Multiple Criteria Assessment of Insulating Materials with a Group Decision Framework Incorporating Outranking Preference Model and Characteristic Class Profiles

We present a group decision making framework for evaluating sustainability of the insulating materials. We tested thirteen materials on a model that was applied to retrofit a traditional rural building through roof's insulation. To evaluate the materials from the socio-economic and environmental viewpoints, we combined life cycle costing and assessment with an adaptive comfort evaluation. In this way, the performances of each coating material were measured in terms of an incurred reduction of costs and consumption of resources, maintenance of the cultural and historic significance of buildings, and a guaranteed indoor thermal comfort. The comprehensive assessment of the materials involved their assignment to one of the three preference-ordered sustainability classes. For this purpose, we used a multiple criteria decision analysis approach that accounted for preferences of a few tens of rural buildings' owners. The proposed methodological framework incorporated an outranking-based preference model to compare the insulating materials with the characteristic class profiles while using the weights derived from the revised Simos procedure. The initial sorting recommendation for each material was validated against the outcomes of robustness analysis that combined the preferences of individual stakeholders either at the output or at the input level. The analysis revealed that the most favorable materials in terms of their overall sustainability were glass wool, hemp fibres, kenaf fibres, polystyrene foam, polyurethane, and rock wool

Multi-criteria Human Resources Planning Optimisation Using Genetic Algorithms Enhanced with MCDA

The main objective of this paper is to present an example of the IT system implementation with advanced mathematical optimisation for job scheduling. The proposed genetic procedure leads to the Pareto front, and the application of the multiple criteria decision aiding (MCDA) approach allows extraction of the final solution. Definition of the key performance indicator (KPI), reflecting relevant features of the solutions, and the efficiency of the genetic procedure provide the Pareto front comprising the representative set of feasible solutions. The application of chosen MCDA, namely elimination et choix traduisant la réalité (ELECTRE) method, allows for the elicitation of the decision maker (DM) preferences and subsequently leads to the final solution. This solution fulfils all of the DM expectations and constitutes the best trade-off between considered KPIs. The proposed method is an efficient combination of genetic optimisation and the MCDA method. (original abstract

Classification models for the risk assessment of energy accidents in the natural gas sector

Several initiatives have been proposed nationally and internationally to collect information on accidents in the energy sector, assuming that a detailed, integral and targeted analysis of them can reveal the weak points in the energy infrastructure. The influence and relevance of the descriptors (e.g., country, energy chain, infrastructure type) of energy accidents on the outcome, such as fatalities, has so far not been performed on a comparative basis. This paper presents the first attempt to explore these relationships. Furthermore, it contributes to the resilience literature by exploring the capacity of an energy accidents dataset in storing and retrieving information on past events to tackle the forthcoming ones with more awareness of the possible impacts. This research employed a knowledge extraction method (i.e., rough set analysis) to analyse data on energy accidents for natural gas from the most authoritative information source for accidents in the energy sector, i.e., the ENergy-related Severe Accident Database (ENSAD). The main goal of this paper is to show that the rough set analysis can have a substantial contribution in understanding (i) the capacity of the structure of ENSAD to distinguish the accidents with respect to objective measures of outcome; (ii) the decision rules that clearly and simply explain the combination of attributes’ values and outcome, in this case fatality ranges; and (iii) how the rules can guide the decision-making process when there is an interest in knowing which class (i.e., low, medium, high) of fatalities an energy accident with a specific set of descriptors could have