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Emergency situations may differ in many ways but they share some common characteristics, such as the sudden onset and the need to transparently evaluate various usually conflicting objectives. In nuclear power generation, however, emergency situations constitute a special challenge. The focus of this paper is to highlight the role of multi‐criteria decision analysis (MCDA) in nuclear emergency and recovery management on the basis of a hypothetical case study.

Multi‐attribute value theory as one field of research within MCDA is introduced. Special emphasis is placed on the modelling of the decision makers' preferences which is a crucial part in any multi‐criteria analysis. A central aim is to facilitate the preference elicitation in group decision processes.

The management of emergency situations in nuclear power generation necessitates the consideration of technical, economic, environmental, socio‐psychological and political aspects. Furthermore, various stakeholder and expert groups with diverse background knowledge and different views, responsibilities and interests are involved in such a decision‐making process. MCDA can help to take into account various incommensurable aspects and the subjective preferences of the decision makers and thus contribute to transparency and traceability of decision‐making processes. Since the preference parameters are inherently afflicted with uncertainties, thorough sensitivity analyses are important to visualise the impact of the uncertainties in an understandable way.

A new approach to sensitivity analysis is proposed, allowing one to comprehensibly visualise and communicate the impact of the uncertainties associated with the subjective preference parameters on the results of the decision analysis.

Based on the theoretical viewpoints of criminal geography and environmental criminology, this research uses spatial multi-criteria decision-making methods. In the process of spatial decision-making and optimization of police resources, researchers fully consider the dynamic application of Geographic Information System (GIS) and the effects of spatial prevention and control.

Researchers use an integrated method combining Policing Geographic Information System (PGIS) and multi-criteria decision analysis (MCDA). On the one hand, police GIS has an excellent visual data analysis platform and integrated decision support system in data management, spatial analysis, data exploration and regression analysis. On the other hand, through the design of the indicator system, the quantification of indicators, the determination of weights, comprehensive evaluation and sensitivity analysis, MCDA can select the best plan from a large number of alternatives. When joining MCDA, the spatial dimension will bring the research results closer to the real world.

The study finds that the crime of burglary is affected to a certain extent by the distribution of police forces, the location of police units. Another important finding of this research is the correlation between more precise preventive measures and the crime of burglary.

From a practical point of view, this research would help advance the role of police units and law enforcement agencies in preventing burglary crimes and provide experience for the allocation of regional police resources.

This study aims to focus on the application of a multiple criteria decision analysis (MCDA) method to compare results presented by the Integrated Goals System based on the 12th edition of the Integrated Public Safety Areas (IPSAs) Award, which achieved goals established for Strategic Crime Indicators for the state of Rio de Janeiro, Brazil. The main objective of this research was to limit the compensatory effects of classification criteria on IPSAs that have achieved goals established for crime indicators by applying the MCDA method.

The methodology was based on the application of MCDA. The MCDA method selected and used was ELECTRE III, being implemented by J-Electre software.

Compared to results of the current method, the results of the ELECTRE III method showed a 94.87 per cent change in ranking positions revealed via the SIM method. This finding denotes the elimination of compensatory effects of the criteria. As a consequence, it can be affirmed that the application of resources by IPSA managers to reduce the prevalence of a single strategic crime indicator is no longer a success factor for awarding in the established goal system.

As limitations, it is possible to indicate the time cut used to carry out the research. The research may be extended to other issues of the productivity award.

The methodology applied with the use of ELECTRE III revealed that the government could reduce the cost with the incentive program to reduce criminal indices.

As the MCDA method is based on a binary pairwise comparison system, the methodology imposes a change of attitude on local managers fighting crime to reduce crime indicators and to consequently build a local sense of safety in IPSAs.

This research fills a gap in the literature because there are few studies using the MCDA method in the field of public security. The value of the work lies in the creation of a method that gives the decision-maker, of the law enforcement agency, an alternative to improve the process of rewarding by productividad of the integrated areas of public security.

Supplier selection is a complex and strategic activity needed in every organization, involving many stakeholders and different attributes as price, delivery performance, and product quality. Globalization, in the last decades, increased the competitiveness between vendors, enhancing the use of decision models to support the best choice based on optimizations and bidding variations due to specific needs. This chapter presents three models of multi-dimensional auctions to improve an international humanitarian NGO process procurement efficiency by reducing procurement costs and the decision-making process time. These models have the advantage to be easily implementable in typically complex environments where there is a large number of categories, suppliers, and other features.

The first proposed model uses combinatorial auctions and is suited for procurement, where suppliers can benefit from cost complementarity. The second one uses volume discount auctions and is suited for volumetric purchases, where discounts for large quantities are common. The third one is a multi-attribute model, which computes the best possible solution considering several criteria and can be used in case of complex purchases that involve various categories and trade-offs and are subject to spot prices.

Several design considerations for this type of auctions are reviewed, as well as the mathematical formulation to determine the best alternative (i.e., winner) that can be solved using simple tools like Microsoft Excel. The models are optimized by a mixed-integer programming, and the multi-attribute one is developed using multi-criteria decision analysis (MCDA). All three models developed in this research showed superior results compared to the baseline, being between 9% and 20% more efficient than a regular supplier selection (singly choosing the lowest price) and improving the bidding compliance.

The purpose of this paper is to propose and test a multi-criteria decision analysis (MCDA) approach based on an additive value function (AVF) to select the most economically advantageous tender under European Union public procurement regulations.

A case study in which the AVF tender evaluation model is constructed by the procurement personnel and the results of the original, real-life public procurement evaluation model are compared to those discovered by the MCDA approach.

The AVF model captures the preferences of the procurement authority in a more reliable and transparent manner than commonly used evaluation models based on scoring formulas.

While commonly used in public procurement, relative scoring formulas can neither present the preferences of a procurement unit accurately nor do they enable bidders to draft bids according to these preferences. The proposed MCDA approach can achieve both.

The contribution of this paper is threefold. First, the successful construction of the AVF model with procurement personnel is introduced. Second, the model is used in an actual, real-life case. Third, a thoughtful comparison of features, structures and results of the AVF model and the evaluation model using scoring formulas is presented.

Reducing greenhouse gas emissions from fossil fuel consumption is a big challenge on the view of global warming and climate changes caused by greenhouse gases as per recent scientific reports. This paper aims to identify the major challenges of green energy sources (GES) to the future power systems and suggests an appropriate GES based on the preference by the decision maker on the various issues to meet these challenges.

The proposed work presents a multi‐criteria decision analysis (MCDA) – the analytic hierarchy process (AHP) to evaluate the GES – photovoltaic (PV), wind generator (WG), biomass (BM) and micro‐hydel (MH) and to find the appropriate selection in general, by evaluating its main operational characteristic. In this research, the choices of the green energy alternatives on the basis of various factors have been taken into consideration. MATLAB simulation of different criteria to ascertain their clear‐cut effects on GES selection under multiple uncertainties are presented.

Related articles appearing in the recently published (1995‐2010) works, based on green energy scope and practical implementations, and earlier approaches in the field of energy are gathered and analyzed so that the following questions can be answered: Which evaluating criteria are paid more attention to? Which source is the best GES? Which is the most critical factor in the green energy selection. This research not only provides the application of MCDA to evaluate the operation of the GES – PV, WG, BM and MH but also aids the researchers and decision makers in applying the approaches effectively.

This is the first analysis in the green energy selection which considers future uncertainties of the GES. Instead of focusing only on cost factor, the proposed work considers main uncertainties of the GES. The best GES will be decided based on the preference of the criterion chosen by the end‐user.

Achieving a zero-carbon city requires a long-term strategic perspective. The authors propose a decision-making model which would take into account the economic, environmental and social impacts for prioritizing the zero-carbon measures for sustainable urban transportation.

An integrated intuitionistic fuzzy gained and lost dominance score (IF-GLDS) model is introduced based on intuitionistic fuzzy Yager weighted aggregation (IFYWA) operators and proposed weight-determining IF-SPC procedure. In addition, a weighting tool is presented to obtain the weights of decision experts. Further, the feasibility and efficacy of developed IF-SPC-GLDS model is implemented on a multi-criteria investment company selection problem under IFS context.

The results of the developed model, “introducing zero-emission zones” should be considered as the first measure to implement. The preference of this initiative offers sustainable transport in India to achieve a zero-carbon transport by having the greatest impact on the modal shift from cars to sustainable mobility modes with a lower operational and implementation cost as well as having greater public support. The developed model utilized can be relocated to other smart cities which aim to achieve a zero-carbon transport. Sensitivity and comparative analyses are discussed to reveal the robustness of obtained result. The outcomes show the feasibility of the developed methodology which yields second company as the suitable choice, when compared to and validated using the other MCDA methods from the literature, including TOPSIS, COPRAS, WASPAS and CoCoSo with intuitionistic fuzzy information.

A new intuitionistic fuzzy symmetry point of criterion (IF-SPC) approach is presented to find weights of criteria under IFSs setting. Then, an IF-GLDS model is introduced using IFYWA operators to rank the options in the realistic multi-criteria decision analysis (MCDA) procedure. For this purpose, the IFYWA operators and their properties are developed to combine the IFNs. These operators can offer a flexible way to deal with the realistic MCDA problems with IFS context.

Many problems in science and engineering fields involve decision making. Usually these decision‐making processes are based on several criteria that represent various experts' knowledge. Stakeholder prioritization is useful for assisting in decision‐making situations where various stakeholders have competing interests, resources are limited, and stakeholder requirements must be appropriately balanced. When these conflicts arise it is important to the success of the organization that it has prioritized each stakeholder according to the situation. To date, few researchers tried to resolve this question, mostly are based on intuitive and very simple reasoning methods which are error prone. The purpose of this paper is to propose a multi‐criteria decision analysis process to help decision makers when evaluating and prioritizing stakeholders.

In this process, Mitchell et al.'s model is used for identifying criteria on which stakeholders will be evaluated and the fuzzy Choquet integral as an aggregation operator. This research also tested and discussed the proposal using a case study from Toulouse city subway.

The results show the applicability of this process and the effectiveness of using the fuzzy Choquet integral than a traditional multi‐criteria evaluation method for human subjective evaluation, or when criteria are not mutually independent.

The highly subjective nature of criteria weights and rapid elicitation can lead to questions of validity. Also, results are not always widely accepted.

The paper is original in considering the stakeholder prioritization problem as a multi‐criteria decision analysis problem; using a simple and well‐known model to classify stakeholders, i.e Mitchell et al.'s model; and in using Choquet integral as an aggregation operator which allows considering interaction between criteria.

The study aimed to use geographic information system (GIS) based multi-criteria decision making analysis (GIS-MCDA) to select areas suitable for siting landfills in Ashanti region. It also sought to ascertain variables most sensitive to the siting of landfill in the region.

This study utilized GIS-based Multi-criteria decision making analysis –AHP to model and select areas most suitable to siting landfills within the region. Overall, 16 variables including wind speed and hydraulic conductivity (which were previously neglected in landfill siting in Ghana) were identified through comprehensive literature review. These variables were weighted using AHP method and integrated using the weighted linear combination (WLC) in GIS environment to develop five sub-models: the physical environmental, sociocultural, economic/technical, climatic and hydrogeological sub-models. These sub-models were further weighted and then integrated to derive the final suitability model.

Results show that 13% (3,067 km²) of the region was identified as most suitable to siting engineered landfills. The study also identified 11 sites which are considered most suitable for situating landfills. On a sensitivity angle, hydrogeological (R² = 0.5923; p = 0.003) and physical environmental sub-model (R² = 0.254; p = 0.034) significantly predicted the final suitability model developed.

Ghana's Landfill Guidelines seeks to optimize site selection and ancillary services that culminate into achieving sanitary landfills by 2020. Evidence still abounds on the unsuitability of existing and in some cases, new landfill sites presenting environmental and social negative impacts. The comprehensive evaluation of most crucial variables – social and environmental factors that determine an optimal landfill location – will be of immense help to policy planners like the Environmental Protection Agency (EPA) towards upgraded landfills. The authors hope that, concerned agencies will adopt the model in the study and integrate into their existing landfill suitability modeling techniques to provide a more grounded framework that optimizes landfill site selection within the study area.

This study is the first attempt to consider a regional-level waste collection site selection in Ghana using comprehensive sets of social and environmental factors and will therefore contribute immensely to EPA's goal of achieving upgraded landfills by 2022.

The cost-optimal analysis is not able to address the multi-dimensionality of the decision according to the new European objectives and International sustainable development goals in the field of the nearly-zero energy building (NZEB) design. The purpose of this paper is to study the role of multi-criteria decision analysis (MCDA) for guiding energy investment decisions.

The paper explores the Preference ranking organization method for enrichment of evaluations II (PROMETHEE II) application to support the project of transforming a rural building into a NZEB. The evaluation provides an estimate of the effects of alternative energy efficiency measures, involving energy consumption, life cycle costs, carbon emissions, property value and indoor comfort criteria. The study performs a multi-actors analysis in order to understand how different consumers' point of views can influence the final choice of the best investment. Furthermore, a multi-site analysis explores the spatial variation of NZEB building appreciation in the real estate market.

The PROMETHEE II-based model ranks 16 alternative solutions for the NZEB according to energy, economic and extra-economic criteria. The multi-actors analysis highlights the configuration of the NZEB building that best meets the needs of different end-users, respecting the European directives and national standards. The multi-site analysis concludes that location does not change users' appreciation and not influence the output for the best solution.

The MCDA occurs as a support tool that helps to optimize the preliminary design phase of NZEB through the exploration of the optimal solution considering crucial criteria in the energy and environmental and real estate market rules.