Measurement of functional resilience of transport network:The case of the Beijing subway network

Resilience is an important concept for measuring a system\u27s ability to cope with various disruptions. This study proposes an application-oriented framework for measuring the dynamic functional resilience (FR) of a transport network responding to supply and demand disruptions without external interventions. On the conceptual side, three complementary capacity-related dimensions, namely, robustness, adaptability, and recoverability, are incorporated in the single FR framework from the perspective of physical laws. On the applied side, we suggest a measurement model given certain network indices and apply it to the Beijing subway network (BSN). The results indicate the measurement model can capture the dynamics of network performances, identify the time-varying bottlenecks, and predict the influence of the dynamic capacity expansions on network resilience. The findings are useful for policy-making regarding the dynamic design, operation, and reconstruction of the transport infrastructure

Quantifying fault recovery in multiprocessor systems

Various aspects of reliable computing are formalized and quantified with emphasis on efficient fault recovery. The mathematical model which proves to be most appropriate is provided by the theory of graphs. New measures for fault recovery are developed and the value of elements of the fault recovery vector are observed to depend not only on the computation graph H and the architecture graph G, but also on the specific location of a fault. In the examples, a hypercube is chosen as a representative of parallel computer architecture, and a pipeline as a typical configuration for program execution. Dependability qualities of such a system is defined with or without a fault. These qualities are determined by the resiliency triple defined by three parameters: multiplicity, robustness, and configurability. Parameters for measuring the recovery effectiveness are also introduced in terms of distance, time, and the number of new, used, and moved nodes and edges

Resilience in Transportation Networks

The functionality of transportation networks is greatly challenged by risk factors such as increasing climate-related hazards, rising population exposure, and greater city vulnerability. Inevitably, the transportation network cannot withstand the impact of an overwhelming disaster, which results in rapid declines in the performance of road net-work. As a next step, the authorities need to restore the performance of the road net-work to an acceptable state as soon as possible and rebalance the conflict between the capacity of the road network and travel demand. Resilience is defined as the process of system performance degradation followed by recovery. To improve the transportation network resilience and maintain regular traffic, it is crucial to identify which factors are related to the resilience and investigate how these factors impact resilience. In this thesis, four factors, i.e., road networks, evacuees, disruption types and au-thorities, are identified to analyze resilience mechanisms. Firstly, the change in vehicle speed during a disaster is used as a measure of resilience, and we analyze the quantita-tive relationship between resilience and the structural characteristics and properties of the road network in multiple disruptions in multiple cities. The results show that the connectivity of the road network, the predictability of disruption, and the population density affect the resilience of the road network in different ways. Secondly, as the road connectivity plays a crucial role during the evacuation pe-riod and considering more frequent and extensive bushfires, we explore a practical and challenging problem: are bushfire fatalities related to road network characteristics? Con-nectivity index (CI), a composite metric that takes into account redundancy, connectivi-ty, and population exposure is designed. The statistical analysis of real-world data sug-gests that CI is significantly negatively correlated with historical bushfire fatalities. This parsimonious and simple graph-theoretic measure can provide planners a useful metric to reduce vulnerability and increase resilience among areas that are prone to bushfires. Finally, a modelling framework for optimizing road network pre-disaster invest-ment strategy under different disaster damage levels is proposed. A bi-level multi-objective optimization model is formulated, in which the upper-level aims to maximize the capacity-based functionality and robustness of the road network, and the lower-level is the user equilibrium problem. To efficiently solve the model, the Shapley value is used to select candidate edges and obtain a near-optimal project order. For more reality, the heterogeneity of road segments to hazards and the correlation of road segments in dif-ferent hazard phases are considered. Realistic speed data is used to explore the depend-ency between different disaster states with copula functions. The numerical results illus-trate that the investment strategy is significantly influenced by the road edge character-istics and the level of disaster damage. Critical sections that can significantly improve the overall functionality of the network are identified. Overall, the core contribution of this thesis is to provide insights into the evalua-tion and analysis of resilience in transportation networks, as well as develop modelling frameworks to promote resilience. The results of this work can provide a theoretical ba-sis for road network design, pre-disaster investment and post-disaster emergency rescue

Recoverable robust single day aircraft maintenance routing problem

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Aircraft maintenance planning is of critical importance to the safe and efficient operations of an airline. It is common to solve the aircraft routing and maintenance planning problems many months in advance, with the solution spanning multiple days. An unfortunate consequence of this approach is the possible infeasibility of the maintenance plan due to frequent perturbations occurring in operations. There is an emerging concept that focuses on the generation of aircraft routes for a single day to ensure maintenance coverage that night, alleviating the effects of schedule perturbations from preceding days. In this paper, we present a novel approach to ensure that a sufficient number of aircraft routes are provided each day so maintenance critical aircraft receive maintenance that night. By penalising the under supply of routes terminating at maintenance stations from each overnight airport, we construct a single day routing to provide the best possible maintenance plan. This single day aircraft maintenance routing problem (SDAMRP) is further protected from disruptions by applying the recoverable robustness framework. To efficiently solve the recoverable robust SDAMRP acceleration techniques, such as identifying Pareto-optimal cuts and a trust region approach, have been applied. The SDAMRP is evaluated against a set of flight schedules and the results demonstrate a significantly improved aircraft maintenance plan. Further, the results demonstrate the magnitude of recoverability improvement that is achieved by employing recoverable robustness to the SDAMRP.Australian Research Council Centre of Excellence for Mathematics and Statistics of Complex SystemsNatural Sciences and Engineering Research Council of Canada

Competitive percolation strategies for network recovery

Restoring operation of critical infrastructure systems after catastrophic events is an important issue, inspiring work in multiple fields, including network science, civil engineering, and operations research. We consider the problem of finding the optimal order of repairing elements in power grids and similar infrastructure. Most existing methods either only consider system network structure, potentially ignoring important features, or incorporate component level details leading to complex optimization problems with limited scalability. We aim to narrow the gap between the two approaches. Analyzing realistic recovery strategies, we identify over- and undersupply penalties of commodities as primary contributions to reconstruction cost, and we demonstrate traditional network science methods, which maximize the largest connected component, are cost inefficient. We propose a novel competitive percolation recovery model accounting for node demand and supply, and network structure. Our model well approximates realistic recovery strategies, suppressing growth of the largest connected component through a process analogous to explosive percolation. Using synthetic power grids, we investigate the effect of network characteristics on recovery process efficiency. We learn that high structural redundancy enables reduced total cost and faster recovery, however, requires more information at each recovery step. We also confirm that decentralized supply in networks generally benefits recovery efforts.Comment: 14 pages, 6 figure