A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time

Fertier, Audrey; Barthe‐Delanoë, Anne‐Marie; Montarnal, Aurelie; Truptil, Sébastien; Bénaben, Frédérick

doi:10.1016/j.dss.2020.113260

Cited by 58 publications

(18 citation statements)

References 48 publications

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“…Where, i d is preference of the expert for the i -th scheme;  is the expert weight, ] 1 , 0 [   , determined according to the degree to which experts are recognized in this field. When many experts participate in the scheme decision-making, they will have different preferences for the scheme because of the differences in knowledge structure, professional level and personal experience [35]. If the weight of the k -th expert is k…”

Section: Definitionmentioning

confidence: 99%

Contingency Response Decision of Network Public Opinion Emergencies based on Intuitionistic Fuzzy Entropy and Preference Information of Decision Makers

Sha

Xiao

Zhou

2021

Preprint

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A multi-attribute group decision-making (MAGDM) method based on intuitionistic fuzzy preference information is proposed for the multi-attribute intuitionistic fuzzy group decision-making problem where the decision-makers weight and attribute weight are completely unknown and the decision-maker has preference information for the scheme. Firstly, an intuitionistic fuzzy interval judgment matrix is established to describe the original data of the key decision indicators for multiple network public opinion emergencies that erupt simultaneously. Secondly, the attribute weights are determined based on the improved intuitionistic fuzzy entropy construction method, and the expert weights are determined by using objective decision information, taking into account the intuitionistic fuzzy entropy of decision matrix. Thirdly, a scheme preference model and an attribute weight optimization model are established to determine the ranking method of intuitionistic fuzzy interval values. Then, an improved intuitionistic fuzzy number distance measure is introduced to make the evaluation result more accurate and reasonable when it comes to solving the deviation between the evaluation value and ideal solution of each scheme. Finally, the effectiveness and practicability of the proposed decision-making method are verified by an example of emergency crisis severity, which improves the efficiency of emergency treatment, helps emergency departments to better deal with the network public opinion crisis, improves the ability of public opinion guidance and control, and provides a new method and idea for multi-attribute intuitionistic fuzzy group decision-making problem.

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Section: Definitionmentioning

confidence: 99%

Contingency Response Decision of Network Public Opinion Emergencies based on Intuitionistic Fuzzy Entropy and Preference Information of Decision Makers

Sha

Xiao

Zhou

2021

Preprint

View full text Add to dashboard Cite

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“…First, there are disaster support system papers that determine important quantities and parameters via simulation and/or physical models of the situation [Alvear, Abreu, Cuesta, and Alonso (2013), Cuesta, Alvear, Abreu, and Silió (2014), Sahebjamnia, Torabi, and Mansouri (2017), Eguchi et al (1997), Fikar, Gronalt, andHirsch (2016), Todini (1999), Yilmaz, Aydemir-Karadag, and Erol (2019), Kureshi, Theodoropoulos, Mangina, O'Hare, and Roche (2015), Yang, Guo, and Yang (2019), van Zuilekom, van Maarseveen, and van der Doef (2005)]. Second, DSS papers can also determine parameters via data processing as in Fertier, Barthe-Delanoë, Montarnal, Truptil, andBénaben (2020), Horita, de Albuquerque, Degrossi, Mendiondo, andUeyama (2015), and H. Zhang and Ritchie (1994), or they can utilize machine learning to determine modeling structures as in Abpeykar and Ghatee (2014). Other DSS papers use geographic information system (GIS) techniques to estimate parameters [Cioca, Cioca, and Buraga (2007); Rodríguez-Espíndola, Albores, and Brewster (2018)].…”

Section: Disaster Support Systemsmentioning

confidence: 99%

A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection

Allen¹,

Terekhov²,

Gabriel³

2021

Preprint

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Disaster management is a complex problem demanding sophisticated modeling approaches. We propose utilizing a hybrid method involving inverse optimization to parameterize the cost functions for a road network's traffic equilibrium problem and employing a modified version of Fan and Liu (2010)'s two-stage stochastic model to make protection decisions using the information gained from inverse optimization. We demonstrate the framework using two types of cost functions for the traffic equilibrium problem and show that accurate parameterizations of cost functions can change spending on protection decisions in most of our experiments.

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“…The need to face the suddenness, the complexity, and the chaotic nature of disasters makes disaster response challenging (Devlin, 2006). The key factor for the success of large-scale disaster response is the availability of useful and real-time information to facilitate the decision-making process (Fertier et al, 2020). In fact, emergency responders need accurate and relevant information to be provided in a timely manner in order to allow for appropriate resource deployment and dispatching and to ensure key processes of disaster response, including mass evacuation, are carried out successfully (Bharosa et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Ontology-Driven Multicriteria Decision Support for Victim Evacuation

Elmhadhbi

Karray

Archimède

et al. 2021

Int. J. Info. Tech. Dec. Mak.

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In light of the complexity of unfolding disasters, the diversity of rapidly evolving events, the enormous amount of generated information, and the huge pool of casualties, emergency responders (ERs) may be overwhelmed and in consequence poor decisions may be made. In fact, the possibility of transporting the wounded victims to one of several hospitals and the dynamic changes in healthcare resource availability make the decision process more complex. To tackle this problem, we propose a multicriteria decision support service, based on the Analytic Hierarchy Process (AHP) method, that aims to avoid overcrowding and outpacing the capacity of a hospital to effectively provide the best care to victims by finding out the most appropriate hospital that meets the victims’ needs. The proposed approach searches for the most appropriate healthcare institution that can effectively deal with the victims’ needs by considering the availability of the needed resources in the hospital, the victim’s wait time to receive the healthcare, and the transfer time that represents the hospital proximity to the disaster site. The evaluation and validation results showed that the assignment of hospitals was done successfully considering the needs of each victim and without overwhelming any single hospital.

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A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time

Abstract: A new emergency decision support system: the automatic interpretation and contextualisation of events to model a crisis situation in real-time. (2020) Decision Support Systems, 133. 113260.

Cited by 58 publications

References 48 publications

Contingency Response Decision of Network Public Opinion Emergencies based on Intuitionistic Fuzzy Entropy and Preference Information of Decision Makers

Contingency Response Decision of Network Public Opinion Emergencies based on Intuitionistic Fuzzy Entropy and Preference Information of Decision Makers

A Hybrid Inverse Optimization-Stochastic Programming Framework for Network Protection

Ontology-Driven Multicriteria Decision Support for Victim Evacuation

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