Optimal Risk Mitigation Research Articles

Optimizing risk mitigation investment strategies for improving post-earthquake road network resilience

The road network is a major urban lifeline, and its post-earthquake resiliency is crucial to rapid and reliable rescue work. This study aims to optimize risk mitigation investment strategies for improving post-earthquake road network resilience. First, instead of theoretical assumptions on link failure probability, an empirical predictive model of debris obstruction of collapsed buildings was used to calculate link connectivity probability. Second, the Monte Carlo simulation method was employed to calculate post-earthquake road network accessibility based on the results of link connectivity reliability. Third, an optimal risk mitigation investment model was used in order to maximize post-earthquake network accessibility with different government budgets. A case study was analyzed by using the proposed method in an Shanghai historical area. Results may help local governments make better decisions of risk mitigation strategies in regard to earthquakes.

A Hybrid Approach for Dynamic Simulation of Safety Risks in Mega Construction Projects

Mega construction projects (MCPs) are inherently high‐risk and complex. The challenge of safety management for mega construction projects is that safety risk factors constantly change and interact with each other in the long‐term construction period. Few of the prior studies have enabled the prediction of the safety state in a dynamic and connected overview, which is a critical characteristic of safety risks in MCPs. Therefore, a hybrid approach for the dynamic simulation of risk factors is proposed. A three‐stage procedure review of explicit documents, including accident investigation reports and construction standards, was carried out to identify safety risk factors and the causal relationships among them. Subsequently, the likelihood exposure and consequence (LEC) assessment method was applied to define the changes in risk factors over time. A system dynamics (SD) model was established to integrate the interacting risks and simulate the developing trend of the overall safety risk state. Moreover, a sensitivity analysis was provided to rank risk factors and simulate optimal risk mitigation strategies. Finally, the model was applied to the urban rail transit Line 9 project in China as a case study. The results indicated that the proposed hybrid approach performed satisfactorily under complex interrelated risk factors. Therefore, this study provides a practical framework to simulate and predict the safety state dynamically in a timely process for MCPs, either ahead of a project theoretically or during a project with real data.

Coordination of nonholonomic mobile robots for diffusive threat defense

This paper studies coordination of a team of nonholonomic mobile robots with smart actuators for defending against invasive threat to a planar convex area. The threat refers to a kind of harmful substance such as chemical pollutant appearing outside and moving towards the area. The invasion of threat can be modeled by a 2D unsteady reaction-diffusion process. To reflect the adverse effect of threat on the area, a so-called risk intensity field is introduced. The value of risk intensity is equal to the concentration of threat measured by a static mesh sensor network. Based on this risk intensity field, a coordination control scenario using Voronoi tessellation is formulated. In order to minimize the actuator performance loss and reduce the total average risk intensity simultaneously, a generalized centroidal Voronoi tessellation (CVT) algorithm including optimal motion control and risk mitigation control is designed. The proposed algorithm is gradient-based and guides mobile robots to track their optimal trajectories asymptotically. Meanwhile, two conditions of choosing control gains are derived to keep the total average risk intensity below a safety level. Several simulation examples with different cases of threat invasion are provided and the advantage of proposed algorithm over traditional control method is presented.

Roles of inventory and reserve capacity in mitigating supply chain disruption risk

This research focuses on managing disruption risk in supply chains using inventory and reserve capacity under stochastic demand. While inventory can be considered as a speculative risk mitigation lever, reserve capacity can be used in a reactive fashion when a disruption occurs. We determine optimal inventory levels and reserve capacity production rates for a firm that is exposed to supply chain disruption risk. We fully characterise four main risk mitigation strategies: inventory strategy, reserve capacity strategy, mixed strategy and passive acceptance. We illustrate how the optimal risk mitigation strategy depends on product characteristics (functional versus innovative) and supply chain characteristics (agile versus efficient). This work is inspired from a risk management problem of a leading pharmaceutical company.

Disruption Risk Management in Two-Echelon Supply Chains: Early Commitment to Finished Goods

This research focuses on managing supply chain disruption risk using inventory and reserve capacity in serial multi-stage supply chains. The research problem is to determine the optimal risk mitigation inventories (RMI) and reserve capacities when product transformation occurs at each stage. Disruptions at each stage are modeled as a random process. We find that under reasonable conditions it is better to hold more RMI downstream than upstream even when the holding costs upstream are lower. We also find that it is often optimal to hold more reserve capacity downstream than upstream even when reserve capacity costs are lower upstream. Our research also suggests that RMI is preferred to reserve capacity as a risk mitigation lever in long serial supply chains, i.e., in supply chains with a large number of stages. We further show that RMI and reserve capacity are not always substitutes as suggested by the literature, and we establish conditions when they are complements; that is an increase RMI can increase the marginal value of reserve capacity and vice versa.

A quantitative approach for risk assessment of a ship stuck in ice in Arctic waters

Arctic waters have historically been regarded as harsh environments owing to their extreme weather conditions and remoteness from land. The advantages of shorter sea routes and hydrocarbon energy exploitation have recently led to increased marine activities in such harsh environments. To ensure safe operation within the area, the potential risks of ship accidents, need to be systematically analyzed, assessed and managed along with the associated uncertainties. The treatment of epistemic uncertainty in the likelihoods of adverse events due to lack of knowledge and information should also be considered. This paper presents a Frank copula-based fuzzy event tree analysis approach to assess the risks of major ship accidents in Arctic waters, taking uncertainty into consideration. The quantitative approach includes four steps, namely, accident scenario modeling by an event tree model, probability and dependence analysis of the associated intermediate events, risk assessment with respect to the consequent outcome events. A major ship accident in Arctic waters - ships stuck in ice, is chosen as a case to interpret the modeling process of the approach proposed. Crews and ships owners can use such approach to defining risk control options that enable optimal risk mitigation. Maritime management may also benefit from better risk assessment.

Embedding risk in closed-loop supply chain network design

PurposeThe purpose of this paper is to assist a manufacturing firm in designing the closed-loop supply chain network under risks that are affecting its supply quality and logistics operations. The modeling approach adopted aims at the embedding supply chain risks in a closed-loop supply chain (CLSC) network design process and suggests optimal supply chain configuration and risk mitigation strategies.Design/methodology/approachThe method proposes a closed-loop supply chain network and identifies the network parameter and variables required for closing the loop. Mixed-integer-linear-programming-based mathematical modeling approach is used to formulate the research problem. The solutions and test results are obtained from CPLEX solver.FindingsThe outcomes of the proposed model were demonstrated through a case study conducted in an Indian hospital furniture manufacturing firm. The modern supply chain is mapped to make it closed loop, and potential risks in its supply chain are identified. The supply chain network of the firm is redesigned through embedding risk in the modeling process. It was found that companies can be in great profit if they follow closed-loop practices and simultaneously keep a check on risks as well. The cost of making the supply chain risk averse was found to be insignificant.Practical implicationsAlthough the study was conducted in a practical case situation, the obtained results are not indiscriminate to the other circumstances. However, the approach followed and proposed methodology can be applied to many industries once a firm decides to redesign its supply chain for closing its loop or model under risks.Originality/valueBy using the identified CLSC parameters and applying the proposed network design methodology, a firm can design/redesign their supply chain network to counter the risk and accordingly come up with planned mitigation strategies to achieve a certain degree of robustness.

Population vulnerability models for asteroid impact risk assessment

AbstractAn asteroid impact is a low probability event with potentially devastating consequences. The Asteroid Risk Mitigation Optimization and Research (ARMOR) software tool calculates whether a colliding asteroid experiences an airburst or surface impact and calculates effect severity as well as reach on the global map. To calculate the consequences of an impact in terms of loss of human life, new vulnerability models are derived that connect the severity of seven impact effects (strong winds, overpressure shockwave, thermal radiation, seismic shaking, ejecta deposition, cratering, and tsunamis) with lethality to human populations. With the new vulnerability models, ARMOR estimates casualties of an impact under consideration of the local population and geography. The presented algorithms and models are employed in two case studies to estimate total casualties as well as the damage contribution of each impact effect. The case studies highlight that aerothermal effects are most harmful except for deep water impacts, where tsunamis are the dominant hazard. Continental shelves serve a protective function against the tsunami hazard caused by impactors on the shelf. Furthermore, the calculation of impact consequences facilitates asteroid risk estimation to better characterize a given threat, and the concept of risk as well as its applicability to the asteroid impact scenario are presented.

Resilience of Civil Infrastructure by Optimal Risk Mitigation

This paper puts forward a framework for optimal mitigation of regional risk to enhance the resilience of civil infrastructure. To meet this objective, probabilistic models, methods, and software are developed and applied. The work is conducted within a new reliability-based approach, in which reliability methods compute risk. This contrasts several contemporary approaches for risk analysis. Risk, in this context, denotes the probability of exceeding monetary loss. Evaluating such probabilities requires probabilistic models for hazards, response, damage, and loss. This motivates the following contributions in this paper. First, a new computer program is developed that is tailored to conduct reliability analysis with many interconnected probabilistic models. It orchestrates the interaction of models through an object-oriented architecture. Second, a library of probabilistic models for regional seismic risk analysis are developed. The library includes new models for earthquake location and magnitude and building response, damage, and loss. Third, probabilistic methods for multi-hazard risk analysis are developed and applied in a large-scale regional analysis. The results are cost exceedance probabilities and insights into the seismic risk of the region. Finally, sensitivity measures are developed to identify the buildings whose retrofit yields the most reduction in regional risk, i.e., the most resilience of the region.

Project Complexity and Risk Management (ProCRiM): Towards modelling project complexity driven risk paths in construction projects

Project complexity has been extensively explored in the literature because of its contribution towards the failure of major projects in terms of cost and time overruns. Focusing on the interface of Project Complexity and Interdependency Modelling of Project Risks, we propose a new process that aids capturing interdependency between project complexity, complexity induced risks and project objectives. The proposed modelling approach is grounded in the theoretical framework of Expected Utility Theory and Bayesian Belief Networks. We consider the decision problem of identifying critical risks and selecting optimal risk mitigation strategies at the commencement stage of a project, taking into account the utility function of the decision maker with regard to the importance of project objectives and holistic interaction between project complexity and risk. The proposed process is supported by empirical research that was conducted in the construction industry and its application is illustrated through a simulation study.

Framework for the quantitative assessment of the risk of leakage from LNG-fueled vessels by an event tree-CFD

Abstract: Liquefied natural gas (LNG) is used as fuel in various kinds of vessels, e.g., passenger ship, ferry, cargo vessel and platform supply vessel (PSV). It is an eco-friendly bunker fuel with many advantages, like decreasing the emissions of SOX and particulate materials (PM) and meeting the international maritime organization (IMO) MARPOL Annex VI requirements on NOX emissions, and economic benefits compared to heavy fuel oil (HFO). However, the leakage of LNG-fuel is a threat for the safety of LNG-fueled vessels, due to its inflammable and explosive characteristics. This paper illustrates a framework for the quantitative risk assessment of LNG-fueled vessels with respect to potential leakage. For illustration purposes, reference is made to a typical LNG-fueled ship, as a representative case. Event tree analysis (ETA) and computational fluid dynamics (CFD) simulation are integrated for the investigation of the hazard, the analysis of the consequences, and the quantification the risk of the LNG leakage. The results of the study are used to provide risk control options (RCOs), in terms of optimal risk mitigation for LNG-fueled vessels.

Quantifying the effectiveness of early warning systems for natural hazards

Abstract. Early warning systems (EWSs) are increasingly applied as preventive measures within an integrated risk management approach for natural hazards. At present, common standards and detailed guidelines for the evaluation of their effectiveness are lacking. To support decision-makers in the identification of optimal risk mitigation measures, a three-step framework approach for the evaluation of EWSs is presented. The effectiveness is calculated in function of the technical and the inherent reliability of the EWS. The framework is applicable to automated and non-automated EWSs and combinations thereof. To address the specifics and needs of a wide variety of EWS designs, a classification of EWSs is provided, which focuses on the degree of automations encountered in varying EWSs. The framework and its implementation are illustrated through a series of example applications of EWS in an alpine environment.

Optimizing Bridge Network Retrofit Planning Based on Cost-Benefit Evaluation and Multi-Attribute Utility Associated with Sustainability

During their service life, bridge networks may be exposed to extreme events, including strong earthquakes, which pose an imminent threat to society, economy, and surrounding environment. This threat reinforces the need to implement updated sustainability assessment and optimal risk mitigation procedures. The sustainability-based seismic optimization of bridge networks considering the utility associated with cost and benefit of retrofit interventions is investigated. The ultimate aim of this framework is to reduce the extent of earthquake damage to society, economy, and environment, while simultaneously minimizing the total retrofit costs of bridge networks. The total benefit of a retrofit plan is quantified in terms of the reduction in the seismic loss during a given time interval using multi-attribute utility theory. Retrofit actions associated with varying improvement levels are considered herein. A genetic algorithm based optimization procedure is adopted to determine the optimal retrofit action for each bridge within an existing bridge network.

Developing lean and responsive supply chains: A robust model for alternative risk mitigation strategies in supply chain designs

This paper investigates how organization should design their supply chains (SCs) and use risk mitigation strategies to meet different performance objectives. To do this, we develop two mixed integer nonlinear (MINL) lean and responsive models for a four-tier SC to understand these four strategies: i) holding back-up emergency stocks at the DCs, ii) holding back-up emergency stock for transshipment to all DCs at a strategic DC (for risk pooling in the SC), iii) reserving excess capacity in the facilities, and iv) using other facilities in the SC’s network to back-up the primary facilities. A new method for designing the network is developed which works based on the definition of path to cover all possible disturbances. To solve the two proposed MINL models, a linear regression approximation is suggested to linearize the models; this technique works based on a piecewise linear transformation. The efficiency of the solution technique is tested for two prevalent distribution functions. We then explore how these models operate using empirical data from an automotive SC. This enables us to develop a more comprehensive risk mitigation framework than previous studies and show how it can be used to determine the optimal SC design and risk mitigation strategies given the uncertainties faced by practitioners and the performance objectives they wish to meet.

Forecasting rock slope failure: how reliable and effective are warning systems?

Warning systems are increasingly applied to reduce damage caused by different magnitudes of rockslides and rockfalls. In an integrated risk-management approach, the optimal risk mitigation strategy is identified by comparing the achieved effectiveness and cost; estimating the reliability of the warning system is the basis for such considerations. Here, we calculate the reliability and effectiveness of the warning system installed in Preonzo prior to a major rockfall in May 2012. “Reliability” is defined as the ability of the warning system to forecast the hazard event and to prevent damage. To be cost-effective, the warning system should forecast an event with a limited number of false alarms to avoid unnecessary costs for intervention measures. The analysis shows that to be reliable, warning systems should be designed as fail-safe constructions. They should incorporate components with low failure probabilities, high redundancy, have low warning thresholds, and additional control systems. In addition, the experts operating the warning system should have limited risk tolerance. In an additional hypothetical probabilistic analysis, we investigate the effect of the risk attitude of the decision makers and of the number of sensors on the probability of detecting the event and initiating a timely evacuation, as well as on the related intervention cost. The analysis demonstrates that quantitative assessments can support the identification of optimal warning system designs and decision criteria.

Optimal Risk Mitigation Planning for Supply Chain Disruption

Optimal Risk Mitigation Planning for Supply Chain Disruption

Supply chain risk mitigations with expedited shipping

We study supply chain risk mitigations by incorporating expedited shipping. The firm we consider can source from two suppliers—one is unreliable while the other is perfectly reliable but more expensive. It intends to mitigate the potential risks caused by random disruptions at the unreliable supplier by adapting its risk mitigation policy based on the supplier’s disruption factors, such as the expected disruption length and expected availability, together with other information on costs and transportation time. This work generalizes the research of Tomlin [32] who investigated a similar problem without expedited shipping. We show how the expedited shipping option influences the optimal mitigation strategies, and conclude that a firm’s optimal risk-mitigation strategy is sensitive to its capability of expediting shipments. We also identify the cost-saving feature of expedited shipping for supply chain risk mitigations, and demonstrate that it is a good alternative when the cost of maintaining flexible capacity at the reliable supplier is high. These characterize the importance of considering expedited shipping in supply chain risk mitigation, and offer insights for developing new transportation modes with expedited shipping in practice.

Development of a mitigation strategy against the cascading propagation of risk in R&D network

Considering severe consequences caused by cascading propagation of risk to R&D network, it is essential to develop a mitigation strategy against it for keeping network safety. Firstly, we propose the generation algorithm of R&D network based on two rules of preferential attachment. Then we develop a mitigation strategy on restoring failed firms against cascading propagation of risk, where three types of restoration methods (i.e. random restoration, high-degree restoration and high-capacity restoration) and restoration mechanism are described. Finally, we explore the effects of mitigation strategy under different values some critical parameters through numerical simulation. The simulation results show that all three restoration methods can generally enhance the robustness of R&D network, which is increasingly improved with the increasing homogeneity of firms’ capacities distribution. High-capacity restoration is the most efficient one on mitigating cascading propagation of risk for any degree of capacity distribution, any proportion of restored firms and any kind of attack. But the gap between high-capacity restoration and any other restoration in the efficiency of mitigating cascading propagation of risk is increasingly less with the increasing proportion of restored firms. For any given restoration method, random attack has the least impact on the robustness of R&D network, whereas the other attacks have almost the similar impacts. This research work will provide a useful theoretical basis on building the optimal risk-mitigation strategy to keep the safety of R&D network in the real world.

Perioperative pulmonary complications

This article reviews current concepts in perioperative pulmonary management. Preoperative risk assessment tools for perioperative pulmonary complications (POPCs) are evolving for both children and adults. Intraoperative management strategies have a demonstrable effect on outcomes. Late POPCs may be preceded by clinical signs. POPCs are common and lead to significant resource utilization. Optimal POPC risk mitigation must span all phases of surgical care. Preoperative assessment may identify patients at risk and effectively lower their risk by identifying targeted interventions. Intra-operative strategies impact postoperative outcome. POPCs continue to be a concern for several days postoperatively. We review the current literature on this broad subject with a focus on implementable interventions for the clinician.

Optimizing risk mitigation in management of sexual offenders: A structural model

Sexual violence is an insidious and pervasive problem that insinuates itself into all aspects of contemporary society. It can neither be mitigated nor adequately controlled through current socio-legal practices. A more promising approach must embrace four integrated elements: (1) public policy, (2) primary prevention, (3) statutory management, and (3) secondary intervention. In the present paper we tackle the 3rd and 4th elements by proposing an integrated model for reducing and managing sexual violence among known sex offenders. Relying on the highly effective Risk-Need-Responsivity (RNR) model as the core of our Sex Offender Risk Mitigation and Management Model (SORM 3), we draw together evidence based practices from clinical interventions and risk assessment strategies. Developed by Andrews & Bonta (2006), RNR has a strong empirical track record of efficacy when applied to diverse samples of offenders, including sex offenders (Hanson, Bourgon, Helmus, & Hodgson, 2009). We offer a detailed structural model that seeks to provide a more seamless integration of risk assessment with management and discretionary decisions, including a primary focus on RNR-based post-release aftercare. We end with the mantra that sex offender treatment alone will never effectively mitigate sexual violence in society, since the problem is not confined to the handful of offenders who spend time in prison and are offered some limited exposure to treatment. Any truly effective model must go well beyond the management of those known to be violent and embrace a comprehensive and integrated approach that begins by recognizing the seeds of sexual violence sown by society. Such a public health paradigm places victims – not offenders – at the center, forcing society to come to address the full gamut of hazards that fuel sexual violence.