Coupling GIS and Multi-Criteria Modeling to Support Post-Accident Nuclear Risk Evaluation

Mercat-Rommens, C.; Chakhar, Salem; Chojnacki, E.; Mousseau, Vincent

doi:10.1007/978-3-662-46816-6_13

Cited by 5 publications

(7 citation statements)

References 4 publications

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“…In the nuclear domain, GIS techniques have been applied to map radiological hazards, health effects, protective action locations, and economic areas (Hammond & Bier, ; Mercat‐Rommens, Chakhar, Chojnacki, & Mousseau, ; Silva et al., ; Tsai et al., ). GIS has been used to analyze and visualize field data from historical accidents (Van der Perk, Burrough, & Voigt, ), and generate spatial risk analysis based on postulated hazards (Rigina & Baklanov, , ).…”

Section: Methodological Development For External‐explicit Integrationmentioning

confidence: 99%

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

et al. 2018

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In the nuclear power industry, Level 3 probabilistic risk assessment (PRA) is used to estimate damage to public health and the environment if a severe accident leads to large radiological release. Current Level 3 PRA does not have an explicit inclusion of social factors and, therefore, it is not possible to perform importance ranking of social factors for risk‐informing emergency preparedness, planning, and response (EPPR). This article offers a methodology for adapting the concept of social vulnerability, commonly used in natural hazard research, in the context of a severe nuclear power plant accident. The methodology has four steps: (1) calculating a hazard‐independent social vulnerability index for the local population; (2) developing a location‐specific representation of the maximum radiological hazard estimated from current Level 3 PRA, in a geographic information system (GIS) environment; (3) developing a GIS‐based socio‐technical risk map by combining the social vulnerability index and the location‐specific radiological hazard; and (4) conducting a risk importance measure analysis to rank the criticality of social factors based on their contribution to the socio‐technical risk. The methodology is applied using results from the 2012 Surry Power Station state‐of‐the‐art reactor consequence analysis. A radiological hazard model is generated from MELCOR accident consequence code system, translated into a GIS environment, and combined with the Center for Disease Control social vulnerability index (SVI). This research creates an opportunity to explicitly consider and rank the criticality of location‐specific SVI themes based on their influence on risk, providing input for EPPR.

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Section: Methodological Development For External‐explicit Integrationmentioning

confidence: 99%

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

et al. 2018

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“…This idea has been adopted in multicriteria analysis, where severalmulticriterialearning-setbasedmethodshavebeenproposed(e.g. [ [62]). Alearning-setbasedanalysisisparticularityuseful in spare parts management for large firms where tens of thousands of spare parts need to be managed (see, e.g.…”

Section: Learning-set Based Analysismentioning

confidence: 99%

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Chakhar

Siraj

et al. 2017

European Journal of Operational Research

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Classification is one of the critical issues in the operations management of spare parts. The issue of managing spare parts involves multiple criteria to be taken into consideration, and therefore, a number of approaches exists that consider criteria such as criticality, price, demand, lead time, and obsolescence, to name a few. In this paper, we first review proposals to deal with inventory control. We then propose a three-phase multicriteria classification framework for spare parts management using the dominance-based rough set approach (DRSA). In the first phase, a set of 'if-then' decision rules is generated from historical data using the DRSA. The generated rules are then validated in the second phase by using both the automated and manual approaches, including cross-validation and feedback assessments by the decision maker. The third and final phase is to classify an unseen set of spare parts in a real setting. The proposed approach has been successfully applied to data collected from a manufacturing company in China. The proposed framework was practically tested on different spare parts and, based on the feedback received from the industry experts, 96% of the spare parts were correctly classified. Furthermore, the crossvalidation results show that the proposed approach significantly outperforms other well-known classification methods. The proposed approach has several important characteristics that distinguish it from existing ones: (i) it is a learning-set based analysis approach; (ii) it uses a powerful multicriteria classification method, namely the DRSA; (iii) it validates the generated decision rules with multiple strategies; and (iv) it actively involves the decision maker during all the steps of the decision-making process.

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“…In some situations, aggregating the input data is simply based on discussion among different decision makers as in Damart et al (2007), Saad et al (2005) and Mercat-Rommens et al (2010). In the latter paper, for instance, although several decision makers have been implied, the collective input has been defined by the responsible project manager based on her expertise and the information presented by different decision makers.…”

Section: Aggregation Rulesmentioning

confidence: 99%

“…The objective of the third stage is to use the obtained results to apply the aggregation rule. This approach has been used in different real-world decision problems, e.g., Chakhar and Saad, 2012;Damart et al, 2007;Guay et al, 2011;Mercat-Rommens et al 2010. The case-based reasoning approach has two main advantages. First, it substantially reduces computing time.…”

Section: Preference Parameters Elicitation Approachmentioning

confidence: 99%

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Incorporating stakeholders’ knowledge in group decision-making

Chakhar

Saad

2014

Journal of Decision Systems

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The objective of this paper is to characterize the group decision-making process. Three basic questions should be considered in group decision-making: (i) how to extract stakeholders' knowledge and preferences, (ii) how to combine these preferences and knowledge coherently and (iii) how to conduct discussions and resolve conflicts. To respond to these questions, this paper distinguishes and discusses several elements characterizing the group decision-making process. More specifically, the paper discusses the following topics: techniques for acquiring stakeholders' knowledge, aggregation strategies and rules, preference parameters elicitation techniques, stakeholder weighting and conflict resolution. The paper also includes a brief discussion of several real-world case studies in which the authors of this study participated.

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Coupling GIS and Multi-Criteria Modeling to Support Post-Accident Nuclear Risk Evaluation

Cited by 5 publications

References 4 publications

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

GIS‐Based Integration of Social Vulnerability and Level 3 Probabilistic Risk Assessment to Advance Emergency Preparedness, Planning, and Response for Severe Nuclear Power Plant Accidents

Spare parts classification in industrial manufacturing using the dominance-based rough set approach

Incorporating stakeholders’ knowledge in group decision-making

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