Applying ranking and selection procedures to long-term mitigation for improved network restoration

In this paper, we consider methods to determine the best single arc mitigation plan for improving rapid recovery of a network with a given level of statistical certainty. This problem is motivated by infrastructure managers interested in increasing the resilience of their systems through costly long...

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Autores principales:	EmilyA. Heath, JohnE. Mitchell, ThomasC. Sharkey
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2016
Materias:	90B99 90C90 62F07 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/6785af39dc544a60b4b18afafb4b0e51
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spelling	oai:doaj.org-article:6785af39dc544a60b4b18afafb4b0e512021-12-02T05:00:57ZApplying ranking and selection procedures to long-term mitigation for improved network restoration2192-440610.1007/s13675-016-0065-zhttps://doaj.org/article/6785af39dc544a60b4b18afafb4b0e512016-09-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S219244062100071Xhttps://doaj.org/toc/2192-4406In this paper, we consider methods to determine the best single arc mitigation plan for improving rapid recovery of a network with a given level of statistical certainty. This problem is motivated by infrastructure managers interested in increasing the resilience of their systems through costly long-term mitigation procedures. Our problem is two stage, where we consider a small number of pre-event decisions for mitigation, with a large second-stage integer programming problem to capture the restoration process for each damage scenario and each mitigation plan. We consider a ranking and selection (R&S) procedure and compare its performance against a brute force method using standard statistical testing on problems with low, medium, and high damage levels. These comparisons are made by using the same computational effort for each method and comparing the level of confidence achieved to determine a best single arc mitigation plan and whether the same best single arc mitigation plan is found. We find that the R&S procedure can find a best single arc mitigation plan with 95 % confidence in all cases, and the brute force procedure, while identifying the same mitigation plan as being one of the best, is unable to determine a single best mitigation plan in all but one case. Having developed a general framework for determining the best single arc mitigation plan with statistical certainty for any network, we conclude with thoughts and challenges on how this framework can be expanded and applied to different problems.EmilyA. HeathJohnE. MitchellThomasC. SharkeyElsevierarticle90B9990C9062F07Applied mathematics. Quantitative methodsT57-57.97Electronic computers. Computer scienceQA75.5-76.95ENEURO Journal on Computational Optimization, Vol 4, Iss 3, Pp 447-481 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	90B99 90C90 62F07 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95
spellingShingle	90B99 90C90 62F07 Applied mathematics. Quantitative methods T57-57.97 Electronic computers. Computer science QA75.5-76.95 EmilyA. Heath JohnE. Mitchell ThomasC. Sharkey Applying ranking and selection procedures to long-term mitigation for improved network restoration
description	In this paper, we consider methods to determine the best single arc mitigation plan for improving rapid recovery of a network with a given level of statistical certainty. This problem is motivated by infrastructure managers interested in increasing the resilience of their systems through costly long-term mitigation procedures. Our problem is two stage, where we consider a small number of pre-event decisions for mitigation, with a large second-stage integer programming problem to capture the restoration process for each damage scenario and each mitigation plan. We consider a ranking and selection (R&S) procedure and compare its performance against a brute force method using standard statistical testing on problems with low, medium, and high damage levels. These comparisons are made by using the same computational effort for each method and comparing the level of confidence achieved to determine a best single arc mitigation plan and whether the same best single arc mitigation plan is found. We find that the R&S procedure can find a best single arc mitigation plan with 95 % confidence in all cases, and the brute force procedure, while identifying the same mitigation plan as being one of the best, is unable to determine a single best mitigation plan in all but one case. Having developed a general framework for determining the best single arc mitigation plan with statistical certainty for any network, we conclude with thoughts and challenges on how this framework can be expanded and applied to different problems.
format	article
author	EmilyA. Heath JohnE. Mitchell ThomasC. Sharkey
author_facet	EmilyA. Heath JohnE. Mitchell ThomasC. Sharkey
author_sort	EmilyA. Heath
title	Applying ranking and selection procedures to long-term mitigation for improved network restoration
title_short	Applying ranking and selection procedures to long-term mitigation for improved network restoration
title_full	Applying ranking and selection procedures to long-term mitigation for improved network restoration
title_fullStr	Applying ranking and selection procedures to long-term mitigation for improved network restoration
title_full_unstemmed	Applying ranking and selection procedures to long-term mitigation for improved network restoration
title_sort	applying ranking and selection procedures to long-term mitigation for improved network restoration
publisher	Elsevier
publishDate	2016
url	https://doaj.org/article/6785af39dc544a60b4b18afafb4b0e51
work_keys_str_mv	AT emilyaheath applyingrankingandselectionprocedurestolongtermmitigationforimprovednetworkrestoration AT johnemitchell applyingrankingandselectionprocedurestolongtermmitigationforimprovednetworkrestoration AT thomascsharkey applyingrankingandselectionprocedurestolongtermmitigationforimprovednetworkrestoration
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Applying ranking and selection procedures to long-term mitigation for improved network restoration

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