Measuring and maximizing resilience of freight transportation networks

doi:10.1016/j.cor.2011.09.017

Computers & Operations Research

Volume 39, Issue 7, July 2012, Pages 1633-1643

https://doi.org/10.1016/j.cor.2011.09.017 Get rights and content

Abstract

In assessing a network's potential performance given possible future disruptions, one must recognize the contributions of the network's inherent ability to cope with disruption via its topological and operational attributes and potential actions that can be taken in the immediate aftermath of such an event. Measurement and maximization of network resilience that accounts for both in the context of intermodal freight transport are addressed herein. That is, the problem of measuring a network's maximum resilience level and simultaneously determining the optimal set of preparedness and recovery actions necessary to achieve this level under budget and level-of-service constraints is formulated as a two-stage stochastic program. An exact methodology, employing the integer L-shaped method and Monte Carlo simulation, is proposed for its solution. Optimal allocation of a limited budget between preparedness and recovery activities is explored on an illustrative problem instance involving a network abstraction of a United States rail-based intermodal container network.

Section snippets

Introduction and motivation

Freight transportation infrastructure and related transport elements (trains, ships, planes and trucks) comprise a crucial lifeline for society. In the United States (U.S.), for example, an extensive freight transportation system, with a network of 4 million miles of roadway, nearly 140,000 miles of rail, approximately 25,000 miles of waterways, more than 350,000 intermodal terminals, almost 10,000 coastal and inland waterway facilities and over 5000 public-use airports [37], enables the

Literature review

Numerous works in the literature address network vulnerability, reliability and flexibility. These concepts are not always well defined and their meaning often varies from one work to another. It is only in rare cases, however, that consideration is given to actions that can be taken in the immediate aftermath of the disaster to improve system performance. An overview of the concepts of vulnerability, reliability, flexibility and resilience in the literature is given in [9]. Prior to [9] and

Problem definition

In this section, the problem of measuring resilience given preparedness options is defined. To the extent possible, for consistency, notation and definitions presented in [9] are used.

As in the previous work, network resilience is defined as the expected fraction of demand that can be satisfied post-disaster $α = E (\sum_{w \in W} d_{w} / \sum_{w \in W} D_{w}) = (1 / \sum_{w \in W} D_{w}) E (\sum_{w \in W} d_{w})$ where D_w is the original pre-disaster demand for O–D pair w. d_w is the post-disaster maximum demand that can be satisfied for O–D pair w. Demand that

Overview of solution methodology

The aim of the solution methodology is to determine the optimal portion of the budget to spend on preparedness and amount of the budget to save for post-disaster recovery given future network states that could result from one of the many possible disaster scenarios. The probability of each disaster scenario is assumed to be known a priori and it is possible that no such disaster scenario will be realized. The optimal investment plan will result in the maximum expected resilience index for the

Illustrative case study

To assess the impact of preparedness on resilience level, the integer L-shaped method was applied on the Double-Stack Container Network introduced in [27], [35] and considered in [9]. The solution methodology was implemented in C++ and run in the Microsoft Visual Studio C++ 2005 environment, employing IlOG's CPLEX 10.1 and the Concert Library. The computations were carried out on a personal workstation with a Pentium 4 3.20 GHz processor with 2.00 GB RAM running Windows XP Professional Edition.

Conclusions

This paper revisits the notion of resilience proposed in [9], which accounts for recovery actions that can be taken post-disaster within a limited time frame and budget. Herein, this notion is extended to include preparedness actions that can provide increased recovery capability, in addition to increased coping capacity. The concept is applied in the context of an intermodal rail application, but its relevance extends beyond transportation. The inclusion of preparedness decisions in

Acknowledgments

This work was jointly funded by the Center for Integrated Transportation Systems Management (CITSM), a Tier 1 U.S. Department of Transportation funded center and the National Science Foundation. This support is gratefully acknowledged, but implies no endorsement of the findings.

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Measuring and maximizing resilience of freight transportation networks

Abstract

Section snippets

Introduction and motivation

Literature review

Problem definition

Overview of solution methodology

Illustrative case study

Conclusions

Acknowledgments

European Journal of Operational Research

Operations Research Letters

Computers & Operations Research

Computers & Operations Research

Transportation Research Part A

Computers & Operational Research

Transportation Research Part B

Cities

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Numerische Mathematik

A framework to quantitatively assess and enhance the seismic resilience of communities

Earthquake Spectra

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Transportation Science

Monte Carlo simulation for correlated variables with marginal distributions

Journal of Hydraulic Engineering

Potential impacts of climate change on flood-induced travel disruption: a case study of Portland in Oregon, USA

Annals of the Association of American Geographers

Disaster recovery response to tropical storm Alberto

Solving stochastic transportation network protection problems using the progressive hedging-based method

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IEEE Transactions on Power Systems

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Transportation Research Record