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Sequencing algorithm with multiple-input genetic operators: Application to disaster resilience
Abstract
A novel evolutionary optimization methodology called ‘‘Algorithm with Multiple-Input Genetic Operators”
(AMIGO) for scheduling independent tasks considering resource and time constraints is presented. AMIGO
is characterized by new genetic operators enriched with complementary information, including auxiliary
variables computed by the fitness function, as well as the global parameters of the problem. The application
of AMIGO to multi-phase optimal resilience restoration scheduling of highway bridges is presented
and discussed. To this purpose, enhancements have been made also to the bridge network resilience analysis
(a new performance metric and restoration model). The quality of the solution and efficiency of
AMIGO are demonstrated through the application to a large transportation network subjected to
earthquake.
... However, this method is highly dependent on accurate origin-destination (OD) flow demand and route choice assumptions based on traffic equilibrium (Chang et al. 2012). During the emergency phase after a major earthquake, the travel behaviour is extremely complex as it is influenced by socioeconomic factors as well as emergency policies, and the choice of drivers may not be rational (Karamlou and Bocchini 2016). Therefore, it is almost impossible to predict the OD flow demand under such high uncertainty (Khademi et al. 2015;. ...
... However, these performance metrics only focus on the impact of network disruptions under normal traffic demand, without considering the possible changes in travel behaviour after disasters. A significant characteristic of the post-disaster emergency phase is the dramatic change of traffic demand (Karamlou and Bocchini 2016;Feng, Li, and Ellingwood 2020;Chang et al., 2010), in which the travel behaviours related to emergency rescue are the most concerned. Franchin, Lupoi, and Pinto (2006) conducted a reliability analysis of the post-earthquake functionality of the road network in terms of the accessibility between schools and hospitals, which takes into account the damage of buildings and road network. ...
... However, this is obviously not in line with reality, but very few studies have considered the issue. Karamlou and Bocchini (2016) defined the network performance metric in emergency response as weighted number of connected OD pairs that are considered urgent. In the metric, the connectivity of OD pairs is divided into three levels (1.0 for fully connected, 0.5 for partially connected, and 0 for disconnected) based on the presence or absence of a fully or partially served route between them. ...
As one of the most important urban infrastructure systems, the road network plays a vital role in post-earthquake emergency response. Quantitative evaluation of the emergency response functionality of road network under earthquakes can provide decision support for emergency planning and future construction. In this paper, a novel network performance metric is proposed to consider both the special traffic demand in emergency response and the supply capability of road network. On this basis, an integrated probabilistic assessment framework for post-earthquake network emergency response functionality is presented, considering various uncertainties. In the framework, a penalty-based approach is proposed to consider the impact of partially degraded links on network functionality. At last, the proposed methodology is illustrated on the road network of Tangshan city, China, under different seismic scenarios.
... Alternatively, the restoration sequence can be simulated with quantitative computational models. For example, the decision on restoration sequencing can be simulated as the solution of an optimization model (Bocchini & Frangopol, 2012a;Chang, 2003;Fang, Fang, Zio, & Xie, 2019;Gonz alez, Dueñas-Osorio, S anchez-Silva, & Medaglia, 2016;Karamlou & Bocchini, 2016;Miles, 2018;Zhang, Wang, & Nicholson, 2017). However, the modeling and solution of optimization problems require the implementation of complicated mathematical formulations and become computationally expensive for large and complex applications, which may render this approach inappropriate for some practitioners. ...
... Practical recovery plans may have different goals (such as minimal repair cost) and conflicting goals (such as minimal life-cycle cost and maximum resilience). The first problem can be addressed by using optimization formulations with a different objective function; the second problem can be addressed by multiple objective optimization methods, such as multi-objective genetic algorithms (Bocchini & Frangopol, 2012b;Karamlou & Bocchini, 2016) and the weighted sum method (Bueno, Haeser, & Mart ınez, 2016;Zhang et al., 2017). ...
... The functionality of an infrastructure system can be defined by different metrics, such as network connectivity (Bocchini & Frangopol, 2013), network flow (Bocchini & Frangopol, 2012b;Ma et al., 2019), and the percentage of customers with service (Mitsova, Escaleras, Sapat, Esnard, & Lamadrid, 2018;Ouyang & Dueñas-Osorio, 2014). This study uses the following metric based on a weighted network (Karamlou & Bocchini, 2016): ...
Due to continuous population expansion and the threat of climate change, the past century has witnessed increasing occurrences of natural hazards, leading to significant global losses and requiring substantial restoration efforts. This issue challenges decision makers to act in a timely and effective manner to protect infrastructure systems from future natural hazards. This study presents a policy-based decision model for restoration planning, as part of the PRAISys platform, to support informed disaster mitigation of interdependent infrastructure systems under uncertainty. Following the concept of disaster recovery priority used in practice, this model determines the priority rank of each recovery task from pre-defined policies and simulates the restoration accordingly. This model captures different types of interdependencies with rigorous models at the component and system levels and predicts possible system recoveries under a given damage scenario in a probabilistic manner. This model can quantitatively evaluate the effectiveness of decision strategies on system recovery and resilience under different disaster recovery policies. As a demonstration example, this study applies the proposed model to the post-earthquake recovery simulation of three interdependent infrastructure systems (i.e., power, communication, and transportation) in the Lehigh Valley, Pennsylvania, USA. A total of sixteen cases were considered to represent different restoration strategies. For every case, the uncertainties in the recovery steps are captured by probabilistic simulation, and system resilience is calculated for every recovery sample. Simulation results from different strategies are compared to evaluate the effectiveness of non-intuitive strategies on system recovery and resilience. The proposed model uses a simple and straightforward concept to mimic practical disaster recovery plans. It is easy to understand and implement for modelers, and it is also useful to compare outcomes from different recovery criteria and decision strategies for practitioners.
... Under a hazard scenario, both nodes and links are subject to failure, with binary functionality (ATC 1985;Guidotti et al. 2017;Sun et al. 2020b), continuous functionality (Karamlou and Bocchini 2017b;Thurner et al. 2018), or discrete functionality (Shinozuka et al. 2003;Bocchini and Frangopol 2011a;Karamlou and Bocchini 2017a). The system functionality is usually defined based on the network topology or network flow, such as connectivity (Dueñas-Osorio and Vemuru 2009; Bocchini and Frangopol 2011b), number of functional/failed/repaired components (Johansson and Hassel 2010;Karamlou and Bocchini 2016), flow capacity Frangopol 2011b, 2012a, b), number of customers with service (Mitsova et al. 2018;Sun et al. 2020b, c), and network flow (Lee et al. 2007;Ma et al. 2019). In addition to physical dependencies, the link concept can be generalized to describe other types of dependencies across systems, and a joint adjacency matrix can be used to describe both physical connectivity and other dependencies. ...
... Functionality dependencies, including both compositional functionality dependencies and intersystem functionality dependencies, can be represented by rigorous restoration functions (Karamlou and Bocchini 2017a;Sun et al. 2019Sun et al. , 2020bLiu et al. 2021). In applications of dependencies and interdependencies, optimization models are often integrated with other models, such as network models (Karamlou and Bocchini 2016;Ouyang 2017;Zlotnik et al. 2017;Almoghathawi et al. 2019;Ma et al. 2019;Karakoc et al. 2019) and agent-based models (Permann 2007;Kizhakkedath et al. 2013). For instance, optimization models have been applied to identifying effective recovery decisions on network resilience enhancement (Vugrin et al. 2014;Ouyang and Wang 2015;Zhang et al. 2018;Sun et al. 2020b). ...
Critical infrastructure systems are interdependent to ensure normal operations for supporting a national economy and social well-being. In the wake of a disaster, such interdependencies may introduce additional vulnerability and cause cascading failures. Therefore, understanding interdependencies and assessing their impact are essential to mitigate such adverse consequences and to enhance disaster resilience in the long term. There have been various models developed to capture dependencies and interdependencies across infrastructure systems. However, problems of inconsistent usage and a lack of technical guidance hinder practical applications of interdependency models. Therefore, this study presents a new classification of interdependency models based on the following implementation methods: dependency tables, interaction rules, and data-driven approaches. For every class of interdependency model, fundamental assumptions and detailed implementation methods are described, with discussion of appropriate application areas, advantages, and limitations. This study also compares different types of models to facilitate analysts in choosing models based on their needs. Due to the intrinsic complexity of dependencies and interdependencies, there are many challenging modeling issues; this study discusses future research directions to address such challenges.
... Appropriate traffic assignment and distribution techniques are adopted to handle traffic demand in the system before and after the seismic event. Adopting the same methodology and concept, Karamlou and Bocchini (2016) evaluated resilience of a real highway network in San Diago, CA that constitutes 80 bridges. The primary objective of that study was to develop a robust optimisation algorithm named Algorithm with Multiple-Input Genetic Operators, which was further applied to identify resilience-based optimal postearthquake restoration interventions of damaged highway bridges. ...
... It is assumed that the time taken for restoration has linear dependency on the damage level of bridges. Other studies related to seismic resilience of bridge networks used the recovery model proposed in HAZUS (HAZUS-MH 2004) with or without case-wise customisations (Alipour & Shafei, 2016;Karamlou & Bocchini, 2016). Readers can also refer to Gidaris et al. (2017) for a discussion on available restoration models of highway bridges for the purpose of resilience assessment under earthquake and tsunami hazards. ...
Assessment of resilience for engineered systems has drawn ample attention from the engineering community in recent years. It has resulted in a significantly large body of literature focusing on pertinent areas of resilience. This article provides a systematic and comprehensive review of the literature addressing resilience assessment of bridges and bridge networks under single hazard and multihazard conditions. Though not much work been performed yet on multihazard resilience of bridges, relevant aspects including combinations of multiple hazards for bridge performance evaluation, methods for loss assessment and approaches taken for post-event recovery are discussed. Furthermore, maintenance is a key component if resilience is assessed in a life-cycle framework. Hence, available maintenance plans and strategies and their probable applications for bridges and bridge networks are discussed. The article concludes with a discussion on the need for further research in the focus area and challenges involved with the same. ARTICLE HISTORY
... Appropriate traffic assignment and distribution techniques are adopted to handle traffic demand in the system before and after the seismic event. Adopting the same methodology and concept, Karamlou and Bocchini (2016) evaluated resilience of a real highway network in San Diago, CA that constitutes 80 bridges. The primary objective of that study was to develop a robust optimisation algorithm named Algorithm with Multiple-Input Genetic Operators, which was further applied to identify resilience-based optimal postearthquake restoration interventions of damaged highway bridges. ...
... It is assumed that the time taken for restoration has linear dependency on the damage level of bridges. Other studies related to seismic resilience of bridge networks used the recovery model proposed in HAZUS (HAZUS-MH 2004) with or without case-wise customisations (Alipour & Shafei, 2016;Karamlou & Bocchini, 2016). Readers can also refer to Gidaris et al. (2017) for a discussion on available restoration models of highway bridges for the purpose of resilience assessment under earthquake and tsunami hazards. ...
Assessment of resilience for engineered systems has drawn ample attention from the engineering community in recent years. It has resulted in a significantly large body of literature focusing on pertinent areas of resilience. This article provides a systematic and comprehensive review of the literature addressing resilience assessment of bridges and bridge networks under single hazard and multihazard conditions. Though not much work been performed yet on multihazard resilience of bridges, relevant aspects including combinations of multiple hazards for bridge performance evaluation, methods for loss assessment and approaches taken for post-event recovery are discussed. Furthermore, maintenance is a key component if resilience is assessed in a life-cycle framework. Hence, available maintenance plans and strategies and their probable applications for bridges and bridge networks are discussed. The article concludes with a discussion on the need for further research in the focus area and challenges involved with the same.
... Post-disaster decision-making on recovery management is one of the most promising fields for the application of resilience in engineering practice [23] . Frangopol and Bocchini proposed an optimization model for the post-disaster restoration schedule of transportation networks with respect to the total present cost, using both the total travel time (TTT) and total travel distance (TTD) as network performance metrics [24] . In addition to long-term resilience indicators, Karamlou and Bocchini introduced a connectivity-based resilience indicator to optimize the restoration priorities during medium-phase disaster management [25] . ...
The road transportation infrastructure system (RTIS) provides a network of options that support the movement of people and goods. As a critical lifeline system, the resilience assessment of RTISs under the impact of different natural hazards, particularly earthquakes, has attracted extensive attention. When an earthquake occurs, an assessment of the connectivity reliability and travel time on road networks is necessary for emergency planning. In this study, the road network in the Aba Autonomous Prefecture, Sichuan Province, China, was considered as the study area and divided into 13 traffic analysis zones (TAZs) based on the administrative divisions. To consider the uncertainties related to seismic hazard assessment, random fields of ground motions were generated using a Monte Carlo simulation (MCS), considering the spatial correlation. Additionally, a connectivity reliability assessment model and travel time assessment model for the road networks were proposed. The connectivity reliability between the TAZs and increased travel time on the road networks after an earthquake were evaluated using MCS to evaluate the uncertainties related to the damage state assessment of road assets, such as bridges, tunnels, and road segments. Consequently, the results can be used as a theoretical basis for decision-making on the location and number of emergency rescue points after an earthquake and as a functional metric for resilience assessment models.
... The second way is based on contemporary computational and mathematical techniques and capabilities that allow to simulate and optimize the recovery process. In this case, the recovery process is viewed as a set of actions that must be carried out by a group of agents and stakeholders, and the corresponding restoration plan is generated by matching actions and agents using linear programming [3] or more complex techniques like a genetic algorithm [4,5,6]. An optimization approach's restriction is the requirement for correct information or at least the proper estimation [7] of the status of the damaged system and the evaluated state to which the city must be restored. ...
Present work is devoted to the problem of emergency response to a large-scale disaster and the recovery management of an urban socio-technical system. An iterative algorithm based on reactive decision-making is proposed as an approach enabling the recovery manager to test a set of potential restoration priorities using numerical simulation and agent-based modeling. The proposed method helps to coordinate reconstruction processes even in the case when the information is fluid and uncertain via providing a set of different recovery plans, the efficiency of which may be assessed using various factors. It also enables to determine how an under- or over-evaluation of the damage degree will affect the integral duration of the recovery process. As a result, the recovery manager will be supplied with a collection of possible recovery plans, which are numerically tested, visualized, and whose relative efficiency is evaluated using multiple estimators. Such an outcome enables to avoid the delay of recovery initiation caused by the ambiguity of the initial state of the system and the impossibility to define the recovery strategy right after a disaster occurrence.
... In the post-earthquake chaotic environment, the transportation network is considered as one of the most significant infrastructure systems to support emergency response activities, and the disruption of transportation networks could lead to additional casualties, economic losses and secondary disasters [43,[68][69][70][71]. Maintaining effective connectivity between affected areas and the emergency facilities has always been recognized as the immediate priority following a major earthquake [40,[72][73][74]. In this paper, we utilize an improved connectivity-based model to evaluate the post-earthquake network functionality of emergency response [75]. ...
Seismic mitigation of transportation systems has become a worldwide challenge, because identifying an optimal retrofit strategy entails significant computational efforts, especially for large-scale networks with numerous candidate strategies and time-consuming risk assessment processes. An efficient joint importance-based methodology is proposed in this paper to address the challenge. The proposed method selects the component set (e.g., bridges) that is most decisive to the network seismic risk based on only one set of stochastic samples but takes into account the uncertainty of multiple damage states and the interactive effect between different components. The reliability and stability of the proposed method are verified on a hypothetical transportation network under different conditions.
... In other cases, nodes and links may have continuous or discrete functionality states, ranging between 0 and 100% (Bocchini and Frangopol 2011;Cimellaro et al. 2015;Guidotti et al. 2016;Manzo et al. 2012;Shinozuka et al. 2003aShinozuka et al. , 2003b. The system functionality is usually defined based on the network topology and/or network flow, in terms of connectivity loss (Dueñas-Osorio et al. 2007;Bocchini and Frangopol 2013), number of functional or failed or repaired components (Di Muro et al. 2016;Johansson and Hassel 2010;Karamlou et al. 2016), the reciprocal shortest path length (Ouyang et al. 2009), flow capacity (Bocchini and Frangopol 2011a, 2012a, 2012b, or number of customers with or without service (Poljanšek et al. 2012). In addition, network flow can be used as the functionality metric to assess the impact of interdependencies on the network functionality by considering the nodal capacity (Lee II et al. 2007;Wallace et al. 2003). ...
Critical infrastructure systems provide essential services for economic prosperity and a good quality of life. Over time, they have become increasingly interdependent on each other at multiple levels. Under ordinary conditions, these interconnections enhance the overall performance of the infrastructure and can accelerate the transition to “smart cities”. However, in the case of extreme events, interdependencies introduce additional vulnerabilities that can lead to cascading failures and delays in restoration activities. Consequently, better understanding of the interdependencies among critical infrastructure systems and an objective way to model them are important to support planning, design, maintenance, and emergency decision-making, especially if disaster resilience is the ultimate focus. Since resilience is a systemic property, any model that addresses the various infrastructure sectors independently is likely to yield inaccurate predictions. This chapter provides a comprehensive review of available methods to model infrastructure interdependencies for researchers, practitioners, and administrators who are interested in the quantification, prediction, and enhancement of disaster resilience. Current implementations of dependency and interdependency are mainly realized through dependency tables and interaction rules. Within every major implementation category, detailed classifications of modeling methods are described. For each classification, we discuss fundamental assumptions, computational implementation, most suitable areas of application, advantages and limitations. We then review how various computational frameworks and tools implement different interdependency models for community resilience assessments, followed by future directions for research and practice.
... Similarly, traffic-carrying capacity for bridge system functionality analysis has been presented in many research works(Decò et al., 2013;Dong & Frangopol, 2016;A. Guo et al., 2017;Karamlou & Bocchini, 2016; functionality relationship, the traffic flow functionality model associated with the considered damage states can be expressed through the following form ], ±*) is the post-event time-dependent average daily traffic (ADT) of the link that is usually expressed in discrete-valued tables in literature, variable ranges from 0 to 1, and § : is bridge ADT at the full functionality, and XL ./ is the integrated damage index from Equation (2.15).For example,Shinozuka et al. (2008) assumed 100%, 25%, and 10% link traffic throughput for minor, moderate, and collapse damage states, respectively(Shinozuka et al., 2008). ...
Bridge structures are required to possess high reliability and robustness against the concurrent effect of extreme loads and environmental attacks. To achieve such interrelated goals, it is necessary to assess the system performance and resilience subjected to multi-hazard impacts and the beneficial effects of any retrofitting or hazard-countermeasure in a lifecycle context. The damaged bridge needs to be restored rapidly over its service life due to the significant economic losses and disruption to transportation networks. For river-crossing bridges, one of the essential hazard mitigation strategies is scour countermeasures. However, a quantitative understanding of the effects of SCs on bridge system resilience is not found.
This dissertation presents a critical synthesis of the existing literature that provides relevant knowledge and a profound understanding of probabilistic multi-hazard assessment for bridge structures. Then, a finite element-based probabilistic framework is designed to assess the lifecycle resilience of reinforced concrete river-crossing bridges under seismic, flood-induced scour, and chloride-induced corrosion impacts, including the consideration of a typical scour countermeasure at variable service times. Based on the general performance-based approach, two probabilistic models are formulated, termed the mean-scour fragility analysis (MS-FA) model and the total-scour demand hazard analysis (TS-DHA) model, which produce straightforward functional curves and can be readily used to evaluate the seismic-scour multi-hazard effects. An integrated damage index is defined based on both local and system-level ductility demands to develop a demand hazard model and to estimate the damage-based residual functionality and recovery duration to quantify the lifecycle bridge resilience.
Notably, the exceeding probability approach is designed to reveal how progressive and sudden hazards interact and result in resilience degradation and how scour countermeasures contribute to resilience enhancement. The outcomes of the numerical experiment reveal the positive and distinct effects of implementing SCs at different lifecycle intervals. More importantly, resilience time-series demonstrate arbitrary multi-modes and nonparametric patterns. Accordingly, a robust statistical distance-based approach is presented to determine the sequential evolution of time-varying multi-hazard resilience. The proposed framework would assist stakeholders and decision-makers in resilience patterns recognition, assessing the effectiveness of hazard mitigation strategies, and taking short- and long-term proactive intervention actions by specifying resilience thresholds.
Here is the link to download the full-text: https://mospace.umsystem.edu/xmlui/handle/10355/78906
... In addition to the long-term resilience indicator, Karamlou and Bocchini (2016) introduced a connectivity-based resilience indicator to optimise the restoration priorities during mediumphase disaster management. Zhang, Wang, and Nicholson (2017) presented a framework to optimise the restoration schedule for post-earthquake road network based on the total recovery time (TRT) and skew of recovery trajectory (SRT), where the network performance is measured by weighted average number of reliable interdependent routes over all origindestination (OD) pairs. ...
Transportation network is of vital importance for city maintaining and reconstruction especially after extreme events. Decisions on the restoration plan of transportation network have to be made in a short period after event, but taking into account various decision-making criteria and resource constraints. This paper explains the essential problems in network restoration scheduling, and presents a novel resilience-based optimisation model for post-disaster urban transportation network restoration. The proposed method could obtain a set of general optimal restoration schedules in an efficient manner, so that decision-makers could make a final choice by weighting other preferences and experience. In the methodology, a new travel speed-based metric is introduced for performance assessment of transportation network, and three normalised independent indicators are developed to characterise network resilience from the perspective of functionality curve. Furthermore, a bi-objective optimisation model based on the recovery trajectory is recommended to search for non-dominated optimal restoration schedules. To illustrate the proposed method, heuristic algorithms are used to solve the restoration schedule optimisation problem of a hypothetical transportation network.
... The European project SYNER-G provided a computation framework for seismic vulnerability and risk analysis of interdependent civil infrastructure systems, including social losses as a result of direct physical damage to facilities and their lack of functionality (Franchin, 2014;Pitilakis et al., 2014). The National Science Foundation funded project PRAISys focuses on using mathematical techniques to address stochastic interdependencies among infrastructure systems after an extreme event in a probabilistic way (Karamlou & Bocchini, 2016a, 2016b. The NIST funded Center for Risk-Based Community Resilience Planning provides a quantitative and science-based approach to comprehensively assess community resilience by narrowing the gap between engineering, social sciences, and economics (Ellingwood et al., 2016). ...
In a modern society, civil infrastructure facilities in different systems depend upon each other for product input and/or information sharing. Thus, understanding such dependencies and properly modeling them is essential to assess the performance of the civil infrastructure systems during and following a disruptive event, which lays the foundation of assessing the community resilience. This paper introduces the Dynamic Integrated Network model which is developed to assess the damage and recovery of the network of all considered systems following a disaster by considering the facility-level dependencies. A hypothetical study region consisting of electric power, potable water and cellular systems under a scenario hurricane is used to demonstrate the proposed model. A comparative study is performed with the case when inter-system dependency is ignored. This study confirms the importance of incorporating the inter-system dependencies to improve the estimation on the recovery process.
... Also, the concept of resilience triangle proposed by Bruneau et al. is one of the most common tools to quantify resilience, and the underlying idea inspired several researchers to propose other metrics and methodologies to measure, enhance, and optimize resilience of different structures and infrastructure systems [6][7][8][9]. These include, but are not limited to, healthcare structures and facilities [10,11], power grids [12,13], gas distribution systems [14], bridges and transportation networks [15][16][17]. As indicated in the definition of resilience, the assessment of the restoration process of different units of the community and their functionality immediately after the event and during the recovery phase are among the key steps of resilience assessment. ...
To reduce the negative impacts of indirect losses after an extreme event, typically caused by long-lasting reduced functionality of critical structures and infrastructure systems, disaster managers need to plan the disaster relief activities considering the post-event resilience as one of the main determining factors.
However, this requires better understanding of the restoration process of the system and the underlying uncertainties, as well as the impact of each component on the functionality of the system. In this paper, a technique is presented to simulate the restoration schedule and recovery curve of damaged infrastructure systems with multiple components. This is carried out by assembling the restoration tasks of each damaged component of the system considering the availability of resources for the restoration activities, and the uncertainties involved in restoration duration. The application of the proposed methodology is presented for two cases: an electrical distribution substation, and a pair of bridges, all with multiple damaged components. Sample restoration schedules and recovery curves are presented, and probabilistic restoration functions are derived for every structure.
... The concept of resilience has been studied in a large number of fields such as engineering, psychology, sociology, ecology, business, and economics. In the engineering world, resilience has been generally defined as the ability and capacity of a system or social units to absorb, withstand, and efficiently recover from a perturbation to an acceptable level of functioning [1]. It is a characteristic of the system that indicates performance under unusual conditions, recovery speed, and the amount of outside assistance required for restoration to its original functional state [2]. ...
Effective integration of resilience in transportation planning requires the identification and prioritization of multifaceted resilience factors. In this study, an extensive review of the academic literature and state planning documents was conducted to identify measurable technical, socioeconomic, and environmental resilience factors for wind- and water-related hazards. Transportation experts across the state of Florida were then surveyed to gain insight into the significance, relevance, and comparability of the identified factors. By statistically analyzing the survey responses, the resilience factors were prioritized to allow transportation agencies to optimize their resource allocation toward critical resilience aspects and effectively improve resilience within their limited budgets. Overall, the age of infrastructure, emergency response, and network exposure were identified as the top technical, socioeconomic, and environmental factors, respectively. This study also presents five key recommendations, informed by insights from a workshop involving transportation practitioners throughout Florida, to overcome current barriers to incorporating resilience in transportation planning. These recommendations cover the topics of proper communication, funding availability and public engagement, plans alignment, data collection processes, and the development of comprehensive resilience assessment frameworks. This study contributes to the body of knowledge and state of practice by providing valuable insights and actionable strategies to facilitate the practical integration of resilience considerations in transportation planning, ultimately assisting in the overall resilience enhancement of transportation systems.
Recent natural or manmade hazards highlighted the pressing need for allocating enough financial resources for the enhancement of the resilience of infrastructure in our society. In this regard, bridges play a pivotal role in transportation networks, where any reduction in their performance can influence the functionality of the whole network. Bridge retrofitting is a common strategy for enhancing transportation network resilience. Composite reinforcement with different thicknesses and concrete seat extenders with different widths have been used to retrofit an existing curved bridge in Iran. A novel hybrid algorithm called NSGAII-SA was proposed and compared against a simple optimisation strategy, a traditional genetic algorithm solver (NSGA-II), and pure simulated annealing (SA). These optimisation strategies identify the optimal retrofit scenarios set in terms of maximum resilience index, minimum recovery time, and minimum retrofitting cost. For this problem, NSGAII-SA outperforms the traditional NSGA-II. The comparison shows that for this particular application, SA has the best overall performance. However, the proposed NSGAII-SA algorithm has satisfactory performance too, it is a clear improvement over NSGAII and it has the potential to be very useful and better suited than SA for problems with large design spaces and many local minima.
The resilience of a bridge is computed using different quantitative and qualitative assessment methodologies. However, the resilience score obtained by these assessment approaches is insufficient for the decision-makers for setting a priority level for bridges in need of resilience improvement. To address this issue, the present study develops a methodology using the data envelopment analysis (DEA) approach. A total of 12 bridges are selected as the decision-making units in the DEA model. This study considers the variables such as age, area, design high flood level, and finish road level of the bridge as inputs, and bridge resilience index as the output variable. Based on these variables, three frameworks are developed to compute the efficiency of bridge resilience. A variable return to scale with the output-oriented formulation of DEA is selected to compute the efficiency of bridge resilience in all three frameworks. Thus, the proposed methodology enables bridge owners to set a priority level for bridges in need of resilience improvement based on the scores of the assessment methodology.
Natural and man-made disasters can disrupt road networks. The post-disaster period is usually divided into short and long-term response phases. In the short-term phase, blocked roads can jeopardize emergency operations such as search-and-rescue, evacuation of victims, and the distribution of emergency supplies. The detritus may pose an environmental challenge in the long-term, as the rubble must be properly collected, disposed of, and recycled. Optimization models designed to schedule repair and restoration activities and determine the repair crews' routes are helpful to give insights to decision-makers on how to improve emergency response plans. This article presents a literature review on short and long-term network models designed for roads repair and restoration. This review identifies the trends in publications, solution approaches, frequency of locations and type of disaster in the case studies, and the size of the networks. In addition, a qualitative analysis of the models’ characteristics, such as the type of network, uncertainties, and interdependencies, is presented. A discussion is provided to indicate future research directions. Among these, future research should focus on the combination of roads repair and restoration with other emergency activities such as relief distribution.
Coastal transportation systems are extremely vulnerable due to the coupled impacts of storm surge, waves, and inundation. Existing literature has developed coastal fragility models for bridges. However, to date, flood fragility models for coastal roadways are lacking. For this purpose, the current study proposes a data-driven fragility model based on logistic regression for coastal roadways, with failure probability conditioned on distance to shoreline and inundation duration, using hindcast data for Hurricane Ike. In addition, the effect of bridge and roadway damage on transportation network performance is investigated through a case study on Galveston Island, Texas. The results indicate the spatial distribution of storm impacts on the transportation network, with select roads highly vulnerable if they are located within a couple of hundred meters of the shoreline. In addition, considering roadway damage in addition to bridge damage alone, which is the current state of the art, can have a significant impact on decreasing the performance of the transportation network. Such analyses shed light on potential policy or risk mitigation practices that are expected to be increasingly important in the future as sea level rise further reduces roadway distance to the shoreline or as storm intensity and frequency changes.
River-crossing bridges are often subject to multiple hazards, including foundation scour, seismic attacks, and environmental degradation. When river-crossing bridges are in service, they should be restored rapidly after any disruption over their lifetime. To achieve this resilient quality, it is necessary to assess the system resilience subjected to multihazard impacts and the beneficial effects of any retrofitting or hazard-countermeasure in a lifetime context. To river-crossing bridges, one important intervention is to implement scour countermeasures. This study presents a probabilistic framework to quantify the lifetime system resilience of river-crossing bridges subject to multiple hazards. Notably, the framework is designed to reveal how progressive and abrupt hazards interact and result in resilience degradation and how scour countermeasures contribute to resilience enhancement. Experimental outcomes reveal the positive and distinct effects of implementing scour countermeasures at different times. The proposed framework is expected to assist civil engineers in conducting lifecycle management of river-crossing bridges that are subject to hydraulic scour and demand timely countermeasures.
Infrastructure interdependencies have been widely recognized, especially in the postdisaster restoration process. It is essential to develop models to simulate interdependencies and quantify their impact on the functionality recovery of infrastructures. This study presents a generalized simulator to investigate the impact of different types of interdependency on functionality recovery. The proposed simulator considers that there are multiple possible modes to execute a restoration task by framing the restoration process of interconnected systems as a multimode resource-constrained project scheduling problem (MRCPSP). In addition, it considers three sets of uncertainties: restoration duration and resource demand to execute a task, as well as intersystem functionality dependency. By solving the MRCPSP with the objective of minimal restoration completion time, the optimal restoration schedules for different systems are calculated to predict functionality recovery. This simulator implements three types of interdependencies at both the component level and the system level: resource-sharing interdependency, restoration precedence dependency, and functionality dependency. Through a simple example, it is demonstrated how the proposed approach can quantitatively evaluate the impact on system recovery due to different types of interdependency. Research findings from this study can help to identify the interdependencies with the strongest impact and then develop preventive mitigation actions and effective plans of emergency response and disaster recovery for interconnected systems.
Natural hazards have the potential to cause catastrophic damage and significant socioeconomic loss. The actual damage and loss observed in the recent decades has shown an increasing trend. As a result, disaster managers need to take a growing responsibility to proactively protect their communities by developing efficient management strategies. A number of research studies apply artificial intelligence (AI) techniques to process disaster-related data for supporting informed disaster management. This study provides an overview of current applications of AI in disaster management during its four phases: mitigation, preparedness, response, and recovery. It presents example applications of different AI techniques and their benefits for supporting disaster management at different phases, as well as some practical AI-based decision support tools. We find that the majority of AI applications focus on the disaster response phase. This study also identifies challenges to inspire the professional community to advance AI techniques for addressing them in future research.
The transportation infrastructure plays an important role in supporting the national economy and ensuring the well-being of its citizenry. Extreme events (including both natural hazards and man-made disasters) have caused terrible physical damages to the transportation infrastructure, long-term socioeconomic impacts, and psychological damages. There is an increasing number of studies focusing on the resilience analysis of the transportation infrastructure to support planning and design and to optimize emergency management and restoration schedules. We provide a comprehensive review of recent literature on the metrics and methods for resilience analyses of the transportation infrastructure under extreme events. The paper covers functionality metrics, functionality-based resilience metrics and socioeconomic resilience metrics. Despite the advancements of research in resilience, there are still fundamental challenges to comprehensively evaluate the resilience of the transportation infrastructure, especially due to two main sources of complexity: uncertainties and interdependencies, which are discussed in the paper. We also point out that validations of resilience assessments are limited due to the general scarcity of data, which may hinder the practical applications. Finally, directions for future research in this field are suggested. The paper provides an organized overview of the many lines of research in the field, accomplishments, and open gaps. It indicates useful starting points for researchers new to this field, and serves as a reference for teams already active on this topic.
The paper presents a methodology for the selection of an optimal set of stochastic intensity measure (IM) maps representing the regional hazard over a geographic area, which can subsequently be used for the analysis of spatially distributed infrastructure systems. A key characteristic of the proposed approach, named Hazard Quantization (HQ), is that it embraces the nature of regional IM maps as two-dimensional (2D) random fields, and therefore uses a methodology for the optimal representation of non-Gaussian and nonhomogeneous random fields with a limited number of samples. In HQ, the representation of the regional hazard is supported by proofs of optimality. In particular, HQ ensures mean-square convergence of the ensemble of representative IM maps to the complete portfolio of possible hazard events, which is a particularly important property for risk analysis. HQ does not require the use of specialized simulation techniques, such as importance sampling or hierarchical sampling of the involved parameters, making the method simple to use. Other desirable characteristics make the method robust and applicable to a variety of hazard sources. In this paper, HQ is demonstrated for the regional seismic hazard analysis of the Charleston, South Carolina, region. A small set of IM maps and their associated probabilities resulting from the application of HQ are evaluated at all points and all pairs of points, on their ability to correctly represent the hazard curve and autocorrelation. Finally, a detailed comparison of the proposed technique with other popular methodologies in the same field is presented.
In order to realize the optimization of ladle tracking of aluminium tapping, a mathematical model, which takes the grade of aluminium, the energy required for transportation and the optimum ratio of aluminium liquid into account, is established. The traditional method optimizes the impurity content and the transport distance based on a single objective optimization, but it requires the empirical values of the weight coefficients. The paper proposes a modified multi-objective optimization model with the elitist non-dominated sorting genetic algorithm (NSGA-II). In the crossover operator process and the mutation operator process, the separately improved methods are introduced based on the ladle tracking problem of aluminium tapping, which replaces the Simulated Binary Crossover (SBX) in the original NSGA-II algorithm into Partially Matched Crossover (PMX) based on natural number coding and uses exchange mutation (EM) operator. Finally, the practical production data of the aluminium factory is used to verify the validity and practicability of the method, and the results show that this method can obtain a feasible solution for the user to choose suitable solutions, and avoid the defects of selecting empirical weighting coefficient.
Disaster resilience and serviceability of transportation networks are highly influenced by the post-event state and functionality of their bridges. However, the evaluation of the functionality of bridges in the aftermath of extreme events and particularly during the restoration process has not been subjected to extensive studies in the past, due to several difficulties, such as the uncertainties involved in the restoration and decision-making processes. To this end, this paper presents a new framework to estimate probabilistic restoration functions for damaged bridges. The restoration schedule of the bridge is obtained through a mathematical optimization, given the restoration tasks of its components, as well as a number of logical practical construction considerations and logistic constraints. The residual functionality of the bridge is obtained from the restoration schedule by taking into account a number of safety-and construction-induced traffic disruptions during the repair process. A simulation-based technique is proposed to compute the probabilistic characteristics of the functionality. The application of the proposed methodology is presented for the case of a multispan simply supported (MSSS) steel girder bridge numerically analyzed for a seismic event scenario.
Transportation networks are necessary infrastructure elements to provide aid to impacted areas after the occurrence of an extreme event. Without functional roads, recovering other damaged facilities and lifelines would be slow and difficult. Therefore, restoring the damages of transportation networks, specifically bridges as their most vulnerable elements, is among the first priorities of disaster management officials. This paper presents a new methodology for scheduling the restoration of damaged bridges. The problem is formulated as a multi-objective combinatorial optimization solved by Genetic Algorithms, which minimizes the time to connect the selected critical locations and maximizes the resilience of the transportation network. The main purpose of developing the algorithm was to provide a restoration plan which is practical to be used by decision makers at the time of an event, yet based on solid computations rather than mere engineering judgment. The algorithm is examined with a numerical example. The presented algorithm can be considered as the enhancement of previous work performed at Lehigh University. The results show that the new optimization setup improved the solution quality and efficiency compared to the previous techniques.
This paper focuses on the planning of road restoration efforts during disaster response and recovery. The primary objective is to maximize network accessibility for all locations in the area during the restoration process so that survivors are evacuated and road side debris is removed as soon as possible. We propose a dynamic path based mathematical model that identifies criticality of blockages and clears them with limited resources. This model is more efficient than link based models and can solve restoration problems for realistic size networks within reasonable time. Algorithm performance is demonstrated using two instances based on districts in Istanbul.
This paper presents a comprehensive model to quantify disaster resilience of systems that is defined as the capability to sustain functionality and recover from losses generated by extreme events. The model combines loss estimation and recovery models and can be applied to critical facilities (e.g. hospitals, military buildings, etc.), as well as utility lifelines (e.g. electric power systems, transportation networks, water systems etc.) that are crucial to the response of recovery processes, decisions and policies. Current research trend leads toward the definition of complex recovery models that are able to describe the process over time and the spatial definition of recovery (e.g. meta-models for the case of health care facilities). The model has been applied to a network of hospitals in Memphis, Tennessee. The resilience framework can be used as a decision support tool to increase the resilience index of systems, such as health care facilities, and reduce disaster vulnerability and consequences.
In this paper, research on the resource-constrained project scheduling problem is classified according to specified objectives and constraints. Each classified area is extensively surveyed, and special emphasis is given to trends in recent research. Specific papers involving nonrenewable resource constraints and time/cost-based objectives are discussed in detail because they present models that are close representations of real-world problems. The difficulty of solving such complex models by optimization techniques is noted. For the purposes of this survey, a set of 78 optimally solved test problems from the literature and a second set of 110 benchmark problems have been subjected to analysis with some well-known dispatching rules and a scheduling algorithm that consists of a decision-making process utilizing the problem constraints as a base of selection. The computational results are reported and discussed in the text. Constructive scheduling algorithms that are directly based on the problem constraints and whose performances are independent of problem characteristics are identified as a promising area for future research.
Resiliency can be defined as the ability of the system to bounce back after a shock and return to its normal value delivery levels. In maritime transportation systems (MTS), manmade and natural disruptions reduce the capacity of ports to send and receive goods, which can result in significant negative socio-economic impacts. Implementing resiliency in these systems improves their ability to cope with disruptions hence minimising losses. This paper proposes several schemes that improve resiliency by reducing the system's vulnerability and increasing its adaptive capacity. The impact of the schemes on the system's resiliency metrics are evaluated by applying the methodology of the Networked Infrastructure Resiliency Assessment framework. The framework consists of three stages in which a network model is extracted from the physical network, the resiliency metrics are identified and the system is modelled using network optimisation techniques and a system dynamics model. The disruptions are modelled by reducing the capacity of a port to send and receive goods. Three MTS resiliency metrics are identified, namely tonnage resiliency, time resiliency and cost resiliency. The presented case study assesses resiliency schemes, such as Diversity, Collaboration and Resource Allocation on the three resiliency metrics.
SUMMARY This paper presents a conceptual framework to define seismic resilience of communities and quantitative measures of resilience that can be useful for a coordinated research effort focusing on enhancing this resilience. This framework relies on the complementary measures of resilience: "Reduced failure probabilities," "Reduced consequences from failures," and "Reduced time to recovery." The framework also includes quantitative measures of the "ends" of robustness and rapidity, and the "means" of resourcefulness and redundancy, and integrates those measures into the four dimensions of community resilience — technical, organizational, social and economic.
One of the major challenges for researchers in the field of management science, information systems, business informatics, and computer science is to develop methods and tools that help organizations, such as companies or public institutions, to fulfill their tasks efficiently. However, during the last decade, the dynamics and size of tasks organizations are faced with has changed. Firstly, production and service processes must be reorganized in shorter time intervals and adapted dynamically to the varying demands of markets and customers. Although there is continuous change, organizations must ensure that the efficiency of their processes remains high. Therefore, optimization techniques are necessary that help organizations to reorganize themselves, to increase the performance of their processes, and to stay efficient. Secondly, with increasing organization size the complexity of problems in the context of production or service processes also increases. As a result, standard, traditional, optimization techniques are often not able to solve these problems of increased complexity with justifiable effort in an acceptable time period. Therefore, to overcome these problems, and to develop systems that solve these complex problems, researchers proposed using genetic and evolutionary algorithms (GEAs). Using these nature-inspired search methods it is possible to overcome some limitations of traditional optimization methods, and to increase the number of solvable problems. The application of GEAs to many optimization problems in organizations often results in good performance and high quality solutions.
Transportation networks are critical civil infrastructures susceptible to natural hazards such as earthquakes. It is essential to understand and model the travel demand in emergency situation when considering measures to secure traffic function immediately after the earthquake and to restore the performance of transportation networks. Post-earthquake travel demand is complicated, and the changes of traffic pattern after the event are often coupled with the physical damage of transportation infrastructures. This study develops a scenario-based methodology to model the performance of post-earthquake transportation systems by extending the combined trip distribution and assignment model. Several general assumptions are made on post-earthquake travel behaviour and emergency traffic management measures. The proposed methodology is demonstrated with the Sioux-Falls road network and results are presented. The findings suggest that such scenario-based integrated methodology can capture the post-earthquake travel characteristics and is capable to provide more reasonable results than conventional transportation modelling approaches. The methodology can be used to model the emergency demand and post-earthquake performance of transportation systems. It is helpful to aid traffic planners and emergency managers in evaluating their contingency traffic plans and making decisions in emergency management.
In this paper, we outline a method to characterize the behavior of networked infrastructure for natural hazard events such as hurricanes and earthquakes. Our method includes resilience and interdependency measures. Because most urban infrastructure systems rely on electric power to function properly, we focus on the contribution of power delivery systems to post-event infrastructure recovery. We provide a brief example of our calculations using power delivery and telecommunications data collected post-landfall for Hurricane Katrina. The model is an important component of a scheme to develop design strategies for increased resilience of urban infrastructure for extreme natural hazard scenarios.
Project scheduling is concerned with single-item or small batch production where scarce resources have to be allocated to dependent activities over time. Applications can be found in diverse industries such as construction engineering, software development, etc. Also, project scheduling is increasingly important for make-to-order companies where the capacities have been cut down in order to meet lean management concepts. Likewise, project scheduling is very attractive for researchers, because the models in this area are rich and, hence, difficult to solve. For instance, the resource-constrained project scheduling problem contains the job shop scheduling problem as a special case. So far, no classification scheme exists which is compatible with what is commonly accepted in machine scheduling. Also, a variety of symbols are used by project scheduling researchers in order to denote one and the same subject. Hence, there is a gap between machine scheduling on the one hand and project scheduling on the other with respect to both, viz. a common notation and a classification scheme. As a matter of fact, in project scheduling, an ever growing number of papers is going to be published and it becomes more and more difficult for the scientific community to keep track of what is really new and relevant. One purpose of our paper is to close this gap. That is, we provide a classification scheme, i.e. a description of the resource environment, the activity characteristics, and the objective function, respectively, which is compatible with machine scheduling and which allows to classify the most important models dealt with so far. Also, we propose a unifying notation. The second purpose of this paper is to review some of the recent developments. More specifically, we review exact and heuristic algorithms for the single-mode and the multi-mode case, for the time–cost tradeoff problem, for problems with minimum and maximum time lags, for problems with other objectives than makespan minimization and, last but not least, for problems with stochastic activity durations.
This paper originates from a European research proposal entitled RISE (Resilient Infrastructures and Structures against Emergencies). In RISE the assessment of the resilience of an urban development is carried out within an effective theoretical framework in which the large scale urban built infrastructure is perceived as a network, where nodes represent premises for the activities of the infrastructure and links represent the physical connections between them. The framework implies that the response to a critical event and the capability of recovering from the consequence of a disaster are considered both at a local level, by investigating the network elements (nodes and links) and at a global level, by considering the interactions between the elements and the response of the entire network.A basic application focusing on a specific analysis aspect (the local resilience of a critical component of the network) is provided.
Critical infrastructure resilience has become a national priority for the US Department of Homeland Security. Rapid and efficient restoration of service in damaged transportation networks is a key area of focus. The intent of this paper is to formulate a bi-level optimisation model for network recovery and to demonstrate a solution approach for that optimisation model. The lower-level problem involves solving for network flows, while the upper-level problem identifies the optimal recovery modes and sequences, using tools from the literature on multi-mode project scheduling problems. Application and advantages of this method are demonstrated through two examples.
Computer Science and Operations Research continue to have a synergistic relationship and this book represents the results of the cross-fertilization between OR/MS and CS/AI. It is this interface of OR/CS that makes possible advances that could not have been achieved in isolation. Taken collectively, these articles are indicative of the state of the art in the interface between OR/MS and CS/AI and of the high-caliber research being conducted by members of the INFORMS Computing Society.
This paper proposes a new multi-stage framework to analyze infrastructure resilience. For each stage, a series of resilience-based improvement strategies are highlighted and appropriate correlates of resilience identified, to then be combined for establishing an expected annual resilience metric adequate for both single hazards and concurrent multiple hazard types. Taking the power transmission grid in Harris County, Texas, USA, as a case study, this paper compares an original power grid model with several hypothetical resilience-improved models to quantify their effectiveness at different stages of their response evolution to random hazards and hurricane hazards. Results show that the expected annual resilience is mainly compromised by random hazards due to their higher frequency of occurrence relative to hurricane hazards. In addition, under limited resources, recovery sequences play a crucial role in resilience improvement, while under sufficient availability of resources, deploying redundancy, hardening critical components and ensuring rapid recovery are all effective responses regardless of their ordering. The expected annual resilience of the power grid with all three stage improvements increases 0.034% compared to the original grid. Although the improvement is small in absolute magnitude due to the high reliability of real power grids, it can still save millions of dollars per year as assessed by energy experts. This framework can provide insights to design, maintain, and retrofit resilient infrastructure systems in practice.
This paper presents an optimization procedure for the restoration activities associated with the bridges of a transportation network severely damaged by an earthquake. The design variables are (i) the time intervals between the occurrence of the distress and the start of the interventions on each bridge of the network; and (ii) the restoration pace of the interventions, which represents a measure of the funding allocated to each bridge. The objectives of the optimization are the maximization of the network resilience, the minimization of the time required to reach a target functionality level, and the minimization of the total cost of the restoration activities. Because the first two objectives clearly conflict with the last one, the optimization procedure does not provide a unique solution, but an entire set of Pareto solutions. A numerical example involving a complex, existing transportation network in Santa Barbara, California, illustrates the capabilities of the proposed methodology. [DOI: 10.1193/1.4000019]
In this paper, a general framework for the optimal resilience- and cost-based prioritization of interventions on bridges distributed along a highway connection between two cities that have experienced a disruptive natural or man-made event is proposed. Given the structural damage levels after the extreme event and the bridge characteristics, the proposed computational procedure finds the best intervention schedules, defined as starting time and progress pace of the restoration. The possible intervention schedules are considered optimal when they maximize the resilience of the highway segment and minimize the total cost of interventions. Because the two objectives are conflicting, the procedure uses genetic algorithms (GAs) to automatically generate a Pareto front of optimal solutions. Numerical examples are presented and discussed. DOI: 10.1061/(ASCE)BE.1943-5592.0000201. (C) 2012 American Society of Civil Engineers.
This paper addresses the issue of connectivity-and cost-based optimal scheduling for maintenance of bridges at the transportation network level. Previous studies in the same field have considered the connectivity just between two points or other network performance indicators, such as the total travel time. In this paper, the maximization of the total network connectivity is chosen as the objective of the optimization, together with the minimization of the total maintenance cost. From a computational point of view, several numerical tools are combined to achieve efficiency and applicability to real cases. Random field theory and numerical models for the time-dependent structural reliability are used to handle the uncertainties involved in the problem. Latin hypercube sampling is used to keep the computational effort feasible for practical applications. Genetic algorithms are used to solve the optimization problem. Numerical applications to bridge networks illustrate the characteristics of the procedure and its applicability to realistic scenarios.
The large losses occurred in the past due to earthquakes, even in highly developed countries, as well as the ensuing prolonged inactivity of the stricken societies, imparted momentum to research into regional seismic impact and community resilience to earthquakes. Need for comprehensive and consistent modeling is apparent, and this work presents a contribution in this direction. The extension of a recently developed civil infrastructure simulation framework to the evaluation of resilience, as well as the introduction of a new infrastructure network-based resilience metric represent the novelties of the article and allow one to explore the effect that sources of uncertainty and key vulnerability factors have on the probability distribution of resilience.
Given the importance of encouraging residents of a disaster-stricken community to remain there during the recovery process, this paper examines the determinants of disaster evacuee decisions to return to their communities, and if they return, to stay in the community where they lived before the disaster. The data come from two panel surveys of Hurricane Katrina survivors. The surveys were taken in 2005, just after Katrina, and again in 2006, a year after the disaster. Although the study sample is not indicative of the pre-Katrina population of New Orleans, it is of great value in allowing us to understand the behaviors of disaster survivors and in helping to design disaster recovery plans. Analytical results show that government performance in initial disaster recovery and individual perceptions of future lives in the community play an important role in evacuees’ decisions of whether to stay in the pre-Katrina communities. Race, risk, and damage done are also valid predictors of return decisions. This finding emphasizes the importance of both governmental initial response to the disaster and effective communication of a clear vision toward a fully recovered community.
One of the most important tasks of urban and hazard planning is to mitigate the damages and minimize the costs of the recovery process after catastrophic events. In this context, the capability of urban systems and communities to recover from disasters is referred to as resilience. Despite the problem of resilience quantification having received a lot of attention, a mathematical definition of the resilience of an urban community, which takes into account the social aspects of an urban environment, has not yet been identified. In this article, we provide and test a methodology for the assessment of urban resilience to catastrophic events which aims at bridging the gap between the engineering and the ecosystem approaches to resilience. We propose to model an urban system by means of different hybrid social–physical complex networks, obtained by enriching the urban street network with additional information about the social and physical constituents of a city, namely citizens, residential buildings, and services. Then, we introduce a class of efficiency measures on these hybrid networks, inspired by the definition of global efficiency given in complex network theory, and we show that these measures can be effectively used to quantify the resilience of an urban system, by comparing their respective values before and after a catastrophic event and during the reconstruction process. As a case study, we consider simulated earthquakes in the city of Acerra, Italy, and we use these efficiency measures to compare the ability of different reconstruction strategies in restoring the original performance of the urban system.
This paper proposes a new multi-stage framework to analyze infrastructure resilience. For each stage, a series of resilience-based improvement strategies are highlighted and appropriate correlates of resilience
identified, to then be combined for establishing an expected annual resilience metric adequate for both single hazards and concurrent multiple hazard types. Taking the power transmission grid in Harris County,
Texas, USA, as a case study, this paper compares an original power grid model with several hypothetical
resilience-improved models to quantify their effectiveness at different stages of their response evolution
to random hazards and hurricane hazards. Results show that the expected annual resilience is mainly compromised by random hazards due to their higher frequency of occurrence relative to hurricane hazards. In
addition, under limited resources, recovery sequences play a crucial role in resilience improvement, while
under sufficient availability of resources, deploying redundancy, hardening critical components and ensuring
rapid recovery are all effective responses regardless of their ordering. The expected annual resilience of
the power grid with all three stage improvements increases 0.034% compared to the original grid. Although
the improvement is small in absolute magnitude due to the high reliability of real power grids, it can still
save millions of dollars per year as assessed by energy experts. This framework can provide insights to
design, maintain, and retrofit resilient infrastructure systems in practice.
This article provides a comprehensive framework for conceptualizing, categorizing, and quantifying system performance measures in the presence of uncertain events, component failure, or other disruptions/disasters with the potential to reduce system capacity/performance. The framework clarifies the interrelationships between notions of coping capacity, preparedness, robustness, flexibility, recovery capacity, and resilience, previously espoused as independent measures, and provides a single mathematical decision problem for quantifying these measures congruously and maximizing their values. Required solution methodologies are presented for use in evaluating system performance in terms of these measures and resulting solutions can be exploited to determine an optimal allocation of limited resources to preparedness and response options. A numerical-transportation-related example is provided to illustrate its application. Results of this application offer insights into these various performance measures, their relationships, and the relative importance of preparedness and response actions.
SUMMARY The present study evaluates seismic resilience of highway bridges that are important components of highway transportation systems. To mitigate losses incurred from bridge damage during seismic events, bridge retrofit strategies are selected such that the retrofit not only enhances bridge seismic performance but also improves resilience of the system consisting of these bridges. To obtain results specific to a bridge, a reinforced concrete bridge in the Los Angeles region is analyzed. This bridge was severely damaged during the Northridge earthquake because of shear failure of one bridge pier. Seismic vulnerability model of the bridge is developed through finite element analysis under a suite of time histories that represent regional seismic hazard. Obtained bridge vulnerability model is combined with appropriate loss and recovery models to calculate seismic resilience of the bridge. Impact of retrofit on seismic resilience is observed by applying suitable retrofit strategy to the bridge assuming its undamaged condition prior to the Northridge event. Difference in resilience observed before and after bridge retrofit signified the effectiveness of seismic retrofit. The applied retrofit technique is also found to be cost-effective through a cost-benefit analysis. First order second moment reliability analysis is performed, and a tornado diagram is developed to identify major uncertain input parameters to which seismic resilience is most sensitive. Statistical analysis of resilience obtained through random sampling of major uncertain input parameters revealed that the uncertain nature of seismic resilience can be characterized with a normal distribution, the standard deviation of which represents the uncertainty in seismic resilience. Copyright © 2013 John Wiley & Sons, Ltd.
In this paper, we have reviewed various approaches to defining resilience and the assessment of resilience. We have seen that while resilience is a useful concept, its diversity in usage complicates its interpretation and measurement. In this paper, we have proposed a resilience analysis framework and a metric for measuring resilience. Our analysis framework consists of system identification, resilience objective setting, vulnerability analysis, and stakeholder engagement. The implementation of this framework is focused on the achievement of three resilience capacities: adaptive capacity, absorptive capacity, and recoverability. These three capacities also form the basis of our proposed resilience factor and uncertainty-weighted resilience metric. We have also identified two important unresolved discussions emerging in the literature: the idea of resilience as an epistemological versus inherent property of the system, and design for ecological versus engineered resilience in socio-technical systems. While we have not resolved this tension, we have shown that our framework and metric promote the development of methodologies for investigating “deep” uncertainties in resilience assessment while retaining the use of probability for expressing uncertainties about highly uncertain, unforeseeable, or unknowable hazards in design and management activities.
Resilience is generally understood as the ability of an entity to recover from an external disruptive event. In the system domain, a formal definition and quantification of the concept of resilience has been elusive. This paper proposes generic metrics and formulae for quantifying system resilience. The discussions and graphical examples illustrate that the quantitative model is aligned with the fundamental concept of resilience. Based on the approach presented it is possible to analyze resilience as a time dependent function in the context of systems. The paper describes the metrics of network and system resilience, time for resilience and total cost of resilience. Also the paper describes the key parameters necessary to analyze system resilience such as the following: disruptive events, component restoration and overall resilience strategy. A road network example is used to demonstrate the applicability of the proposed resilience metrics and how these analyses form the basis for developing effective resilience design strategies. The metrics described are generic enough to be implemented in a variety of applications as long as appropriate figures-of-merit and the necessary system parameters, system decomposition and component parameters are defined.
This paper deals with a novel technique that jointly uses structural fragility analysis, network flow analysis, and random field theory to assess the correlation among the damage levels of bridges in a transportation network under extreme events, and to estimate the sensitivity of the network performance to the correlation distance. A stochastic computational framework for the combined use of the individual bridge damage level due to extreme events and the bridge network performance evaluation is presented. Random field theory is used to simulate the bridge damage level, so that it is possible to directly control its correlation and perform a parametric analysis.Two numerical examples that involve bridges in parallel and series configurations subject to extreme events (e.g. earthquakes) show that the correlation distance of the damage can strongly affect the network performance indicators. Therefore, this correlation should be taken into account for every analysis that involves the network performance assessment.
This paper demonstrates the concept of disaster resilience through the development and application of quantitative measures. As the idea of building disaster-resilient communities gains acceptance, new methods are needed that go beyond estimating monetary losses and that address the complex, multiple dimensions of resilience. These dimensions include technical, organizational, social, and economic facets. This paper first proposes resilience measures that relate expected losses in future disasters to a community's seismic performance objectives. It then demonstrates these measures in a case study of the Memphis, Tennessee, water delivery system. An existing earthquake loss estimation model provides a starting point for quantifying resilience. The analysis compares two seismic retrofit strategies and finds that only one improves community resilience over the status quo. However, it does not raise resilience to an adequate degree. The exercise demonstrates that the resilience framework can be valuable for guiding mitigation and preparedness efforts. However, to fully implement the concept, new research on resilience is needed that goes beyond loss estimation modeling.
Resilience is the ability of the system to both absorb shock as well as to recover rapidly from a disruption so that it can return back to its original service delivery levels or close to it. The trans-oceanic telecommunication fiber-optics cable network that serves as the backbone of the internet is a particularly critical infrastructure system that is vulnerable to both natural and man-made disasters. In this paper, we propose a model to measure the base resiliency of this network, and explore the node to node and the overall resiliency of the network using existing data for demand, capacity and flow information. The submarine cable system is represented by a network model to which hypothetical disruptions can be introduced. The base resiliency of the system can be measured as the ratio of the value delivery of the system after a disruption to the value deliver of the system before a disruption. We further demonstrate how the resiliency of the trans-oceanic telecommunication cable infrastructure is enhanced through vulnerability reduction.
This paper presents a multimodal trip distribution function estimated and validated for the metropolitan Washington region. In addition, a methodology for measuring accessibility, which is used as a measure of effectiveness for networks, using the impedance curves in the distribution model is described. This methodology is applied at the strategic planning level to alternative HOV alignments to select alignments for further study and Right-of-Way preservation.
THIS REVIEW EXPLORES BOTH ECOLOGICAL THEORY AND THE BEHAVIOR OF NATURAL SYSTEMS TO SEE IF DIFFERENT PERSPECTIVES OF THEIR BEHAVIOR CAN YIELD DIFFERENT INSIGHTS THAT ARE USEFUL FOR BOTH THEORY AND PRACTICE. THE RESILIENCE AND STABILITY VIEWPOINTS OF THE BEHAVIOR OF ECOLOGICAL SYSTEMS CAN YIELD VERY DIFFERENT APPROACHES TO THE MANAGEMENT OF RESOURCES. THE STABILITY VIEW EMPHASIZES THE EQUILIBRIUM, THE MAINTENANCE OF A PREDICTABLE WORLD, AND THE HARVESTING OF NATURE'S EXCESS PRODUCTION WITH AS LITTLE FLUCTUATION AS POSSIBLE. THE RESILIENCE VIEW EMPHASIZES DOMAINS OF ATTRACTION AND THE NEED FOR PERSISTENCE. BUT EXTINCTION IS NOT PURELY A RANDOM EVENT: IT RESULTS FROM THE INTERACTION OF RANDOM EVENTS WITH THOSE DETERMINISTIC FORCES THAT DEFINE THE SHAPE, SIZE AND CHARACTERISTICS OF THE DOMAIN OF ATTRACTION. THE VERY APPROACH, THEREFORE, THAT ASSURES A STABLE MAXIMUM SUSTAINED YIELD OF A RENEWABLE RESOURCE, MIGHT SO CHANGE THESE CONDITIONS THAT THE RESILIENCE IS LOST OR IS REDUCED SO THAT A CHANCE AND RARE EVENT THAT PREVIOUSLY COULD BE ABSORBED CAN TRIGGER A SUDDEN DRAMATIC CHANGE AND LOSS OF STRUCTURAL INTEGRITY OF THE SYSTEM. A MANAGEMENT APPROACH BASED ON RESILIENCE, ON THE OTHER HAND, WOULD EMPHASIZE THE NEED TO KEEP OPTIONS OPEN, THE NEED TO VIEW EVENTS IN A REGIONAL RATHER THAN A LOCAL CONTEXT, AND THE NEED TO EMPHASIZE HETEROGENEITY. THE RESILIENCE FRAMEWORK DOES NOT REQUIRE A PRECISE CAPACITY TO PREDICT THE FUTURE BUT ONLY A QUALITATIVE CAPACITY TO DEVISE SYSTEMS THAT CAN ABSORB AND ACCOMMODATE FUTURE EVENTS IN WHATEVER UNEXPECTED FORM THEY MAY TAKE.
This paper introduces an approach to assess and improve the time-dependent resilience of urban infrastructure systems, where resilience is defined as the systems' ability to resist various possible hazards, absorb the initial damage from hazards, and recover to normal operation one or multiple times during a time period T. For different values of T and its position relative to current time, there are three forms of resilience: previous resilience, current potential resilience, and future potential resilience. This paper mainly discusses the third form that takes into account the systems' future evolving processes. Taking the power transmission grid in Harris County, Texas, USA as an example, the time-dependent features of resilience and the effectiveness of some resilience-inspired strategies, including enhancement of situational awareness, management of consumer demand, and integration of distributed generators, are all simulated and discussed. Results show a nonlinear nature of resilience as a function of T, which may exhibit a transition from an increasing function to a decreasing function at either a threshold of post-blackout improvement rate, a threshold of load profile with consumer demand management, or a threshold number of integrated distributed generators. These results are further confirmed by studying a typical benchmark system such as the IEEE RTS-96. Such common trends indicate that some resilience strategies may enhance infrastructure system resilience in the short term, but if not managed well, they may compromise practical utility system resilience in the long run.
(1) A procedure has been described for the qualitative analysis of insect outbreak systems using spruce budworm and balsam fir as an example. This consists of separating the state variables into fast and slow categories. (2) The dynamics of the fast variables are analysed first, holding the slow variables fixed. Then the dynamics of the slow variables are analysed, with the fast variables held at corresponding equilibrium values. If there are several such equilibria, there are several possibilities for the slow dynamics. (3) In the case of the budworm, this analysis exhibits the possibility of `relaxation oscillations' which are familiar from theory of the Van der Pol oscillator. In more modern terminology, the jumps of the system are governed by a cusp catastrophe. (4) Such an analysis can be made on the basis of qualitative information only, but additional insight emerges when parameter ranges are defined by the kind of information typically available from an experienced biologist. (5) At the least this can be a guide to assess subsequent priorities for both research and policy.
Notations. Preface. Part I: Project Management: Basics and Scheduling Problems. 1. The Project Management Process. 2. Project Planning and Control. 3. Resource-Constrained Scheduling Problems. Part II: Resource-Constrained Project Scheduling: Solution Methods. 4. Lower Bound Methods. 5. Heuristic Procedures. 6. Exact Procedures. 7. Computational Expirements. 8. Summary and Conclusions. References. Index.
The transport planning process as it is usually carried out consists of a number of stages. This paper considers commonly used models for two of these stages, trip distribution and traffic assignment, and derives models combining them into a single stage.The trip distribution stage of the transport planning is concerned with estimating the number of trips per unit time which will be made between each pair of zones in the study area. The models considered are all gravity models so that the estimated pattern of trips depends on the costs of travel between the various pairs of zones and these costs have usually been calculated from fixed costs associated with the links of the transport network. It is known, however, that the cost of travelling along a link increases with the amount of traffic using the link, and this is taken into account in the model used at the traffic assignment stage when the trip demands obtained from the trip distribution model are allocated to routes through the network. The link costs which correspond to the final estimated traffic flows obtained from the traffic assignment model are, however, not in general the same as those assumed at the trip distribution stage. In this paper this problem is overcome by combining trip distribution and traffic assignment into one stage and describing them by one model. The combined model is then reformulated as an equivalent optimatization problem which is solved.
This paper conducts an analysis to assess the significance of highway network links in Maryland under flood damage. An accessibility index is derived to incorporate the distance-decay effect and the volume of traffic influence on the transportation network. The accessibility level of individual counties and the state as a whole is checked before and after the hypothetical disruption of individual links within the floodplain. The results indicate that critical links identified based on the distance-only and the distance-traffic volume criteria appear to be different, implying that the priority of retrofit might also vary depending on what criterion to choose. The percentage loss of accessibility due to the disruption of a link is generally greater in the latter. However, distance-only consideration results in a more prominent spatial distribution pattern of links in percentage loss induced. Some links remain significant in both cases. Especially if the disruption of a certain link does not have an alternative solution (for example, if the link is the only way in and out of a certain county) and if counties connected by the link are low accessibility counties, the two criteria may produce a similar outcome.
The resource-constrained project scheduling problem (RCPSP) consists of activities that must be scheduled subject to precedence and resource constraints such that the makespan is minimized. It has become a well-known standard problem in the context of project scheduling which has attracted numerous researchers who developed both exact and heuristic scheduling procedures. However, it is a rather basic model with assumptions that are too restrictive for many practical applications. Consequently, various extensions of the basic RCPSP have been developed. This paper gives an overview over these extensions. The extensions are classified according to the structure of the RCPSP. We summarize generalizations of the activity concept, of the precedence relations and of the resource constraints. Alternative objectives and approaches for scheduling multiple projects are discussed as well. In addition to popular variants and extensions such as multiple modes, minimal and maximal time lags, and net present value-based objectives, the paper also provides a survey of many less known concepts.
There have been many survey papers in the area of project scheduling in recent years. These papers have primarily emphasized modeling and algorithmic contributions for specific classes of project scheduling problems, such as net present value (NPV) maximization and makespan minimization, with and without resource constraints. Paralleling these developments has been the research in the area of project scheduling decision support, with its emphasis on data sets, data generation methods, and so on, that are essential to benchmark, evaluate, and compare the new models, algorithms and heuristic techniques. These investigations have extended the frontiers of research and application in all areas of project scheduling and management. In this paper, we survey the vast literature in this area with a perspective that integrates models, data, and optimal and heuristic algorithms, for the major classes of project scheduling problems. We also include recent surveys that have compared commercial project scheduling systems. Finally, we present an overview of web-based decision support systems and discuss the potential of this technology in enabling and facilitating researchers and practitioners in identifying new areas of inquiry and application.
Two of the primary measures that characterize the concept of disaster resilience are the initial impact of a disaster event and the subsequent time to recovery. This paper presents a new analytic approach to representing the relationship between these two characteristics by extending a multi-dimensional approach for predicting resilience into a technique for fitting the resilience function to the preferences and priorities of a given decision maker. This allows for a more accurate representation of the perceived value of different resilience scenarios to that individual, and thus makes the concept more relevant in the context of strategic decision making.
Introduction Cleveland and Smith's Approach Gupta, Gupta, and Kumar's Approach Lee and Kim's Approach Cheng and Gen's Approach
Recent earthquake disasters have caused major damage to transportation networks, leading to significant economic disruption. While this suggests the need to evaluate total system performance in transportation risk assessment, in addition to examining the vulnerability of individual components such as bridges, no appropriate measures currently exist. This paper develops post-disaster system performance measures and applies them to the urban rail and highway transportation systems in the Kobe, Japan, region devastated by the 1995 Hyogoken-Nanbu earthquake. Performance is evaluated in terms of network coverage and transport accessibility. Performance degradation was much more severe for highways and railways than for other lifeline infrastructure systems. Both transportation systems fared poorly in the disaster but service restoration proceeded much more rapidly for rail. The restoration of highway system performance correlated closely with the recovery of highway traffic volumes. The paper further develops a measure of subarea transport accessibility and applies this to Kobe's constituent city wards. Results indicate substantial spatial disparity that is maintained throughout the restoration period. Comparisons with the 1989 Loma Prieta and 1994 Northridge earthquakes in the US show that although these disasters caused notable damage to highway bridges, system performance degradation was small in comparison with the Kobe experience. The paper argues that explicitly measuring transportation system performance can greatly facilitate both understanding the effects of historic disasters and preparing for future hazard events.