objective Stackelberg game model for water supply networks against interdictions with incomplete information," European Journal of Operational Research, Elsevier, vol. 266(3), pages 920-933.

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