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This paper aims to test and compare two innovative methodologies (utility additive and evolutionary polynomial regression) for mass appraisal of residential properties. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits.

With reference to the same case studies, concerning samples of residential properties recently sold in three Italian cities, the two procedures are tested and the results are compared. The first method is the utility additive, which interprets the process of the property price formation as a multi-criteria selection of multi-objective typology, where the selection criteria are the property characteristics that are decisive in the real estate market; the second method is a hybrid data-driven technique, called evolutionary polynomial regression, that uses multi-objective genetic algorithms to search those models expressions that simultaneously maximize accuracy of data and parsimony of mathematical functions.

The outputs obtained from the experimentation highlight the potentialities and the limits of the two methodologies, as well as the possibility of jointly applying them to interpret and predict the real estate phenomena in a more realistic representation.

In all countries, mass appraisal techniques have become strategic for the definition of management and enhancement policies of public and private property assets, in the case of investments of technical and economic refunctionalization (energy, environment, etc.), and for the alienation of buildings no longer suitable for public needs (military barracks, hospitals, areas in disuse, etc.). In this context, the use of mass appraisal techniques for residential properties assumes a leading role for sector operators (buyers, sellers, institutions, insurance companies, banks, real estate funds, etc.). Therefore, the results of the applications outline the potentialities of the two methodologies implemented and the opportunity of further insights of the topics that have been dealt with in this research.

The purpose of this paper is to analyze the impacts of Portfolio size effect due to scaling issues in the outcome obtained in a project portfolio selection for an electricity company in Brazil, focusing on improving business strategic performance.

The study uses a web-based decision support system (DSS), in which scaling issues are considered, incorporating results of previous work. The study evaluates 32 projects from the electricity company and compared the possible results when considering different scales. Additionally, a sensitivity analysis was conducted to analyze the robustness of the case, using the web-based DSS.

The results for an interval scale context showed a portfolio with 21 projects, contrasting with the correct solution of a portfolio containing 23 projects. The latter is related to a ratio scale context, with the proper transformation of weights, which was found to be robust with a sensitivity analysis using Monte Carlo simulation. This demonstrates that only appropriate models for selecting projects can improve the contribution to the company’s permanent strategies of increasing productivity, considering its constraints to achieve optimal results.

Additive value functions approach imposes certain requirements on the measurement scales used for the items in a portfolio that should not be ignored, once they have significant impact on the general portfolio results, which are directly related to the business strategic performance and the facilities of doing that with a web-based DSS.

The Renewable Energy Directive 2009/28/EC of the European Union provides another element to cross-border cooperation by allowing Member States to fulfill their 2020 renewable energy sources (RES) targets by implementing joint projects in third countries through the cooperation mechanisms. The purpose of this paper is to assess the country risk, to support bilateral cooperation for RES electricity generation projects.

A multicriteria decision support methodology has been developed taking into account three evaluation parameters, namely, the investment framework, the social conditions and the energy and technological status. An additive value model has been constructed, and the UTilitès Additives (UTA) – UTA* (UTASTAR) disaggregation method has been implemented to infer the criteria weights. The obtained ranking of alternatives has been subjected to robustness analysis, and finally the proposed methodology has been applied to five North Africa countries, so as to draw key results.

The pilot application of the methodological approach proposed and the model developed was fully compatible with the decision maker’s ranking on a set of fictitious countries and facilitated the assessment of a country’s current situation with regards to its investment, social conditions and energy and technological status. The results regarding the five North African countries examined, indicated the country’s investment framework as the most important factor, from foreign investors’ perspective, affecting a country’s suitability for the implementation of RES projects through a cooperation mechanism and Morocco, as well as Tunisia as the countries with the most suitable conditions for a successful implementation of such projects.

To the best of authors’ knowledge, there are only very few studies trying to assess opportunities and risks emerging from the implementation of joint projects between European and third countries in the field of electricity generation from RES. There are even less studies using (UTASTAR) method on real-world decision-making problems, and almost none are dedicated to energy sector-related problems.

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.

This paper investigates the financial strength of banks in Bangladesh and factors affecting the financial strength over the years 2010–2015 on 35 banks.

Additive value function with CAMEL rating (capital stength, asset quality, managerial efficiency, earning ability, liquidity) has been employed to calculate banks’ financial strength index (FSI). In the second stage, panel regression has been exercised to find out the determinants of banks’ financial strength.

Empirical finding exhibits that the Islamic banks of Bangladesh are financially stronger and outperform conventional and Islamic window banks with higher liquidity. In the ownership category, private banks have more financial strength with higher capital strength, asset quality, managerial efficiency and earning ability than public banks. Bank size, loan recovery, salary and banking sector development positively affect whereas the loan-asset negatively affect the bank’s financial strength in Bangladesh.

This study has its limitations despite its importance. CAMELS is a more improved form than using CAMEL. But because of the data deficiency on “S” which represents sensitivity, it would not be possible to use CAMELS framework. Further researchers could incorporate this.

Government and banks should allow Islamic banks to enter the market on easy terms because of their outstanding performance in the existing market. In addition, banks should provide loans with consideration so that they cannot create credit risk. In addition, they should calculate composite financial strength annually to understand which components they need to work on.

This study extends the extant result on the composite FSI. It is hard to examine the financial strength of banks using only ratio value, which misleads most of the time. The study offers evidence on how the FSI provides more rigorous results and what are the factors contribute most to the financial strength of banks.

The main goal of this paper is better understand the risk/return trade-off of investing in socially responsible investment funds (SRIF) and green investment funds (GIF).

To achieve our aim a green investment fund portfolio, a socially responsible investment portfolio and a conventional fund (CF) portfolio from the United States of America (USA) were selected to compare the efficiency of these three different portfolios, by using Value-Based Data Envelopment Analysis (DEA) methodology.

The results point out that SRIF and GIF are more efficient than CF. For five years, the CFs have not outperformed the GIF.

The results suggest that there is a growing awareness on the part of investors that sustainable companies are the companies that will allow a better quality of life and a more sustainable environment. It seems that somehow managers and investors are aware that the market will compensate them for thinking about a cleaner and more equitable world.

This paper is motivated by the need to assess the risk profiles associated with the substantial number of items within military supply chains. The scale of supply chain management processes creates difficulties in both the complexity of the analysis and in performing risk assessments that are based on the manual (human analyst) assessment methods. Thus, analysts require methods that can be automated and that can incorporate on-going operational data on a regular basis.

The approach taken to address the identification of supply chain risk within an operational setting is based on aspects of multiobjective decision analysis (MODA). The approach constructs a risk and importance index for supply chain elements based on operational data. These indices are commensurate in value, leading to interpretable measures for decision-making.

Risk and importance indices were developed for the analysis of items within an example supply chain. Using the data on items, individual MODA models were formed and demonstrated using a prototype tool.

To better prepare risk mitigation strategies, analysts require the ability to identify potential sources of risk, especially in times of disruption such as natural disasters.

One of the important decision problems for a power system operator is to choose the appropriate operation point when possible contingencies must be considered. The operator may decide to do nothing or to take some actions that change the operating point and decrease the negative impact (cost) of some or all the contingencies. The purpose of this paper is to investigate the applicability of utility and value functions to this decision problem, since the large range of consequences raises some doubts about the fulfillment of the theoretical conditions imposed by Decision Theory. Although not essential to this discussion, some alternatives to prescriptive methodologies are presented and discussed, mostly regarding the definition of risk attributes and other determinist equivalents, suitable for multicriteria analysis.

For some specific multi-criteria decision-making (MCDM) problems, especially in emergency situations, because of the feature of criteria and other fuzzy factors, it is more appropriate that values of different criteria are expressed in their correspondingly appropriate value types. The purpose of this paper is to build a multi-criteria group decision-making (MCGDM) model dealing with heterogeneous information based on distance-based VIKOR to solve emergency supplier selection in practice appropriately and flexibly, where a compromise solution is more acceptable and suitable.

This paper extends the classical VIKOR to a generalized distance-based VIKOR to handle heterogeneous information containing crisp number, interval number, intuitionistic fuzzy number and hesitant fuzzy linguistic value, and develops an MCGDM model based on the distance-based VIKOR to handle the multi-criteria heterogeneous information in practice. This paper also introduces a parameter called non-fuzzy degree for each type of heterogeneous value to moderate the computation on aggregating heterogeneous hybrid distances.

The proposed distance-based model can handle the heterogeneous information appropriately and flexibly because the computational process is directly operated on the heterogeneous information based on generalized distance without a transformation process, which can improve the decision-making efficiency and reduce information loss. An example of emergency supplier selection is given to illustrate the proposed method.

This paper develops an MCGDM model based on the distance-based VIKOR to handle heterogeneous information appropriately and flexibly. In emergency supplier selection situations, the proposed decision-making model allows the decision-makers to express their judgments on criteria in their appropriate value types.