Applying Ranking and Selection Procedures to Long-Term Mitigation for Improved Network Restoration

EURO Journal on Computational Optimization, 4(3), pages 447-481, 2016.
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Authors:

Emily Heath
(email)
Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, U.S.A.

John E. Mitchell
(email)
Department of Mathematical Sciences, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, U.S.A.

Thomas Sharkey
(email)
Department of Industrial and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York 12180-3590, U.S.A.

Abstract:

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 infras- tructure 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 miti- gation, 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 number of integer programs for each method, and comparing the level of confidence achieved to determine a best single arc mitigation plan. We find that R&S procedures perform as well or better than brute force procedures in all cases, and significantly out- perform the brute force procedure in almost all cases (five out of six). Having developed a general framework for determining the best single arc mitigation plan for any network, we conclude with thoughts and challenges on how this framework can be expanded and applied to different problems.

Keywords: ranking and selection; network resilience; mitigation; restoration

Research supported in part by the National Science Foundation under grant numbers CMMI-1314350 and CMMI-1254258.

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