Severe weather events, such as snowfall, flooding and storms, may affect wide geographical areas and adversely impact discrete transport infrastructure networks (e.g. road, rail) at the same time, ...thus revealing the existence of geographic interdependencies between these networks. In this paper, we develop two accessibility-based measures to assess the impact of geographic interdependency on resilience based on the concepts of redundancy and substitutability, respectively. These measures are applied to the railway and long-distance bus networks in Scotland. Results reveal that the combined effect of redundancy and substitutability on the accessibility of locations offered by these discrete modes is reduced due to geographic interdependencies, with the extent of losses being positively associated with the spatial footprint of potential events. The results can be used to identify parts of the network where the potential impacts of geographic interdependencies are greatest, and thus require more in-depth scrutiny by network managers.
Research Summary: We ask two questions: First, what are the underlying mechanisms that explain the power of modularity? Second, is the power of modularity robust in nonmodular problems? We replicate ...and then reconcile the key results in two prior models on modularity: E&L and Ssearch. Our results yield several important insights. First, a significant portion of the advantage enjoyed by S-search is attributed to multi-bit mutation. Second, organizationevaluation needs to be used in combination with multi-bit mutation. Third, when the underlying problem structure becomes nonmodular, S-search outperforms E&L search, even though the advantage is reduced. More generally, organizational designers need to pay close attention to how different elements of modular search interact, and avoid making incremental adjustments. Managerial Summary: Modularity in product or organizational design is an approach that divides a system into smaller modules and attempts to augment the system level performance by experimenting with new modules. Because of its potential benefits such as parallel problem solving, adaptability in turbulent environment, or high speed in experimentation, both scholars and practitioners subscribed to the "power of modularity" thesis. Despite its popularity, there are significant number of cases where the superiority of modular design does not hold. We compare and contrast two representative prior studies that had different views on modeling organizational evolution under a modular design principle. By doing so, we are able to uncover what contributes to the superiority of modular design. Our results suggest that, when conducting experimentation under a modular design, it is important to (a) experiment multiple decision components simultaneously within a single module; and (b) allow evaluation of the changes to be made by the module-level manager not by the organization-level manager. When the manager does not know whether the modularity in organizational design fits with the modularity in the task, it is advised to do multiple experimentation in a single module at a time while allowing the organization-level manager to evaluate the changes.
The importance of understanding system resilience and identifying ways to enhance it, especially for interdependent infrastructures our daily life depends on, has been recognized not only by ...academics, but also by the corporate and public sectors. During recent years, several methods and frameworks have been proposed and developed to explore applicable techniques to assess and analyze system resilience in a comprehensive way. However, they are often tailored to specific disruptive hazards/events, or fail to properly include all the phases such as absorption, adaptation, and recovery. In this paper, a quantitative method for the assessment of the system resilience is proposed. The method consists of two components: an integrated metric for system resilience quantification and a hybrid modeling approach for representing the failure behavior of infrastructure systems. The feasibility and applicability of the proposed method are tested using an electric power supply system as the exemplary infrastructure. Simulation results highlight that the method proves effective in designing, engineering and improving the resilience of infrastructures. Finally, system resilience is proposed as a proxy to quantify the coupling strength between interdependent infrastructures.
•A method for quantifying resilience of interdependent infrastructures is proposed.•It combines multi-layer hybrid modeling and a time-dependent resilience metric.•The feasibility of the proposed method is tested on the electric power supply system.•The method provides insights to decision-makers for strengthening system resilience.•Resilience capabilities can be used to engineer interdependencies between subsystems.
Critical Infrastructures (CIs) are exposed to various risks, which hinder their successful operation and induce great losses. Due to the interdependency between risks and that between CIs, the ...identification of the most prominent risks becomes complex and challenging. However, existing studies rarely considered the dual interdependency of risks and CIs. This study proposes a double-layer network for multiple interdependent risks and CIs. The Design Structure Matrix (DSM) and Restart Random Walk (RRW) algorithm are combined to determine the impact of risks on CIs by incorporating the strength of dual interdependency. The LeaderRank algorithm is then used to rank these risk factors and an illustrative example is given to validate the model. The proposed model and algorithms can systematically quantify complex interdependencies embedded in the operation of CIs susceptible to multiple risks, and provide decision-makers with evidence to prioritize risks.
Structure and degree of oil price impact on sectoral indices are examined using Quantile Regression Analysis (QRA). Nine sectors are found to provide diversification opportunities during a bull ...market (i.e. 90th quantile of the return distribution) and three sectors during a bear market (10th quantile) to hedge oil price risk. Further, the contagion effect and interdependency between oil price and sectoral equity are assessed through frequency domain causality. The causality from oil price in the long run determined that there is interdependence between three sectors and oil price changes. The direction of causality from oil price is mixed in both the short run (high frequency) and long run (low frequency) for nine sectors. Overall, the carbon sector is the only sector that is immune to oil price risk, thereby providing investment and hedging opportunities for portfolio managers.
•Oil price shock impacts on Indian stock market sectoral index are investigated.•We use asymmetric quantile regression and frequency-domain Granger causality.•Oil price tail risk affects all sectoral indices other than of the carbon sector.•A contagion effect for negative oil price shocks is found in six sectors.•Interdependency is found in five sectors in the case of positive oil price shocks.
An efficient smart and connected community (SCC) depends on the interconnectivity of essential infrastructure systems. However, current modeling tools are unable to determine which interconnections ...are most important to include, particularly as system dynamics become more complex with high-order effects. To bridge this gap, we propose a comprehensive framework that incorporates multi-layers, multi-blocks, and multi-agents to model interdependent infrastructure systems. Interconnections span cyber, physical, and logical aspects, including human interactions. With the equation-based object-oriented language Modelica, we model energy, transportation, communication, and water systems for a hypothetical SCC and assess higher-order interdependency effects during normal operation. Additionally, we develop a quasi-Monte Carlo sensitivity analysis framework and use variance-based sensitivity metrics to assess the impact of interdependencies on energy system operation. Compared to the decoupled baseline system, the energy consumption of logical interdependency cases varied by 23.3%, the cyber interdependency by 2.0%, and the nested global interdependency by 21.5%. The sensitivity analysis further revealed that interrelationships are not linear nor quadratic, but involve higher-order interactions between parameters. Specifically, occupancy and cyber delays had significant first-order effects. Road delays had a significant higher-order effect, which corresponded to a stronger influence on other model parameters. By modeling higher-order cascading dependencies, our proposed framework has the potential to improve the planning, operation, and co-design of SCCs by quantifying the importance of complex system interactions.
•First to model the interaction of energy, communication, transportation, and water systems in one platform.•Includes physical, cyber, and logical interdependencies for comprehensive analysis.•Quantifies the sensitivity of complex system interactions through Quasi-Monte Carlo approach.•An efficient tool to improve the planning, operation, and co-design of Smart Connected Communities (SCC).
•Develop a Spatiotemporal graph convolutional network for hourly energy predictions.•Inter-building impacts are considered in graph-based method for prediction.•Test ST-GCN on campus buildings and ...validate its improved performance.•Discuss the interpretability of the ST-GCN modelling results.
The world is increasingly urbanizing, and to improve urban sustainability, many cities adopt ambitious energy-saving strategies through retrofitting existing buildings and constructing new communities. In this situation, an accurate urban building energy model (UBEM) is the foundation to support the design of energy-efficient communities. However, current UBEM are ineffective to capture the inter-building interdependency due to their dynamic and non-linear characteristics. Those conventional models either ignored or oversimplified these building interdependencies, which can substantially affect the accuracy of urban energy modeling. To fill the research gap, this study proposes a novel data-driven UBEN synthesizing the solar-based building interdependency and spatio-temporal graph convolutional network (ST-GCN) algorithm. Especially, we took a university campus located in the downtown area of Atlanta as an example to predict the hourly energy consumption. Furthermore, we tested the feasibility of the ST-GCN model by comparing the performance of the ST-GCN model with other common time-series machine learning models. The results indicate that the ST-GCN model overall outperforms in different scenarios, the mean absolute percentage error of ST-GCN is around 5%. More importantly, the accuracy of ST-GCN is enhanced when simulating buildings with higher edge weight and in-degrees, this phenomenon is magnified in summer daytime and winter daytime, which validated the interpretability of the ST-GCN models. After discussion, it is found that data-driven models integrated with engineering or physics knowledge can significantly improve urban building energy use prediction.
Compared with a single and isolated network, interdependent networks have two types of links: connectivity link and dependency link. This paper aims to improve the robustness of interdependent ...networks by adding connectivity links. Firstly, interdependent networks failure model and four frequently used link addition strategies are briefly reviewed. Furthermore, by defining inter degree–degree difference, two novel link addition strategies are proposed. Finally, we verify the effectiveness of our proposed link addition strategies by comparing with the current link addition strategies in three different network models. The simulation results show that, given the number of added links, link allocation strategies have great effects on the robustness of interdependent networks, i.e., the double-network link allocation strategy is superior to single-network link allocation strategy. Link addition strategies proposed in this paper excel the current strategies, especially for BA interdependent networks. Moreover, our work can provide guidance on how to allocate limited resources to an existing interdependent networks system and optimize its topology to avoid the potential cascade failures.
•Considering interdependent relationships, two novel connectivity link addition strategies are proposed.•Performance comparisons among six link addition strategies are conducted in three types of interdependent networks.•The robustness of interdependent networks can be improved by adding connectivity links.•Double-network link allocation strategy yields better performance to single-network link allocation strategy.•Simulation results indicate that our proposed methods are superior to the current link addition strategies.
The wide integration of gas-fired units and implementation of power-to-gas technologies bring increasing interdependence among the natural gas and electricity infrastructures. This paper studies the ...equilibrium of the coupled gas and electricity markets, which is driven by the strategic offering behaviors: each producer endeavours to maximize its own profit by taking the market clearing process into consideration. The market equilibrium can be obtained from an equilibrium problem with equilibrium constraints. A special diagonalization algorithm is devised, in which the unilateral equilibrium of the gas or electricity market is found in the inner loop given the rivals' strategies; the interactions of the two markets are tackled in the outer loop. Case studies on two test systems validate the proposed methodology.
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