scholarly journals Minimization of Mean-CVaR Evacuation Time of a Crowd using Rescue Guides: a Scenario-based Approach

2021 ◽  
Vol 6 ◽  
Author(s):  
Anton Von Schantz ◽  
Harri Ehtamo ◽  
Simo Hostikka
Keyword(s):  
Public Space ◽  
Value At Risk ◽  
Optimal Solution ◽  
Conditional Value At Risk ◽  
Force Model ◽  
Worst Case ◽  
Evacuation Time ◽  
Scenario Optimization ◽  
The Mean ◽  
Evacuation Plan

In case of a threat in a public space, the crowd in it should be moved to a shelter or evacuated without delays. Risk management and evacuation planning in public spaces should also take into account uncertainties in the traffic patterns of crowd flow. One way to account for the uncertainties is to make use of safety staff, or guides, that lead the crowd out of the building according to an evacuation plan. Nevertheless, solving the minimum time evacuation plan is a computationally demanding problem. In this paper, we model the evacuating crowd and guides as a multi-agent system with the social force model. To represent uncertainty, we construct probabilistic scenarios. The evacuation plan should work well both on average and also for the worst-performing scenarios. Thus, we formulate the problem as a bi-objective scenario optimization problem, where the mean and conditional value-at-risk (CVaR) of the evacuation time are objectives. A solution procedure combining numerical simulation and genetic algorithm is presented. We apply it to the evacuation of a fictional passenger terminal. In the mean-optimal solution, guides are assigned to lead the crowd to the nearest exits, whereas in the CVaR-optimal solution the focus is on solving the physical congestion occurring in the worst-case scenario. With one guide positioned behind each agent group near each exit, a plan that minimizes both objectives is obtained.

ASSET CORRELATIONS IN TURBULENT MARKETS AND THE IMPACT OF DIFFERENT REGIMES ON ASSET MANAGEMENT

2011 ◽  
Vol 28 (01) ◽  
pp. 1-23 ◽  
Author(s):  
GERMAN BERNHART ◽  
STEPHAN HÖCHT ◽  
MICHAEL NEUGEBAUER ◽  
MICHAEL NEUMANN ◽  
RUDI ZAGST
Keyword(s):  
Risk Aversion ◽  
Asset Management ◽  
Value At Risk ◽  
Conditional Value At Risk ◽  
Dependence Structure ◽  
The Us ◽  
The Mean ◽  
Asian Market ◽  
Asset Classes ◽  
The Impact

In this article, the dependence structure of the asset classes stocks, government bonds, and corporate bonds in different market environments and its implications on asset management are investigated for the US, European, and Asian market. Asset returns are modelled by a Markov-switching model which allows for two market regimes with completely different risk-return structures. Using major stock indices from all three regions, calm and turbulent market periods are identified for the time period between 1987 and 2009 and the correlation structures in the respective periods are compared. It turns out that the correlations between as well as within the asset classes under investigation are far from being stable and vary significantly between calm and turbulent market periods as well as in time. It also turns out that the US and European markets are much more integrated than the Asian and US/European ones. Moreover, the Asian market features more and longer turbulence phases. Finally, the impact of these findings is examined in a portfolio optimization context. To accomplish this, a case study using the mean-variance and the mean-conditional-value-at-risk framework as well as two levels of risk aversion is conducted. The results show that an explicit consideration of different market conditions in the modelling framework yields better portfolio performance as well as lower portfolio risk compared to standard approaches. These findings hold true for all investigated optimization frameworks and risk-aversion levels.

scholarly journals Risk-based constraints with correlated uncertainties for the optimal operation of an energy community

2021 ◽  
Author(s):  
Mihály Dolányi ◽  
Kenneth Bruninx ◽  
Jean-François Toubeau ◽  
Erik Delarue
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Measurement Data ◽  
Real Life ◽  
Optimal Operation ◽  
Conditional Value At Risk ◽  
Forecast Errors ◽  
Worst Case ◽  
Life Measurement ◽  
Out Of Sample

<div>This paper formulates an energy community's centralized optimal bidding and scheduling problem as a time-series scenario-driven stochastic optimization model, building on real-life measurement data. In the presented model, a surrogate battery storage system with uncertain state-of-charge (SoC) bounds approximates the portfolio's aggregated flexibility. </div><div>First, it is emphasized in a stylized analysis that risk-based energy constraints are highly beneficial (compared to chance-constraints) in coordinating distributed assets with unknown costs of constraint violation, as they limit both violation magnitude and probability. The presented research extends state-of-the-art models by implementing a worst-case conditional value at risk (WCVaR) based constraint for the storage SoC bounds. Then, an extensive numerical comparison is conducted to analyze the trade-off between out-of-sample violations and expected objective values, revealing that the proposed WCVaR based constraint shields significantly better against extreme out-of-sample outcomes than the conditional value at risk based equivalent.</div><div>To bypass the non-trivial task of capturing the underlying time and asset-dependent uncertain processes, real-life measurement data is directly leveraged for both imbalance market uncertainty and load forecast errors. For this purpose, a shape-based clustering method is implemented to capture the input scenarios' temporal characteristics.</div>

Analysis of freight train collision risk in the United States

2018 ◽  
Vol 233 (8) ◽  
pp. 817-830 ◽  
Author(s):  
Tejashree Turla ◽  
Xiang Liu ◽  
Zhipeng Zhang
Keyword(s):  
United States ◽  
Value At Risk ◽  
The United States ◽  
Mitigation Strategies ◽  
Conditional Value At Risk ◽  
Collision Risk ◽  
Worst Case ◽  
Freight Train ◽  
The Us ◽  
Traffic Exposure

Rail transportation is pivotal for the national economy. Despite being rare, a train accident can potentially result in severe consequences, such as infrastructure damage costs, casualties, and environmental impacts. An understanding of accident frequency, severity, and risk is important for rail safety management. In the United States, extensive prior research has focused on risk analyses of train derailments and highway–rail grade crossing accidents. Relatively less work has been conducted regarding train collision risk. The US Federal Railroad Administration identifies various accident causes, among which the authors of this study have analyzed the major collision causes. For each major accident cause, the authors have analyzed its resultant collision frequency, severity (in terms of damage cost or casualties), and correspondingly the risk, which is the combination of the frequency and severity. The analysis was based on train collision data in the United States from 2001 to 2015. This analysis focuses on freight trains in the United States, due to their immense traffic exposure. On the temporal scale, collision rate (the number of collisions normalized by traffic exposure) has an approximately 5% annual reduction. In terms of collision cause, failures to obey signals, overspeeds, and violations of mainline operating rules accounted for more collisions than other causes. Two alternative risk measures, namely the expected consequence and conditional value at risk, were used to evaluate the freight train collision risk on main tracks, accounting for both the average and worst-case scenarios. This collision risk analysis methodology may provide the US Department of Transportation and railroad industry with information and decision support for identifying, evaluating, and implementing cost-effective risk mitigation strategies.

scholarly journals A Bi-Objective Portfolio Optimization with Conditional Value-at-Risk

2010 ◽  
Vol 4 (2) ◽  
pp. 47-69 ◽  
Author(s):  
Bartosz Sawik
Keyword(s):  
Value At Risk ◽  
Stock Exchange ◽  
Risk Measure ◽  
Performance Measure ◽  
Conditional Value At Risk ◽  
Programming Approach ◽  
Expected Return ◽  
Worst Case ◽  
Warsaw Stock Exchange ◽  
The Relationship

This paper presents a bi-objective portfolio model with the expected return as a performance measure and the expected worst-case return as a risk measure. The problems are formulated as a bi-objective linear program. Numerical examples based on 1000, 3500 and 4020 historical daily input data from the Warsaw Stock Exchange are presented and selected computational results are provided. The computational experiments prove that the proposed linear programming approach provides the decision maker with a simple tool for evaluating the relationship between the expected and the worst-case portfolio return.

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