Alvydas Baležentis

MULTIMOORA for the EU member states updated with fuzzy number theory

Abstract Fuzzy logic handles vague problems in various areas. The fuzzy numbers can represent either quantitative or qualitative variables. The quantitative fuzzy variables can embody crisp numbers, aggregates of historical data or forecasts. The qualitative fuzzy variables may be applied when dealing with ordinal scales. The MULTIMOORA method (Multiplicative and Multi-Objective Ratio Analysis) was updated with fuzzy number theory. The MULTIMOORA method consists of three parts, namely Ratio System, Reference Point and Full Multiplicative Form. Accordingly, each of them was modified with triangular fuzzy number theory. The fuzzy MULTIMOORA summarizes the three approaches. The problem remains how to summarize them. It cannot be done by summation as they are composed of ranks (ordinal). Indeed summation of ranks is against any mathematical logic. Another method, the Dominance Method, is used to rank the EU Member States according to their performance in reaching the indicator goals of the Lisbon Strategy 200...

An integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods

Abstract The aim of this study was to offer a novel procedure for integrated assessment and comparison of Lithuanian economic sectors on the basis of financial ratios and fuzzy MCDM methods. The complex of interrelated issues regarding integrated assessment of economic sectors is discussed in the paper. The object of research is financial indicators of different Lithuanian economic sectors. The proposed procedure for multi-criteria comparison of economic sectors encompasses: 1) the indicator system, 2) application of fuzzy MCDM methods, and 3) inter-sectoral comparison based on ranks provided by fuzzy MCDM methods. The research covers period of 2007–2010, starting at the very beginning of the economic recession and, hopefully, ending with the upcoming recovery. The application of the three MCDM methods was successful. The results suggested the best performing sector being that of forestry and logging. Furthermore, enterprises operating in trade sector, hospitality sector, mining and quarrying sector, info...

EUROPEAN union member states preparing for EUROPE 2020. An application of the MULTIMOORA method

Abstract It is the intention of the European Union to create a growing and sustainable European economy by 2020, a much more moderate target than the 2010 target of becoming the most competitive and dynamic knowledge-based economy in the world. This intention has to be supported by an adequate Optimization and Decision Support System. Therefore, MULTIMOORA is proposed. MULTIMOORA is a quantitative method, which compares multiple and optimum objectives, expressed in different units, as much as possible on a non-subjective basis. In opposition to similar methods MULTIMOORA does not need normalization, being based on dimensionless measures. Importance of an objective can eventually be given by the stakeholders concerned. MULTIMOORA is composed of three approaches: Ratio System, Reference Point and Multiplicative Form Methods, all of the same importance and each controlling each other. Twenty two objectives, 10 originating from statistics and 12 from statistics and forecasts, important for the future, charact...

Multi–Objective Optimization of Well–Being in the Eu-Ropean Union Member STATES

Abstract Well-being is of crucial importance for both individual and society as a whole. It is therefore important to quantify performance and progress made by certain states, regions, communities, social groups, and individuals in improving their well–being. The aim of study was to offer a new framework for multi–criteria assessment as well as international comparison of objective well–being. Well–being is a multi–dimensional phenomenon; hence the appropriate indicator system should be capable to identify the most important underlying processes influencing well–being. For our research we have established the indicator system of twelve indicators identifying various dimensions of well–being. Therefore we propose MULTIMOORA, a model which can be used for approaching the objective of societal well–being. It is applied for international comparison of the well-being in the EU Member States. Consequently, it was revealed that Ireland, the Netherlands, Denmark, Austria, France, Cyprus, Finland, Germany, and Belgium have achieved the highest level of well–being as of 2009. At the other end of spectrum, Czech Republic, Lithuania, Slovakia, Bulgaria, Poland, Hungary, Estonia, Latvia, and Romania can be considered as those peculiar with relatively lowest well–being.

ASSESSING THE EFFICIENCY OF LITHUANIAN TRANSPORT SECTOR BY APPLYING THE METHODS OF MULTIMOORA AND DATA ENVELOPMENT ANALYSIS

Abstract This study focuses on evaluating Lithuanian transport sector throughout 1995–2009 by applying multi–criteria decision making method MULTIMOORA (Multi–Objective Optimization plus the Full Multiplicative Form) and data envelopment analysis (DEA). MULTIMOORA provided ranks that enabled to perform time series analysis, whereas DEA made possible to identify both technical and scale inefficiencies. Due to limited data availability, we analyzed the transport sector as a whole, i. e. it was not decomposed into that of land, air, railway or water. Although every production factor, including labour, capital and land is required for developing the transport sector, due to limited data availability, it is not possible to tackle them all when performing analysis. Consequently, one input, namely energy consumption in transport, was considered in the conducted analysis. On the other hand, two forms of transport – passenger and freight transport – were distinguished, and each of them was measured using composite...

Efficiency and productivity change across the economic sectors in Lithuania (2000–2010): the DEA–MULTIMOORA approach

AbstractReasonable strategic management requires the complex assessment of the regulated area. This study, thus, presents a multi-criteria framework for frontier assessment of efficiency and productivity across the Lithuanian economic sectors throughout 2000–2010. The data envelopment analysis was employed to estimate efficiency in terms of an output indicator (value added) and input indicators (intermediate consumption, capital consumption, and remunerations). Furthermore, the decomposition of the Malmquist productivity index enabled to describe the impact of frontier shifts and catch-up effect on the overall change in efficiency. The multi-criteria decision making method MULTIMOORA aggregated different indicators of efficiency and productivity and thus resulted in the ranking of the economic sectors. The analysis suggests that services sector was the most efficient one, whereas manufacturing was second best. Certain branches of manufacturing, namely pharmaceutical, wood, food, and furniture industry, we...

Innovation policy measurement: analysis of Lithuania’s case

The article seeks to justify a need and possibilities to form Lithuania’s innovation performance measurement framework in terms of existing international practice. To do so different analytical tools, known worldwide in innovation fields, are discussed and the main categories of the assessment are summarised. Taking into account the various instances from the index practice, Lithuania’s innovation performance results and their tendencies in comparison with other countries are analysed. To conclude, several suggestions are drawn for the measurement framework of the Lithuanian innovation policy.

Dynamics of the total factor productivity in Lithuanian family farms with a statistical inference: the bootstrapped Malmquist indices and Multiple Correspondence Analysis

Abstract The paper combines the bootstrapped Malmquist productivity index and the Multiple Correspondence Analysis to measure the changes in the total factor productivity. The bootstrapped Malmquist productivity index enables us to identify insignificant change in the total factor productivity, whereas the Multiple Correspondence Analysis relates the estimates to the environmental variables. A sample of Lithuanian family farms is utilised to test the proposed framework. Specifically, the research covers 200 family farms and the period of 2004–2009. The analysis showed that the total factor productivity decreased by some 15–18% during 2004–2009 depending on the farming type. Multiple Correspondence Analysis suggested that all of the farming types exhibited change in the total factor productivity close to the average, although the crop farming was located in the more stochastic area.