This post shares applications, maps, data and/or publications that support infrastructure restoration in the aftermath of a disaster. Restoration scenarios might include, but are not limited to system resilience, economic cost models, environmental damage, system interdependency, and their integration into models, tools, datasets and maps.
Supply Chain Infrastructure Restoration Calculator Software Tool
Image of the User Interface:
- Preliminary Developer Guide and User Manual
- Download Application
- Suggested Citation: Ojha, A., Kanwar, B., Gude, V., Shoberg, T., Long, S. and Corns, S. (2018) “Supply Chain Infrastructure Restoration Calculator Software Tool”, https://communities.geoplatform.gov/disasters/supply-chain-infrastructure-restoration.
Data & Sources
Elevation digital elevation models (DEM) integrated with road networks for the central United States (Ramachandran et al., 2015):
Access and Use Information
- Public: This software is intended for public access and use
Ojha, A., Kanwar, B., Gude, V., Corns, S., Long, S., and Shoberg, T.
Suzanna Long (Email: firstname.lastname@example.org)
We gratefully acknowledge the encouragement and support of Julia Fields, National Geospatial Program Deputy Director (retired), US Geological Survey and Dr. Lynn Usery, Center of Excellence for Geospatial Information Science Director, US Geological Survey throughout the project. Further, we wish to acknowledge partial funding for this research through US Geological Survey award number G13AC00028, as well as funding from the Engineering Management and Systems Engineering Department, Missouri University of Science and Technology.
Ojha, A., Kanwar, B., Corns, S., Long, S., and Shoberg, T., 2018. Supply Chain Infrastructure Restoration Calculator (SCIRC) Software Tool: Developer Guide and User Manual, U.S. Geological Society, Open-File Report, in review.
Pérez Lespier, L., Long, S., and Shoberg, T., 2018, A model for the evaluation of environmental impact indicators for a sustainable maritime transportation system. Frontiers of Engineering Management, Accepted.
Ojha, A., Corns, S., Shoberg, T., Qin, R., and Long, S., 2018. Modeling and Simulation of Emergent Behavior in Transportation Infrastructure Restoration, in Mittal, S., Diallo, S., and Tolk, A., eds., Emergent Behavior in Complex Systems Engineering: A Modeling Simulation Approach. Chapter 15. 349-368. https://doi.org/10.1002/9781119378952.ch15.
Wilt, B., Long, S., and Shoberg, T., 2016. Defining resiliency: An integrative literature review, Proceedings of the American Society for Engineering Management 2016 International Annual Conference, Long, S., Ng, E.-H., Downing, C., and Nepal, B., eds., ASEM, Charlotte, NC. https://www.xcdsystem.com/asem/proceedings/pdfs/ASEM_2016_124.pdf
Corns, S., Long, S., and Shoberg, T., 2016. Infrastructure system restoration planning using evolutionary algorithms, Proceedings of the 26th Annual INCOSE International Symposium (IS2016), International Council on Systems Engineering, Edinburgh, Scotland, UK., 26, 1947-1956. http://dx.doi.org/10.1002/j.2334-5837.2016.00272.x.
Ramachandran, V, Long, S, Shoberg, T, Corns, S and Carlo, H 2016 Post-Disaster Supply Chain Interdependent Critical Infrastructure System Restoration: A Review of Data Necessary and Available for Modeling. Data Science Journal, 15: 1, pp. 1-13, DOI: http://dx.doi.org/10.5334/dsj-2016-001.
Ramachandran, V., Shoberg, T., Long, S.K., Corns, S., and Carlo, H., 2015. Identifying geographical interdependency in critical infrastructure systems using publically available geospatial data in order to model restoration strategies in the after-math of large-scale disasters, International Journal of Geospatial and Environmental Research, 2(1), Article 4. http://dc.uwm.edu/ijger/vol2/iss1/4.
Peréz Lespier, L., Long, S., and Shoberg, T., 2015. A systems thinking approach to post-disaster restoration of maritime transportation systems, in, Cetinkaya, S., and Ryan, J.K., eds., Proceedings of the 2015 Industrial and Systems Engineering Research Conference, Institute of Industrial Engineers, Norcross, GA.
Ramachandran, V., Long, S. K., Shoberg, T., Corns, S, and Carlo, H,. 2015. Modeling supply chain network resiliency in the aftermath of an extreme event, Natural Hazards Review, 16 (4), 040150015. http://dx.doi.org/10.1061/(ASCE)NH.1527-6996.0000184.
Álvarez, W.,F., Vigo, A., Carlo, H., Long, S., Shoberg, T., & Corns, S. (2014). A mathematical model for supply chain network infrastructure restoration. IIE Annual Conference.Proceedings, , 78-85. https://www.researchgate.net/publication/287706793_A_mathematical_model_for_supply_chain_network_infrastructure_restoration.
Long, S.K., T. Shoberg, V. Ramachandran, S.M. Corns, and H.J. Carlo, 2013. Integrating complexity into data-driven multi-hazard supply chain network strategies, Proceedings of the ASPRS\CaGIS 2013 Specialty Conference, 27- 31 Oct, San Antonio, Texas, unpaginated,(American Society for Photogrammetry and Remote Sensing, Bethesda, Maryland).
Gude, V., Ojha, A., Kanwar, B., Corns, S., Shoberg, T., and Long, S., 2018, Supply Chain Infrastructure Restoration Calculator (SCIRC) tool. http://web.mst.edu/~cornss/scirc/scirc.exe