Modeling uncertain and dynamic casualty health in optimization-based decision support for mass casualty incident response

When designing a decision support program for use in coordinating the response to Mass Casualty Incidents, the modelling of the health of casualties presents a significant challenge. In this paper we propose one such health model, capable of acknowledging both the uncertain and dynamic nature of casualty health. Incorporating this into a larger optimisation model capable of use in real-time and in an online manner, computational experiments examining the effect of errors in health assessment, regular updates of health and delays in communication are reported. Results demonstrate the often significant impact of these factors

Online optimization of casualty processing in major incident response: An experimental analysis

When designing an optimization model for use in mass casualty incident (MCI) response, the dynamic and uncertain nature of the problem environment poses a significant challenge. Many key problem parameters, such as the number of casualties to be processed, will typically change as the response operation progresses. Other parameters, such as the time required to complete key response tasks, must be estimated and are therefore prone to errors. In this work we extend a multi-objective combinatorial optimization model for MCI response to improve performance in dynamic and uncertain environments. The model is developed to allow for use in real time, with continuous communication between the optimization model and problem environment. A simulation of this problem environment is described, allowing for a series of computational experiments evaluating how model utility is influenced by a range of key dynamic or uncertain problem and model characteristics. It is demonstrated that the move to an online system mitigates against poor communication speed, while errors in the estimation of task duration parameters are shown to significantly reduce model utility

Demand-driven sustainable tourism? A choice modelling analysis

This paper studies the preferences of tourists visiting Sardinia (Italy), using a choice modelling approach. The focus is on the evaluation of specific ‘demand-enhancing effects’ which, according to economic theory, provide a basis for implementing sustainable tourism policies. Multinomial logit estimates reveal that strong negative effects result from the congestion of tourist attractions and the transformation of coastal environments, though tourists clearly gain utility from the other components of a tourism destination. The extent of the effects related to environmental preservation seems to support planning tourism development policies that will not have strong irreversible effects on coastal areas

Tourism destination competitiveness: second thoughts on the world economic forum reports

The Travel and Tourism Competitiveness Reports of the World Economic Forum elaborate the Travel and Tourism Competitiveness Index (TTCI) as an overall measure of destination competitiveness for 130 economies worldwide. From a tourism management point of view, a measure such as the TTCI is expected to be instrumental in explaining and predicting the tourism performance of receiving countries. This study explores several ways to transform the TTCI into a formative structural model. Partial least squares path modelling, PLS regression, mixture modelling and non-linear covariance-based structural equation modelling are applied to examine the TTCI's predictive power. The analysis probes possible measures for improvement. The destination countries may be subject to unobserved heterogeneity with regard to how the various constituents of competitiveness act on tourism performance. Interaction phenomena seem to prohibit a simple cause-effect pattern and non-linear relationships show encouraging results

A meta-analysis of international tourism demand forecasting and implications for practice

Numerous studies on tourism forecasting have now been published over the past five decades. However,no consensus has been reached in terms of which types of forecasting models tend to be more accurate and in which circumstances. This study uses meta-analysis to examine the relationships between the accuracy of different forecasting models, and the data characteristics and study features. By reviewing 65 studies published during the period 1980-2011, the meta-regression analysis shows that the origins of tourists, destination, time period, modeling method, data frequency, number of variables and their measures and sample size all significantly influence the accuracy of forecasting models. This study is the first attempt to pair forecasting models with the data characteristics and the tourism forecasting context.The results provide suggestions for the choice of appropriate forecasting methods in different forecasting settings

Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident

During a major incident, the emergency services work together to ensure that those casualties who are critically injured are identified and transported to an appropriate hospital as fast as possible. If the incident is multi-site and resources are limited, the efficiency of this process is compromised as the finite resources must be shared among the multiple sites. In this paper, agent-based simulation is used to determine the allocation of resources for a two-site incident which minimizes the latest hospital arrival times for critically injured casualties. Further, how the optimal resource allocation depends on the distribution of casualties across the two sites is investigated. Such application supports the use of agent-based simulation as a tool to aid emergency response

Agent-based simulation for large-scale emergency response: a survey of usage and implementation

When attempting to determine how to respond optimally to a large-scale emergency, the ability to predict the consequences of certain courses of action in silico is of great utility. Agent-based simulations (ABSs) have become the de facto tool for this purpose; however, they may be used and implemented in a variety of ways. This article reviews existing implementations of ABSs for large-scale emergency response, and presents a taxonomy classifying them by usage. Opportunities for improving ABS for large-scale emergency response are identified

Scheduling Response Operations under Transport Network Disruptions

Modeling the complex decision problems faced in the coordination of disaster response as a scheduling problem to be solved using an optimization algorithm has the potential to deliver efficient and effective support to decision makers. However, much of the utility of such a model lies in its ability to accurately predict the outcome of any proposed solution. The stochastic nature of the disaster response environment can make such prediction difficult. In this paper we examine the effect of unknown disruptions to the road transport network on the utility of a disaster response scheduling model. The effects of several levels of disruption are measured empirically and the potential of using real-time information to revise model parameters, and thereby improve predictive performance, is evaluated

Improving agent-based simulation of major incident response in the United Kingdom through conceptual and operational validation

The aim of this paper is to report on how the credibility of an agent-based model (ABM) of the United Kingdom emergency services' response to major incidents has been improved through a process of conceptual validation, and how the ABM's software implementation has been improved through a process of operational validation. Validating the authors' ABM and its implementation contributes towards the long term goal of agent-based modelling and simulation being accepted by emergency planning officers as a means of performing emergency exercises thus playing a useful role in emergency preparedness. Both conceptual and operational validation led to the identification of potential improvements, which when implemented resulted in the authors' ABM software simulating the response to major incidents in the UK more realistically than was possible previously