How to invest in Belgian shares by MULTIMOORA optimization

Different multiple objectives expressed in different units make optimization difficult. Therefore, the internal mechanical solution of a Ratio System, producing dimensionless numbers, is preferred to weights, which are most of the time used to compare the different units. In addition, the ratio system creates the opportunity to use a second approach: a non-subjective Reference Point Theory. Therefore, the Reference Point Theory uses the ratios found in the ratio system as co-ordinates for the alternative solutions, which are then compared to a Maximal Objective Reference Point. The two approaches form a control on each other. This overall theory is called MOORA (Multi-Objective Optimization by Ratio Analysis). The results are still more convincing if a Full Multiplicative Form is added, three methods assembled under the name of MULTIMOORA. At that moment, the control by three different approaches forms a guaranty for a solution being as non-subjective as possible. As to calculate the sum of three obtained ranks is not allowed, a theory of Ordinal Dominance is developed in order to remain in the ordinal sphere. MULTIMOORA is used to decide upon an investment in Belgian shares on basis of a ranking in the BEL20 Index

Is credit rating reserved territory for credit rating agencies? A MULTIMOORA approach for European firms and countries

Credit Rating Agencies rate firms and countries by internal experts but with a final qualitative judgment by their management acting as decision makers. These ratings on their turn influence the countries credit rating and ipso facto of their enterprises. The work of the CRA is in fact double: credit rating of firms and other organizations at one side and countries on the other. Considering the credit rating of firms, the CRA made significant mistakes during the Recession 2007−2009 and their judgment is too much American oriented, in any way from a European point of view. Consequently, in Europe many efforts were made to come to a new agency, but all efforts failed. It could be different for the rating of countries. Is a more scientific approach, eventually on a quantitative and structural basis, not possible? Therefore, MULTIMOORA, a quantitative method, is suggested. The study was made for all countries of the European Continent. Based on data available in 2013 and on their extrapolation, the results are quite comparable to the results of Standard & Poor’s Credit Rating System of the moment. As the classifications of Moody’s and Fitch are very similar to those of Standard & Poor’s the outcome would be similar for these other Credit Rating Agencies

VIŠECILJNA OPTIMIZACIJA BLAGOSTANJA U ZEMLJAMA ČLANICAMA EU-ROPSKE UNIJE

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, Cy-prus, 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, Lat-via, and Romania can be considered as those peculiar with relatively lowest well–being.Blagostanje je od ključnog značaja kako za pojedinca tako i za društvo u cjelini. Stoga je važno kvantificirati performanse i napredak određenih država, regija, zajednica, društvenih grupa i pojedinaca kako bi se unaprijedilo njihovo blagostanje. Cilj istraživanja je ponuditi novi okvir za višeciljnu procjenu kao i međunarodnu usporedbu objektivnog blagostanja. Blagostanje je višedimenzionalna pojava; stoga bi prikladni sustav indikatora trebao biti u mogućnosti identificirati najvažnije temeljne procese koji utječu na blagostanje. Za potrebe našeg istraživanja ustanovili smo indikatorski sustav od dvanaest indikatora koji identificiraju razne dimenzije blagostanja. Stoga predlažemo MULTIMOORA, model koji se može koristiti za približavanje cilju društvenog blagostan-ja. Primjenjuje se u svrhu međunarodne usporedbe blagostanja u zemljama članicama EU. Tako se otkrilo da su Irska, Nizozemska, Danska, Austrija, Francuska, Cipar, Finska, Njemačka i Belgija do-segle najviši stupanj blagostanja od 2009. Na drugom kraju spektra se nalaze Češka, Litva, Slovačka, Bugarska, Poljska, Mađarska, Estonija, Latvija i Rumunjska u kojima je blagostanje najniže

A Bipolar Fuzzy Extension of the MULTIMOORA Method

The aim of this paper is to make a proposal for a new extension of the MULTIMOORA method extended to deal with bipolar fuzzy sets. Bipolar fuzzy sets are proposed as an extension of classical fuzzy sets in order to enable solving a particular class of decision-making problems. Unlike other extensions of the fuzzy set of theory, bipolar fuzzy sets introduce a positive membership function, which denotes the satisfaction degree of the element x to the property corresponding to the bipolar-valued fuzzy set, and the negative membership function, which denotes the degree of the satisfaction of the element x to some implicit counter-property corresponding to the bipolar-valued fuzzy set. By using single-valued bipolar fuzzy numbers, the MULTIMOORA method can be more efficient for solving some specific problems whose solving requires assessment and prediction. The suitability of the proposed approach is presented through an example

A Multi-Objective Decision Support System for Project Selection with an Application for the Tunisian Textile Industry

A developing country like Tunisia needs development planning but it will have problems with a top down strategy. As an answer to this problem the paper proposes a Multi-Objective Decision Support System for Project Selection. Project Selection is subject to an evolution concerning the objectives to strive after. If before the stress was put on market analysis, Net Present Value, Internal Rate of Return and other micro-economic targets, macro-economic objectives receive more and more attention such as employment, value added and the influence on the balance of payments. Employment is a human right, sometimes even written down in national constitutions. T raditional Cost-Benefit does not respond to these purposes. Indeed in Cost-Benefit all benefits (objectives) have to be translated into money terms, leading sometimes to immoral consequences. On the contrary Multi-Objective Optimization takes care of different objectives, whereas the objectives keep their own units. However different methods exist for the application of Multi- Objective Optimization. These methods were tested after their performance. MOORA (Multi- Objective Optimization by Ratio analysis) and MULTIMOORA (MOORA plus a Full Multiplicative Form), showed positive results; the more if they were assisted by Ameliorated Nominal Group and Delphi Techniques. A simulation exercise for Tunisia illustrates the use of these methods. The needs of the T unisian textile industry are analyzed and as an answer three projects facing multiple objectives are simulated

Multimoora for the EU member states updated with fuzzy number theory / Neraiškiųjų skaičiu teorija papildytas MULTIMOORA metodas europos sąjungos valstybių narių išsivystymo vertinimui

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 2000–2008. This ranking will group the best performing countries in a Core Group, followed by a Second Group, the Semi-periphery Group. Group 3, the Periphery Group, will encompass the less performing states. Santrauka Neraiškioji logika padeda įvertinti ir spręsti neapibrežtas problemas įvairiose srityse. Neraiškieji skaičiai gali išreikšti tiek kiekybinius, tiek kokybinius kintamuosius. Kiekybiniai neraiškieji kintamieji gali apimti tradicinius realiuosius skaičius, susintetintus istorinius duomenis (laiko eilutes) ar prognozuojamas tendencijas. Kokybiniai neraiškieji kintamieji gali būti naudojami dirbant su rangų skalėmis (lingvistiniai kintamieji). Taigi daugiakriterinio vertinimo metodų praplėtimas neraiškiųjų skaičių aibių teorija yra svarbus klausimas. MULTIMOORA metodas buvo papildytas neraiškiųjų skaičių teorija. Viršūnės metodas pritaikytas skaičiuojant atstumus tarp neapibrėžtųjų skaičių. Ploto centro metodas pritaikytas konvertuojant neraiškiuosius skaičius į realiuosius. MULTIMOORA metodą sudaro trys dalys: santykių sistema, atskaitos taškas ir pilnoji sandaugos forma. Kiekviena dalis buvo modifikuota papildant ją trečiojo laipsnio neraiškiaisiais skaičiais. Neraiškioji santykių sistema apima vidinį normalizavimą, kriterijų apibendrinimą ir konvertavimą į apibrėžtuosius skaičius. Neraiškioji atskaitos taško sistema remiasi atskaitos taško (vektoriaus) nustatymu ir kiekvienos alternatyvos atstumo iki jo matavimu taikant viršūnės metodą. Neraiškioji pilnoji sandaugos forma sujungia grynosios multiplikatyvinės naudingumo funkcijos maksimizavimą ir konvertavimą į realiuosius skaičius. Neraiškusis MULTIMOORA metodas apibendrina šiuos tris požiūrius. Straipsnyje išspręsta rangų apibendrinimo problema, iškylanti apibendrinant keliais daugiakriterinio optimizavimo metodais gautus rangus. Šiam tikslui pasiųlyta ir pritaikyta dominavimo teorija, apibūdinanti įvairias alternatyvų palyginimo procedūras remiantis skirtingais tos pačios alternatyvos rangais. ES valstybių narių pažanga įgyvendinant Lisabonos strategijos tikslus 2000–2008 m. įvertinta taikant neraiškųjį MULTIMOORA metodą ir dominavimo teoriją. Analizes rezultatai rodo, kad pirmauja Švedija, Liuksemburgas, Suomija, Austrija, Nyderlandai, Danija, Belgija, Jungtinė Karalystė ir Vokietija. Antrajai grupei priklauso Prancūzija, Airija, Ispanija, Italija, Slovėnija, Portugalija, Čekija, Graikija ir Estija. Labiausiai atsilieka Vengrija, Kipras, Lenkija, Lietuva, Slovakija, Latvija, Malta, Rumunija ir Bulgarija. Reikšminiai žodžiai: daugiakriterinis optimizavimas, MOORA, MULTIMOORA, struktūriniai rodikliai, Lisabonos strategija, strateginis valdymas, Europos Sąjunga, darnus vystymas, neraiškieji skaičiai, trečiojo laipsnio skaičiai, dominavimo teorija, tranzityvuma

Implementation of the strategy Europe 2020 by the multi-objective evaluation method Multimoora

The Lisbon Strategy was initiated by the European Union in 2000 in order to turn the European Union into the most competitive and dynamic knowledge-based economy in the world by 2010. The Lisbon Strategy recognized the open method of co-ordination (OMC) as the EU-level governance tool. In the presence of the failure of the 2010 strategy the EU Member States adopted a new one as Europe 2020. Headline targets of the new strategy include an increase of the employment level, encouraging Research and Development, ensuring sustainable development and reducing social exclusion. The aim of this article is the development of the OMC practice by offering new proce- dures namely a system of structural indicators and the application of a multi-objective evaluation method. Being suitable for international comparisons, the multi-objective method MULTIMOORA is applied for analyzing a system of structural indicators and for covering headline targets of the strategy Europe 2020. The data cover the period 2005–2008 enabling to identify the progress of the EU Member States before adoption of the strategy Europe 2020. According to ranks given by MULTIMOORA, the Member States are classified into three groups: high performance, medium performance, and low performance states