Quantitative Methods on Resilience

Despite the increasing number of researches published on supply chain and

transportation resilience, there has been little application of quantitative modeling techniques to both topics. (Falasca, 2008) Nonetheless, we try to provide a review of the quantitative methods on resilience in this section and lay more stress on transportation resilience than supply chain resilience. We then find that there are different methods to measure the resilience.

Most quantitative researches in transportation area develop the methods to explore the network resilience. (Murray-Tuite, 2006, Dorbritz, 2011, Chen and Miller-Hooks, 2011, Nair et al., 2010 )

Murray-Tuite (2006) combines methods of multiple metrics and simulation to provide a promising approach for transportation network resilience. The contribution of this paper is to address the measurement of transportation resilience through the evaluation of four

dimensions (adaptability, mobility, safety, and the ability to recover quickly) by multiple metrics that will aid future development of a single measure of resilience. It is also the first paper to examine the impacts of traffic assignment on resilience. The simulation methodology is used to generate the user equilibrium (UE) and system optimum (SO) traffic assignments for a test network. The UE assignment presents minimizing travel time for individuals, while the SO assignment presents minimizing the travel time for all vehicles in the network. The output of the simulation is then used to evaluate four dimensions. In this study, the user equilibrium results perform better in adaptability and safety while system optimum yields better mobility and faster recovery.

Dorbritz (2011) analyzes the topological and operational disaster resilience of transportation networks. The study aims to anticipate order of large-scale failures and to suggest resilience enhancements for increasing the disaster resilience by assessing the topological and operational consequences of failures. The Swiss railway and Zurich’s

tramway network are modeled and represented. Two different aspects of the disaster resilience can be analyzed: the infrastructural and operational aspect. The infrastructural aspect is the

topological features of the “scale-free networks“. The infrastructural aspect does not consider any operational data such as line paths, track and station capacities and frequencies of the lines. The second aspect, operational resilience, will be assessed by giving above information that can present which extent and where the system performance is reduced in degraded operation. The results indicate that the topological importance of infrastructures and the operational one often do not coincide.

Both Chen & Miller-Hooks (2011) and Nair et al. (2010) propose quantitative measures for the intermodal (IM) freight transport system. The methodology used by Nair et al. (2010) is based on Chen & Miller-Hooks’ (2011) concept.

To address the need for a tool for such measurement for quantifying the vulnerability of IM freight systems, L. Chen & E. Miller-Hooks (2011) designed an indicator of network resilience that quantifies the ability of an IM freight transport network to resist and recover from disruptions due to natural or human-caused disaster. Their resilience indicator considers the network’s inherent ability to cope with the negative consequences of disruptions and accounts for the impact of potential recovery activities that might be taken in the immediate aftermath of the disruption while adhering to a fixed budget. They propose a stochastic combinatorial program for quantifying network resilience as a function of throughput that can be reached post-disaster. Solution of the program also aids in identifying the optimal

post-event action to achieve the maximum resilience level. No prior work provides such means of quantifying an IM network’s vulnerability with consideration for the recovery actions, which is critical for developing insights necessary for improving IM freight transportation security

The aim of a quantitative measure of resilience in Nair et al. (2010) is to determine the best set of actions to improve security at nodal facilities in an IM network. Resilience accounts for both the innate reliability of a facility and the ability of short-term recovery actions to mitigate negative effects. It also develops the necessary steps to apply this concept to an existing port through a case-based analysis. First, a network representation for the system with all its essential processes and stakeholders is generated. Second, the disruption scenarios are developed. The third step is the evaluation of all recovery tools at the disposal of

the facility operator. Each recovery activity is analyzed to determine the time and cost needed to execute the activity and its potential benefit to the system.

Nair et al. (2010) is the extension of Chen &Miller-Hooks (2011). It considers the application of Chen and Miller-Hooks’ concept of resilience to ports, terminals, and other nodal IM infrastructure, which are treated as simple nodes with no properties or specific structure. We can say that Chen & Miller-Hooks (2011) focus on a system-level application of their resilience concept and Nair et al. (2010) propose a framework at the IM component level.

The reason why the author considers the IM component level is that IM networks are seldom managed by one single entity. As an IM terminal operator, facility specific resilience measures can be more targeted than a network-wide measure because the former is directed at operators of facilities. The analysis result can lead to proactive decisions by operators on recovery options and reconfiguring their facilities to mitigate the effects of disruptions.

There is another paper to analyze the network resilience but it isn’t for transportation network. It studies the resilience of logistic networks for aircraft maintenance and service. (W ang & Ip, 2009) Firstly, a resilience evaluation approach is proposed based on the redundancy and distribution of supply resources for logistic networks. Then, the optimal structure of the network, with resource constraints, is studied. A model optimizes the allocation of resources with connections, distribution centers or warehouses. The research results have been provided to the decision makers of the aviation management sector

Different from the quantifying network resilience, Mansouri (2009) develops a systematic process for making strategic and investment decisions. Since cost-effectiveness plays a key role in making decisions during the process of system design and infrastructure development, evolvement of a comprehensive framework for measuring the multiple aspects of resiliency in Maritime Infrastructure and Transportation Systems (MITS) is essential for better systems-level decision-making. The framework called Risk Management-based Decision Analysis (RMDA) consists of three phases, Assessing Risks, Devising Resilience

MITS resilient in accordance of System of Systems (SoS) management techniques. In order to analyze the cost of investment alternatives in MITS, this paper suggests applying decision analysis methodologies such as DTA or the economic theory of real options in third phase.

The RMDA framework enables the decision-makers to identify, analyze, and prioritize risks and to define ways for risk mitigation, plan for contingencies, and devise mechanisms for continuously monitoring and controlling risk factors and threats to the system.

Colicchia et al. (2008) studies the resilience of the supply chain with reference to the global sourcing process, because companies are easily affected by a wider exposure to risk sources in the global sourcing context currently. The contributions on supply chain resilience mainly concerned with the outbound flow before, but this paper focus on the supply risk analysis of inbound flow. Supply lead time (SLT) variability is assumed as a proxy of the supply chain resilience in this paper. The reason is that a resilient supply chain can quickly react to the undesired events and consequently reduce the delays.

Although Colicchia et al. (2008) discuss the resilience of the supply chain, they focus on the transportation process. First, this article conducts an in-depth analysis of international supply process vulnerability on the transportation phase, and then, identifies a set of approaches for managing risk in order to enhance supply chain resilience. The approaches include the contingency plans, which provide alternative ways of transport after the disruption, and mitigation actions, which are the risk management approaches put in place beforehand.

The method it uses is a simulation-based framework for assessing the effectiveness of the proposed approaches and applies on a real case. The result is that the contingency plans have the greater effectiveness than the mitigation actions. It’s converse to the before-mentioned concept by scholars in section 2.2.

We sort out above quantitative literatures as following Table 2.4, which includes methodology, resilience level, resilience indicator and the application subject. According to the literature reviews, we know that they develop different indicators to their research subjects. .

Table 2.4 Quantitative literatures

Literature Methodology System Resilience indicator Research subject

Murray-Tuite (2006)

1. Traffic assignment simulation

2. Metrics

Road transportation network Adaptability, safety, mobility, recovery metrics

Evacuating vehicles (passenger transport)

Colicchia et al.

(2008)

Simulation Transportation process

Supply lead time variability

Materials or

Airline logistic network The percentage of its demand to the total demand

transportation system  Help to make investment

decision

The percentage of the flow that is satisfied between the O-D pair to the total demand for the O-D pair

Freight transport

Dorbritz (2011) 1. Topological analysis 2. Operational analysis

Railway and tramway transportation network and system

1. Fraction of nodes in giant cluster, average shortest path length dynamics

2. Capacity utilization

Passenger transport

Chen &

Miller-Hooks (2011)

Mathematical programming

Intermodal freight system network

The percentage of the maximum demand that can be satisfied

post-disaster to the demand that can be satisfied pre-disaster

Freight transport