TY - JOUR
T1 - A bi-objective formulation for robust defense strategies in multi-commodity networks
AU - McCarter, Matthew
AU - Barker, Kash
AU - Johansson, Jonas
AU - Ramirez-Marquez, Jose E.
N1 - Publisher Copyright:
© 2018 Elsevier Ltd
PY - 2018/8
Y1 - 2018/8
N2 - Characterizing system performance under disruption is a growing area of research, particularly for describing a system's resilience to disruptive events. Within the framework of system resilience, this study approaches the minimization of a multiple-commodity system's vulnerability to multiple disruptions. The vulnerability of a system is defined by the degree to which commodities can no longer flow through the system to satisfy demand given a disruptive event. A multi-objective formulation is developed to find defense strategies at minimal cost that maintain a high level of demand satisfaction across all commodities. A solution method involving an estimation of the Pareto frontier via the Non-dominated Sorted Genetic Algorithm II (NSGA-II) is also proposed. A decision support environment is proposed and supported by application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed formulation and solution method are illustrated with an example generated from the multi-commodity Swedish rail network.
AB - Characterizing system performance under disruption is a growing area of research, particularly for describing a system's resilience to disruptive events. Within the framework of system resilience, this study approaches the minimization of a multiple-commodity system's vulnerability to multiple disruptions. The vulnerability of a system is defined by the degree to which commodities can no longer flow through the system to satisfy demand given a disruptive event. A multi-objective formulation is developed to find defense strategies at minimal cost that maintain a high level of demand satisfaction across all commodities. A solution method involving an estimation of the Pareto frontier via the Non-dominated Sorted Genetic Algorithm II (NSGA-II) is also proposed. A decision support environment is proposed and supported by application of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed formulation and solution method are illustrated with an example generated from the multi-commodity Swedish rail network.
KW - Max flow
KW - Multi-commodity network flow
KW - Rail transportation
KW - Resilience
KW - Vulnerability
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U2 - 10.1016/j.ress.2018.04.011
DO - 10.1016/j.ress.2018.04.011
M3 - Article
AN - SCOPUS:85046398500
SN - 0951-8320
VL - 176
SP - 154
EP - 161
JO - Reliability Engineering and System Safety
JF - Reliability Engineering and System Safety
ER -