Multiple criteria decision making (MCDM) methods in economics: an overview/Daugiatiksliai sprendimu priemimo metodai ekonomikoje: apzvalga.
Decision making problems are of crucial importance in economics. The main research activities in economics during the last five years have significantly increased. The main fields are operation research and sustainable development. Success in economics and business is a straightforward matter: focus on society, government, stakeholders, customers, and amaze them with experiences that exceed their expectations. Decision analysis is widely recognized as a sound prescriptive theory.
On the basis of intensive and productive scientific works and the high achievements, EURO Working Group OR in Sustainable Development and Civil Engineering (EWG-ORSDCE) was established in 2009 (http://www.orsdce.vgtu.lt/?id=54427.33863).
Publication in 1776 of The Wealth of Nations by Adam Smith has been described as the effective birth of economics as a separate discipline (Blaug 2007). Katona (1953) presented and contrasted the most common forms of methodologies of the economic principle of rationality in both psychology and economics, and a general discussion of the role of empirical research among psychologists in studies of economic behaviour was initiated.
Current economic models developed out of a broader field of political economy in the late 19th century, owing to a desire to use an empirical approach more akin to the physical sciences (Clark 1998). Rationality is a central principle in decision-making, where a rational agent is specifically defined as an agent who always chooses the action which maximises its expected performance (Johnson-Laird and Byrne 1991). Rational choice theory, also known as choice theory or rational action theory (Arrow 1989), is a framework for understanding and often formally modelling social and economic behaviour. The basic idea of rational choice theory is that patterns of behaviour in societies reflect the choices made by individuals as they act by comparing the costs and benefits of different courses of action. It is the main theoretical paradigm in the currently-dominant school of microeconomics.
The fact that people act rationally has been recognised by many scientists, but they have seen rational actions alongside other forms of action, seeing a human action as involving both rational and non-rational elements. Actions are often expressed as a set of actions. In rational choice theories, individuals are seen as motivated by the wants or goals that express their 'preferences'. Decision makers act within specific, given constraints and on the basis of the information that they have about the conditions under which they are acting. Durkheim in 1893 (Durkheim 1984) argued that all rational economic actions occur within an institutional framework of norms that cannot itself be explained as a result of rational action alone. Groups and organisations, business enterprises, and others may, then, all figure as collective actors whose individual intentions are aggregated and an agreed policy formulated (Hindess 1988). Individuals or organizations are called rational if they make optimal decisions in pursuit of their goals.
Von Winterfeldt's and Edwards works on multiple stakeholder decision analysis and behavioural decision theory generated a more formal approach to multiple attribute utility analysis (von Winterfeldt and Edwards 1986).
Perhaps the most important ideas are that a common value structure can be created even when stakeholders violently disagree about the issues at hand; that conflicts are often about specific value tradeoffs or facts; that conflicts about values can be expressed as different weights; and that conflicts about facts can be modelled by using judgments from different experts. Most importantly perhaps was the finding that decision analysis can be useful to help multiple stakeholders understand what they agree and disagree about, focus on the things that they disagree about and explore options that are better for everyone involved.
It is believed that a good rationale must be independent from personal emotions, feelings, instincts or culturally specific, moral codes and norms. If these minimum requirements are not satisfied, the analysis may be termed irrational. It is evident that no human has ever satisfied this criterion.
Weber (Max Weber (1864-1920) distinguished between four ideal-types of action (Weber 2011):
--Affectual, determined by an actor's specific affect, feeling, or emotion;
--Value-rational action. Here the action is undertaken for what one might call reasons intrinsic to the actor: some ethical, aesthetic, religious or other motive, independent of whether it will lead to success;
--Means-end rational action.
As expressed by Weintraub (2007), neoclassical economics rests on three assumptions:
--People have rational preferences among outcomes;
--Individuals maximize utility and firms maximize profits;
--People act independently on the basis of full and relevant information.
Bounded rationality is the idea that in decision-making rationality of individuals is limited according to the information they have, the cognitive limitations of their minds, and the finite amount of time they have to make decisions (Elster 1983). Another way to look at bounded rationality is that because decision-makers lack the ability and resources to arrive at the optimal solution; they instead apply their rationality only after having greatly simplified the choices available (Gigerenzer and Selten 2002).
Economists determine priorities of actors by strictly mathematical descriptions. They make a set of assumptions which are referred to as the assumptions of a human's rational behaviour.
Rational choice theory makes two assumptions about individuals' preferences for actions:
--Completeness: all actions can be ranked in order of preference (indifference between two or more is possible).
--Transitivity: if action [a.sub.1] is preferred to [a.sub.2], and action [a.sub.2] is preferred to [a.sub.3], then [a.sub.1] is preferred to [a.sub.3].
Together these assumptions form the result that given a set of exhaustive and exclusive actions to choose from, an individual can rank them in terms of his preferences, and that his preferences are consistent.
An individual's preferences can also take forms:
--Strict preference occurs when an individual prefers [a.sub.1] to [a.sub.2], but not [a.sub.2] to [a.sub.1].
--In some models, a weak preference occurs when an individual has a preference for at least [a.sub.j], similar to the mathematical operator [less than or equal to].
--Indifference occurs when an individual does not prefer [a.sub.1] to [a.sub.2], or [a.sub.2] to [a.sub.1].
--In more complex models, other assumptions are often incorporated, such as the assumption of independence axiom. Also, with dynamic models that include decision making over time, time inconsistency may affect an individual's preferences.
From the first days of the mankind on earth, there is evidence of countless human decision situations related to real life problems with many desirable attributes. These attributes are often referred to in literature as criteria or Performance Measures. All interested parties think about different touch points and allow them to rank feasible alternatives in importance, need for improvement, and overall criteria selection. The different touch points need to be reengineered to conduct the experience according to the criteria that the interested parties defined as important.
2. Operations research in economics
An Operations Research (OR) is the application of scientific method to the management of organized systems such as industrial production systems, government and social programs, and defence systems. OR (also referred to as decision science or management science) is the application of science to the solution of managerial and administrative problems; it focuses on the performance of organized systems taken as a whole rather than on their parts separately. Its techniques and methods, and the areas to which they are applied, can be expected to continue to expand rapidly (Industrial Engineering ... 2011). OR is an interdisciplinary mathematical science that focuses on the determination of the maximum or minimum of some real-world objectives. The environment in which decisions must be made is more complex than ever before. Companies use operations research to devise ways and means to maximize their profits and restrict their losses and risks. Also, they devise means to produce at lower costs or produce more quantities at the same costs.
Many years of research effort have been devoted to developing various mathematical models which could describe decision maker behaviour. These models are applied in OR. Some of the tools used by operational researchers are statistics, optimization, probability theory, queuing theory, game theory, graph theory, decision analysis, mathematical modelling and simulation.
The main stages of conventional OR are as follows:
--Creating the model, which is proper to the problem solution;
--Selecting the optimality criterion;
--Choosing preferable solution.
The primary step in many economical studies and in OR is the construction of models representing the reality. Typical decision making problems imply the creation of a subjective model representing personal perception of a decision problem. Decision making has two roots: economical utility theory and OR.
With expanded technologies and impact on environment, as well as sustainable development of economy, the use of OR is widely extending. Informed stakeholders, society, government and scientists require solving problems taking into account multiple criteria. Such problem solution approach enables taking high quality decisions. A distinction between OR and Decision making methods is that the latter have several different methods to evaluate quality of decisions made. Compromise among several criteria could be determined by the person (group of persons) who makes decisions.
There is a separated class of models for decision making methods which are of objective character (similar to the OR), but quality of the made decision is determined according to a several criteria. This class of problems is named multiple criteria models with objective described models. This class is position between OR and decision-making.
Forecasting is one of the most significant parts in decision-making. The reason of this is that decisions must be made before acting and it deals with future. Executives make forecasts as an essential part of their work. International institute of Forecasters sponsored classifying and the main forecasting principles are presented (Fig. 1). The Methodology Tree for Forecasting classifies all possible types of forecasting methods into categories and shows how they inter-relate.
[FIGURE 1 OMITTED]
Developing economics, changing environment, sustainability of decisions are the reasons for the rapid development of new OR techniques and many of those techniques were adopted for problem solution in economics. But in reality, the modelling of economical problems is based on a different kind of logics, taking into consideration the following elements (i.e. multiple criteria paradigm (Roy 1988)):
--The existence of multiple criteria;
--The conflicting situation between the criteria;
--The complex, subjective and ill-structured nature of the evaluation process;
--The introduction of financial decision makers in the evaluation process.
The main limitation of operations research is that it often ignores the human element in the production process. This science is technology driven and does not take into account the emotional factors and absenteeism of employees.
3. Multiple criteria decision making (MCDM) methods in economics
In the field of MCDM, there are two schools of thought that a human choice is based on: a French school and an American school (Lootsma et al. 1990). The French school mainly promotes the outranking concept for evaluating discrete alternatives (Roy 1968). The American school is based on multi-attribute value functions and multi-attribute utility theory (MAUT) (Keeny and Raiffa 1976).
Multiple criteria decision making, as described by Vincke (1992), is the most directly characterised by a set of multiple criteria method. From 1950s onwards, there had been a large number of refined MCDM methods developed and they differ from each other in the required quality and quantity of additional information, the methodology used, the user-friendliness, the sensitivity tools used, and the mathematical properties they verify. Vincke succinctly outlines a disaggregation of the overall of the multiple criteria decision into three components of multiple attribute utility theory, outranking methods and interactive methods.
Siskos and Spyridakos (1999) presented a survey of the history and the recent status of the multiple criteria decision support systems. Wang et al. (2009) in review of multi-criteria decision analysis aid in sustainable energy decision making pointed out that MCDM methods have become increasingly popular in decision-making for sustainability because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic and biophysical systems.
Carlsson and Fuller (1996) stated that there are four quite distinct families of MCDM methods:
--The value and utility theory based;
--The multiple objective programming;
--Group decision and negotiation theory based methods.
Fuzzy MCDM has basically been developed along the same lines, although with the help of fuzzy set theory a number of innovations have been made possible.
Utility theory is interested in people's preferences or values and with assumptions about a person's preferences and with judgements of preferability, worth, value, goodness or any of a number similar concept that enable them to be presented in numerically useful ways (Fishburn 1965, 1968). In decision theory, utility is a measure of the desirability of consequences of the courses of action that applies to decision making under the risk, i.e. under uncertainty within known probabilities.
The concept of utility applies to both single-attribute and multi-attribute consequences. The fundamental assumption in utility theory is that the decision maker always chooses the alternative for which the expected value of the utility is maximal. If that assumption is accepted, utility theory can be used to predict or prescribe the choice that the decision maker will make, or should make, among the available alternatives. For that purpose, a utility has to be assigned to each of the possible (and mutually exclusive) consequences of every alternative. A utility function is the rule by which this assignment is done and depends on the preferences of the individual decision maker. In utility theory, the utility measures of the consequences are assumed to reflect a decision maker's preferences in the following sense:
--The numerical order of utilities for consequences preserves the decision maker's preference order among the consequences;
--The numerical order of expected utilities of alternatives preserves the decision maker's preference order among these alternatives.
The art of applying multi-attribute utility has expanded since 1976. There should be significant interplay between descriptive studies of how people do process information and make decisions and prescriptive decision analysis to help people make decisions that are consistent with their values and understanding of the problem (Tsoukias and Vincke 2011).
Preferences are used in a lot of decision making problem situations in economics. The first attempt to give an account about preference relations can be referred to Von Neumann and Morgenstern (1944). Savage (1954) was the first to introduce the foundation of the subject. Most of the economical, industrial, financial or political decision problems are multiattribute. The problem to estimate utility function representing the actor's preferences in the multidimensional case (multiattribute utility theory) is very important. The problem of the selection or the ranking of alternatives submitted to a multicriteria evaluation is not an easy problem. Usually, there is no optimum solution; no alternative is the best one for each criterion. Better quality implies higher price. The criteria are conflicting. Compromise solutions have to be considered.
The subject was investigated in Keeney and Raiffa (1976), where the basic conditions under which their use is possible are introduced. Rough set theory is a tool for dealing with granularity, classification, vagueness and incompleteness in data analysis (Zhu 2009). In order to achieve this goal, researchers have proposed many methods other than classical logic, for example, fuzzy set theory, rough set theory, computing with words and granular computing, computational theory for linguistic dynamic systems.
It is obvious that uncertainty is a typical feature of preferences when it is necessary to define calculus so as to handle these situations operationally. Fuzzy set theory could then be a tool (Zadeh 1975a, b, c). The first fuzzy outranking relation is defined as theoretical background for the ELECTRE III method. Greco et al. (1999, 2000 and 2001); Pawlak et al. 1995) pointed at peculiarities of fuzzy sets and rough sets using in MCDM.
A linguistic variable is a variable whose values are expressed in linguistic terms (Zimmermann 1985). The concept of a linguistic variable is very useful in dealing with the situations which are too complex or not well-defined to be reasonably described in conventional quantitative expressions (Larichev and Moshkovich 1997; Larichev and Brown 2000; Ustinovichius et al. 2009, 2010, 2011). Fuzzy numbers are introduced to appropriately express linguistic variables. In the area of fuzzy reasoning, the two-tuple linguistic representation method (Herrera et al. 2000; Liu and Zhang 2011) is widely applied for computing with words.
Problem selection and alternative creation are critically important. Investigation and aggregation of values, which describe the reason actors are interested in decision situation, are referred to as value-focused thinking. The aim of this model is to create better alternatives and aggregation of individual preferences for any decision problem. Many of the complex problems faced by decision makers involve multiple conflicting objectives (Keeney 1982).
4. Classification of discrete multiple criteria methods
There are a lot of MCDM methods (Guitoni and Martel 1998). MCDM approaches are major parts of decision theory and analysis. Hwang and Yoon (1981) grouped the MCDM methods according to the available information. Real-world decision making problems are usually complex and no structures are to be considered through the examination of a single criterion, or point of view that will lead to the optimum decision. Operation in the marketplace requires some knowledge of areas generating critical situations and insolvency. It is necessary to learn the criteria determining both development and downfall of feasible alternatives (Kaplinski 2008a). In a mono-criterion approach, the analyst builds a unique criterion capturing all the relevant aspects of the problem. Such a one-dimensional approach is an oversimplification of the actual nature of the problem. In many real-world decision problems, the decision-maker has a set of multiple conflicting objectives. All new ideas and possible variants of decisions must be compared according to many criteria (Turskis et al. 2009). The problem of decisionmaker consists of evaluating a finite set of alternatives in order to find the best one, to rank them from the best to the worst, to group them into predefined homogeneous classes, or to describe how well each alternative meets all the criteria simultaneously. There are many methods for determining the ranking of a set of alternatives in terms of a set of decision criteria.
[FIGURE 2 OMITTED]
Over the past decades the complexity of economical decisions has increased rapidly, thus highlighting the importance of development and implementation of sophisticated and efficient quantitative analysis techniques for supporting and aiding economical decision-making. MCDM is an advanced field of OR; it provides decision makers and analysts with a wide range of methodologies, which are overviewed and well-suited to the complexity of economical decision problems (Hwang and Yoon 1981; Zopounidis and Doumpos 2002; Figueira et al. 2005). Over the last decade scientists and researchers have developed a set of new MCDM methods (Kaplinski and Tupenaite 2011; Kaplinski and Tamosaitiene 2010; Tamosaitiene et al. 2010). They modified methods and applied to solve practical and scientific problems.
Most of MCDM methods deal with discrete alternatives, which are described by a set of criteria. Criteria values can be determined as a cardinal or ordinal information. Information could be determined exactly or could be fuzzy, determined in intervals. Modern MCDM methods enable decision makers to deal with all above mentioned types of information. One of the problems encountered during multiple criteria decision making process is the choice of the aggregation procedure for solving the decision problem. However, multiple criteria decision analysts provide a variety of aggregation procedures. MCDM methods have become increasingly popular in decision making for economics because of the multi-dimensionality of the sustainability goal and the complexity of socio-economic, environment and government systems (Tables 1 and 2). Approximately one out of six scientific researches in MCDM deal with fuzzy sets or fuzzy relations (Table 2, Fig. 3).
In the multiple criteria approach, the analyst seeks to build several criteria using a few points of view. MCDM is one of the most widely used decision methodologies in science, business, and governmental worlds, which are based on the assumption of a complex world, and can help improve the quality of decisions by making the decision making process more explicit, rational, and efficient. In real life, a decision-maker first of all must understand and describe the situation. This stage includes the determination and assessment of the stakeholders, different alternatives of feasible actions, a large number of different and important decision criteria, type and quality of information, etc. It appears to be the key point defining MCDM as a formal approach. For Zeleny (1977, 1982) decision criteria are rules, measures and standards that guide decision-making. Bouyssou (1990) proposed a general definition of a criterion as a tool allowing comparison of alternatives according to a particular point of view. When building a criterion, the analyst should keep in mind that it is necessary for all the actors of the decision process to adhere to the comparisons that will be deduced from that model. Criteria (relatively precise, but usually conflicting) are measures, rules and standards that guide decision-making, which also incorporates a model of preferences between elements of a set of real or fictitious actions. Typical examples of MCDM problems are referred to as discrete MCDM problems, involve the selection among different investment projects, personnel ranking problem, and financial classification problem, and are decision-support oriented. The major strength of multiple criteria methods is their ability to address to the problems marked by various conflicting interests.
[FIGURE 3 OMITTED]
Classical methods of multiple criteria optimization and determination of priority and utility function were first applied by Pareto in 1896 (Pareto 1971). These methods were strongly related to economical theory, concerning the averages of thousands of decisions. Methods of multiple criteria analysis were developed to meet the increasing requirements of human society and the environment. Methods of multiple criteria analysis were developed in 1960s to meet the increasing requirements of human society and the environment. Keeney and Raiffa (1976) offered the representation theorems for determining multiple criteria utility functions under preferential and utility independence assumptions. Keeney (1982) outlined the essential features and concepts of decision analysis, formulated axioms and major stages. Seo (1981) suggested a multiple criteria decision making method that was concerned with balancing some conflicting objectives in a hierarchical structure. Saaty (1977) showed the global importance of solving problems with conflicting goals by using multiple criteria models and presented decision making models with incomplete information. In his latest works Saaty (Saaty et al. 2003) analyzed measuring problems in assignments associated with uncertainty conditions and applied the AHP method to solve different problems. Tanino et al (1981) analyzed the problem of the coordination of different goals and objectives of various interested parties. Keeney (1982) outlined the essential features and concepts of decision analysis, formulated axioms and major stages. Keeney and Winterfeldt (2001) suggested following the prudence principle in decision process, making decisions precisely and evaluating all possible alternatives, the aims of interested parties, subsequences of decision results and value changes, hereby minimizing the decision making risk.
There are lot of even sophisticated issues in collaboration with specialists representing other domains of science (e.g. mathematicians) (Kaplinski 2008a, b, c). Available wide range of MCDM problems solution techniques, varying complexity and possibly solutions, confuses potential users. Each method has own strengths, weaknesses and possibilities to be applied. It causes phenomena known as the inconsistent ranking problem and can be caused by different MCDM methods. A major criticism of MCDM methods is that due to the differences among different techniques, different results are obtained when applied to the same problem. These differences of algorithms are:
--Using weights differently;
--Different selection of the best solution;
--Attempt to scale objectives;
--Introducing additional parameters that affect solution.
The need of comparing MCDM methods and the importance of the selection problem were first recognized by MacCrimmon who suggested taxonomy of MCDM methods. There are many comparative studies presented in scientific research works. Guitoni and Martel (1998) proposed a methodological approach to select an appropriate MCDM method to a specific decision making situation. The selection may be done via comparing MCDM methods (Zanakis et al. 1998). A simulation by Zanakis et al. (1998) evaluated eight MCDM methods: SAW, multiplicative exponential weighting (MEW); ELECTRE, and AHPs: SAW and MEW performed best. Computations of different examples reveal the fact that evaluation outcome depends on both choice of utility function and its parameters (Podvezko and Podviezko 2010).
There are many ways to classify MCDM methods (Hwang and Yoon 1981; Larichev 2000; Figueira et al. 2005). For instance, Belton and Stewart (2002) offered the following classification of MCDM methods: 1) value measurement models; 2) goal, aspiration, and reference level models; 3) outranking models (the French school).
The classification of MCDM methods according to the type of information based on the Larichev's (Larichev 2000) proposal is given bellow:
--Methods based on quantitative measurements. The methods based on multiple criteria utility theory may be referred to this group (TOPSIS, LINMAP, MOORA, COPRAS, and its modification COPRAS-G).
--Methods based on qualitative initial measurements. These include two widely known groups of methods: AHP and fuzzy set theory methods (Zimmermann 2000).
--Comparative preference methods based on pair-wise comparison of alternatives. This group comprises the modifications of the ELECTRE, PROMETHEE, TACTIC, ORESTE and other methods (Turskis 2008).
--Methods based on qualitative measurements not converted into quantitative variables. This group includes methods of verbal decision making analysis (Berkeley et al. 1991) and uses qualitative data for decision environments involving high levels of uncertainty.
--MCDM problems can be categorized as continuous or discrete, depending on the domain of alternatives.
Hwang and Yoon (1981) classify them as:
--MCDM with discrete, usually limited, number of alternatives, requiring criterion comparisons, involving implicit or explicit tradeoffs;
--MODM (multiple objective decision-making), with decision variable values to be determined in a continuous or integer domain, of infinite on a large number of choices, to satisfy best the decision-maker constraints, preferences or priorities.
In particular, the main steps of multiple criteria decision making are the following:
--Determining the main goal of a problem;
--Establishing system of the main objectives or criteria by which the alternatives are to be judged;
--Generating feasible alternatives (a finite number of alternative plans or options) that can be implemented to achieve goals;
--Evaluating an impact of each criterion on the decision making function or weights of criteria. A decision-maker should express his / her preferences in terms of the relative importance of criteria, and one approach is to introduce criteria weights.
The weights in MCDM do not have a clear economic significance, but their use provides opportunity to model actual aspects of the preference structure:
--A set of performance evaluations of alternatives for each criterion;
--A method for ranking the alternatives based on how well they satisfy the criteria;
--Aggregating alternative evaluations (preferences);
--Accepting one alternative as the best (the most preferable);
--Gathering new information and the next iteration of MCDM if the final solution is not accepted;
--Making recommendations for decision-making.
An alternative in multiple criteria evaluation is usually described by quantitative and qualitative criteria. The criteria have different units of measurement. Normalization aims at obtaining comparable scales of the criteria values. Different techniques of criteria value normalization are used. The impact of the decision-matrix normalization methods on the decision results has been investigated by many authors (Juttler 1966; Korth 1969; Stopp 1975; Weitendorf 1976; Zavadskas 1987, 1990; Peldschus 2009; Ginevicius 2008; Zavadskas and Turskis 2008). There are still no rules determining the application of multiple criteria evaluation methods and interpretation of the results obtained.
The case study findings about pioneering studies in multiple criteria decision making paradigms and earliest application are summarized in Table 3.
5. Recent development and applications
Recent case study findings about the parallels between economics and multiple criteria decision making paradigms are summarized in Table 4. There, it is pointed at the methods applied by users except for authors of the paper. Authors of paper applied most of the methods listed in Table 4 in own researches, but they have not presented them.
Operations research is very beneficial in deciding upon what to produce, the quantities, the methods of production, which employees to engage in the production processes and the marketing schemes of the produced goods. In this survey a comprehensive view of problems that are open in the field of decision making in economics is given.
The fact that people act rationally and are independent of personal emotions, feelings, instincts or culturally specific, moral codes and norms has been recognised by many scientists in classical theories. It is evident that no human has ever satisfied this criterion. Groups and organisations, business enterprises, and others may, then, all figure as collective actors whose individual intentions are aggregated and an agreed policy formulated. There could be definitely stated that the "best" approach does not exist. The eventual choice of one is a multiple criteria problem and, therefore, it has no optimal solution. Economical decision making is extremely complex due to the intricacy of the systems considered and the competing interests of multiple stakeholders. Decision making theories and applications offer different modelling techniques, provide an appropriate approaches for modelling decision aiding, help in development of alternatives as they take into account the complexity of the process.
The selection of a model and problem solution approach depends on the desired goal, actors involved in the decision making process, available information, time, and etc. There are several branches of decision theory that depart from the stand expected utility paradigm. The major strength of multiple criteria methods is their ability to address problems marked by various conflicting interests.
There are a lot of open fields of future research as:
--Analysis of different scaling methods;
--Analysis of preference relations;
--Analysis of aggregation procedures;
--The study of grey relations;
--The study of fuzzy relations;
--The development and modification of new mathematical models to solve outranking problems.
Multiple criteria decision making provides powerful approaches to solve complicated problems in economics. These techniques allow actors to solve those problems which are impossible to solve by applying common optimisation models.
The main focus of this paper was to overview the use of decision support tools, such as recent developments of classical models of multicriteria decision analysis, which are being used increasingly for comparative analysis and assessment of alternatives.
Ananda, J.; Herath, G. 2008. Multi-attribute preference modelling and regional land-use planning, Ecological Economics 65(2): 325-335. doi:10.1016/j.ecolecon.2007.06.024
Antucheviciene, J.; Zavadskas, E. K.; Zakarevicius, A. 2010. Multiple criteria construction management decisions considering relations between criteria, Technological and Economic Development of Economy 16(1): 109-125. doi:10.3846/tede.2010.07
Arrow, K. J. 1989. Economic Theory and the Hypothesis of Rationality, in 1990 The New Palgrave: Utility and Probability, Eatwell, J.; Milgate, M.; Newman, P. (Eds.). W. W. Norton Company, 25-39.
Arslan, G.; Aydin, O. 2009. A new software development for Fuzzy Multicriteria decision-making, Technological and Economic Development of Economy 15(2): 197-212. doi:10.3846/1392-8619.2009.15.197-212
Bakshi, T.; Sarkar, B. 2011. MCA based performance evaluation of project selection, International Journal of Software Engineering & Applications (IJSEA) 2(2): 14-22.
Balezentis, A.; Balezentis, T. 2011. Integrated assessment of Lithuanian economic sectors based on financial ratios and fuzzy MCDM methods, Technological and Economic Development of Economy 17(3) (In press)
Bana e Costa, C. A.; Vansnick, J. C. 1994. MACBETH: An Interactive Path Towards the Construction of Cardinal Value Functions, International Transactions in Operational Research 1(4): 489-500. doi:10.1016/0969-6016(94)90010-8
Belton, V.; Stewart, T. J. 2002. Multiple criteria decision analysis: an integrated approach. Boston: Kluwer Academic Publications.
Benayoun, R.; Roy, B.; Sussman, B. 1966. ELECTRE: Une methode pour guider le choix en presence de points de vue multiples. Note de travail 49, SEMA-METRA International, Direction Scientifique.
Berkeley, D.; Humphreys, P.; Larichev, O.; Moshkovich, H. 1991. Aiding strategic decision making: Derivation and development of ASTRIDA, in Y. Vecsenyi and H. Sol. (Eds.). Environment for Supporting Decision Processes, 59-82 North-Holland, Amsterdam.
Bindu Madhuri, Ch.; Anand Chandulal, J.; Padmaja, M. 2010. Selection of Best Web Site by Applying COPRAS-G method, International Journal of Computer Science and Information Technologies 1(2):138-146.
Blaug, M. 2007. The Social Sciences: Economics, The New Encyclopedia Britannica 27, 343.
Bojkovic, N.; Anic, I.; Pejcic-Tarle, S. 2010. One solution for cross-country transport-sustainability evaluation using a modified ELECTRE method, Ecological Economics 69(5): 1176-1186. doi:10.1016/j. ecolecon.2010.01.006
Bojovic, N.; Boskovic, B.; Milenkovic, M.; Sunjic, A. 2010. A two-level approach to the problem of rail freight car fleet composition, Transport 25(2): 186-192. doi:10.3846/transport.2010.23
Bouysou, D. 1990. Building criteria: A perquisite for MCDA, in Bana a Costa, C. A. (Ed.). Readings in multiple criteria decision aid, Berlin: Springer-Verlag, 319-334.
Brans J. P.; Vincke, P.; Mareschal, B. 1986. How to select and how to rank projects: The PROMETHEE method, European Journal of Operational Research 24(2): 228-238. doi:10.1016/0377-2217(86)90044-5
Brans, J. P.; Mareschal, B. 1992. PROMETHEE V- MCDM problems with segmentation constraints, INFOR 30(2): 85-96.
Brans, J. P.; Mareschal, B.; Vincke, P. 1984. PROMETHEE: a new family of outranking methods in multicriteria analysis, In J.P. Brans Ed., Operational Research '84IFORS 84. North Holland, 477-490.
Brauers, W. K. M.; Balezentis, A.; Balezentis, T. 2011. MULTIMOORA for the EU Member States updated with fuzzy number theory, Technological and Economic Development of Economy 17(2): 259-290. doi:10.3846/20294913.2011.580566
Brauers, W. K. M.; Ginevicius, R. 2009. Robustness in regional development studies. The case of Lithuania, Journal of Business Economics and Management 10(2): 121-140. doi:10.3846/1611-1699.2009.10.121-140
Brauers, W. K. M.; Ginevicius, R. 2010.The economy of the Belgian regions tested with MULTIMOORA, Journal of Business Economics and Management 11(2): 173-209. doi:10.3846/jbem.2010.09
Brauers, W. K. M.; Ginevicius, R.; Podvezko, V. 2010. Regional development in Lithuania considering multiple objectives by the MOORA method, Technological and Economic Development of Economy 16(4): 613-640. doi:10.3846/tede.2010.38
Brauers, W. K. M.; Zavadskas, E. K. 2006. The MOORA method and its application to privatization in a transition economy, Control and Cybernetics 35(2): 443-468.
Brauers, W. K. M.; Zavadskas, E. K. 2010a. Project management by MULTIMOORA as an instrument for transition economies, Technological and Economic Development of Economy 16(1): 5-24. doi:10.3846/tede.2010.01
Carlsson, C.; Fuller. R. 1996. Fuzzy multiple criteria decision making: Recent developments, Fuzzy Sets and Systems 78: 139-153. doi:10.1016/0165-0114(95)00165-4
Cebeci, U. 2009. Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard, Expert Systems with Applications 36(5): 8900-8909. doi:10.1016/j.eswa.2008.11.046
Chakraborty, S. 2011. Applications of the MOORA method for decision making in manufacturing environment, The International Journal of Advanced Manufacturing Technology 54(9-12): 1155-1166. doi:10.1007/s00170-010-2972-0
Chatterjee, P.; Athawale, V. M.; Chakraborty, S. 2011. Materials selection using complex proportional assessment and evaluation of mixed data methods, Materials & Design 32(2): 851-860. doi:10.1016/j.matdes.2010.07.010
Clark, B. 1998. Political-economy: A comparative approach. Westport, CT: Preager.
Cokorilo, O.; Gvozdenovic, S.; Mirosavljevic, P.; Vasov, L. 2010.Multi attribute decision making: Assessing the technological and operational parameters of an aircraft, Transport 25(4): 352-356. doi:10.3846/transport.2010.43
Colombo, S.; Angus, A.; Morris, J.; Parsons, D. J.; Brawn, M.; Stacey, K.; Hanley, N. 2009. A comparison of citizen and "expert" preferences using an attribute-based approach to choice, Ecological Economics 68(11): 2834-2841. doi:10.1016/j.ecolecon.2009.06.001
Datta, S.; Beriha, G. S.; Patnaik, B.; Mahapatra, S. S. 2009. Use of compromise ranking method for supervisor selection: A multi-criteria decision making (MCDM) approach, International Journal of Vocational and Technical Education 1(1): 7-13.
De Keyser, W.; Peters, P. 1994. ARGUS-a new multiple criteria method based on the general idea of outranking. Applying multiple criteria aid for decision to environmental management (Ed. by M. Paruccini), 263-278. Kluwer, Dordrecht.
Dias, L. C.; Clkmaco, J. N. 2000. Additive aggregation with variable independent parameters: The VIP Analysis software, Journal of the Operational Research Society 51(9): 1070-1082.
Dias, L.; Mousseau, V.; Figueira, J.; Clkmaco, J.; Silva, C. G. 2002. IRIS 1.0 software, Newsletter of the European Working Group Multicriteria Aid for Decisions 3(5): 4-6.
Durkheim, E. 1984. The Division of Labour in Society. London: Macmillan.
Elster, J. 1983. Sour Grapes: Studies in the Subversion of Rationality. Cambridge, UK: Cambridge University Press.
Figueira, J.; Greco, S.; Ehrgott, M. (Eds.). 2005. Multiple Criteria Decision Analysis: State of the Art Surveys. Springer.
Fishburn, P. C. 1965. Independence in utility theory with whole product sets, Operations Research 13(1): 28-45. doi:10.1287/opre.13.1.28
Fishburn, P. C. 1968. Utility theory, Management Science 14(5): 335-378. doi:10.1287/mnsc.14.5.335
Forecasting Principles. Evidence-based Forecasting. 2011 [online], [accessed 5 May 2011]. Available from Internet: <http://www.forecastingprinciples.com/index.php?option=com_content&task= view&id=16&Itemid=16>.
Garcia Alcaraz, J. L.; Romero Gonzalez, J.; Canales Valdivieso, I. 2010. Seleccion de proveedores usando el metodo MOORA, CULCyT 7(40-41): 94-105. Available from Internet: <http://www2.uacj.mx/ IIT/CULCYT/Septiembre-diciembre2010/12%20Art.9.pdf>.
Ghazinoory, S.; Divsalar, A.; Soofi, A. S.2009. A new definition and framework for the development of a national technology strategy: The case of nanotechnology for Iran, Technological Forecasting and Social Change 76(6): 835-848. doi:10.1016/j.techfore.2008.10.004
Gigerenzer, G.; Selten, R. 2002. Bounded Rationality. Cambridge: MIT Press.
Ginevicius, R. 2008. Normalization of Quantities of Various Dimensions, Journal of Business Economics and Management 9(1): 79-86. doi:10.3846/1611-1699.2008.9.79-86
Ginevicius, R.; Krivka, A. 2008. Application of game theory for duopoly market analysis, Journal of Business Economics and Management 9(3): 207-217. doi:10.3846/1611-1699.2008.9.207-217
Ginevicius, R.; Krivka, A.; Simkunaite, J. 2010. The model of forming competitive strategy of an enterprise under the conditions of oligopolic market, Journal of Business Economics and Management 11(3): 367-395. doi:10.3846/jbem.2010.18
Ginevicius, R.; Podvezko, V. 2006. Assessing the financial state of construction enterprises, Technological and Economic Development of Economy 12(3): 188-194. doi:10.1080/13928619.2006.9637740
Ginevicius, R.; Podvezko, V. 2008. Multicriteria Evaluation of Lithuanian Banks from the Perspective of their Reliability for clients, Journal of Business Economics and Management 9(4): 257-267. doi:10.3846/1611-1699.2008.9.257-267
Gomes, L. F. A. M.; Rangel, L. A. D. 2009. Determining the utility functions of criteria used in the evaluation of real estate, International Journal of Production Economics 117(2): 420-426. doi:10.1016/j.ijpe.2008.12.006
Greco, S.; Matarazzo, B.; Slowinski, R. 1999. The use of rough sets and fuzzy sets in MCDM, in: Gal, T.; Hanne, T. (Eds.). Advances in Multiple Criteria Decision Making.
Greco, S.; Matarazzo, B.; Slowinski, R. 2000. Extension of the rough set approach to multicriteria decision support, Information Systems and Operational Research (INFOR) 38(3): 161-196.
Greco, S.; Matarazzo, B.; Slowinski, R. 2001. Rough sets theory for multicriteria decision analysis, European Journal of Operational Research 129(3): 1-47. doi:10.1016/S0377-2217(00)00167-3
Guitoni, A.; Martel, J. M. 1998. Tentative guidelines to help choosing an appropriate MCDA method, European Journal of Operational Research 109: 501-521. doi:10.1016/S0377-2217(98)00073-3
Hadi-Vencheh, A.; Niazi-Motlagh, M. 2011. An improved voting analytic hierarchy process-data envelopment analysis methodology for suppliers selection, International Journal of Computer Integrated Manufacturing 24(3): 189-197. doi:10.1080/0951192X.2011.552528
Han, Z.; Liu, P. 2011. A fuzzy multi-attribute decision-making method under risk with unknown attribute weights, Technological and Economic Development of Economy 17(2): 246-258.
Herrera, F.; Herrera-Viedma, E.; Martinez, L. 2000. A fusion approach for managing multigranularity linguistic term sets in decision making, Fuzzy Sets and Systems 114(1): 43-58. doi:10.1016/S0165-0114(98)00093-1
Hindess, B. 1988. Choice, Rationality and Social Theory. London: Unwin Hyman.
Hwang, C. L.; Yoon, K. 1981. Multiple Attribute Decision Making: A State of the Art Survey, in Lecture Notes in Economics and Mathematical Systems 186, Springer-Verlag, Berlin.
Industrial Engineering and Production Management. Scientific Method and Operations Research. 2011. Available from Internet: <http://www.uv.es/EBRIT/macro/macro_5003_8_17.html>.
Ivanov, S.; Stanujkic, D. 2010. Software selection through the application of the multicriteria decision-making method [online]. Available from Internet: <http://www.e-drustvo.org/proceedings/YuInfo2010/html/pdf/050.pdf>.
Jacquet-Lagreze, E.; Siskos, Y. 1982. Assessing a set of additive utility functions for multicriteria decision making, the UTA method, European Journal of Operational Research 10(2): 151-164. doi:10.1016/0377-2217(82)90155-2
Jakimavicius, M.; Burinskiene, M. 2007. Automobile transport system analysis and ranking in Lithuanian administrative regions, Transport 22(3): 214-220. doi:10.1080/16484142.2007.9638127
Jakimavicius, M.; Burinskiene, M. 2009. A GIS and multi-criteria-based analysis and ranking of transportation zones of Vilnius city, Technological and Economic Development of Economy 15(1): 39-48. doi:10.3846/1392-8619.2009.15.39-48
Jin, F.; Liu, P. 2010. The multi-attribute group decision making method based on the interval grey linguistic variables, African Journal of Business Management 4(17): 3708-3715.
Johnson-Laird, P. N.; Byrne, R. M. J. 1991. Deduction. Hillsdale: Erlbaum.
Juan Y.-K. 2010. Optimal decision making on urban renewal projects, Management Decision 48(2): 207-224. doi:10.1108/00251741011022581
Juttler, H. 1966. Untersuchungen zur Fragen der Operations aforschungund ihrer Anwendungsmoglichkeiten auf okonomische Problemstellungen unter besondererBerucksichtigung der Spieltheorie: Dissertation A an der Wirtschaftswissenschaftlichen Fakultat der Humboldt-Universitat, Berlin.
Kaplinski, O. 2008a. Usefulness and credibility of scoring methods in construction industry, Journal of Civil Engineering and Management 14(1): 21-28. doi:10.3846/1392-3730.2008.14.21-28
Kaplinski, O. 2008b. Planing Instruments in Construction Management, Technological and Economic Development of Economy 14(4): 449-451. doi:10.3846/1392-8619.2008.14.449-451
Kaplinski, O. 2008c. Development and Usefulness of Planning Techniques and Decision-Making Foundations on the Example of Construction Enterprises in Poland, Technological and Economic Development of Economy 14(4): 492-502. doi:10.3846/1392-8619.2008.14.492-502
Kaplinski, O.; Tamosaitiene, J. 2010. Game theory applications in construction engineering and management, Technological and Economic Development ofEconomy 16(2): 348-363. doi:10.3846/tede.2010.22
Kaplinski, O.; Tupenaite, L. 2011. Review of the Multiple Criteria Decision Making Methods, Intelligent and Biometric Systems Applied in Modern Construction Economics, Transformations in Business & Economics 10(1): 166-181.
Karbassi, A. R.; Abduli, M. A.; Neshastehriz, S. 2008. Energy saving in Tehran international flower exhibition's building, International Journal of Environmental Research 2(1): 75-86.
Katona, G. 1953. Rational behaviour and economic behaviour, Psychological Review 60(5): 307-318. doi:10.1037/h0060640
Kaya, T.; Kahraman, C. 2011. A fuzzy approach to e-banking website quality assessment based on an integrated AHP-ELECTRE method, Technological and Economic Development ofEconomy 17(2): 313-334.
Keeney R. L.; von Winterfeldt, D. 2001. Appraising the precautionary principle--a decision analysis perspective, Journal of Risk Research 14(2): 191-202. doi:10.1080/13669870010027631
Keeney, R. L. 1982. Decision Analysis: An Overview, Operations Research 30(5): 803-838. doi:10.1287/opre.30.5.803
Keeney, R. L.; Raiffa, H. 1976. Decision with multiple objectives: Preferences and value tradeoffs. New York: John Wiley & Sons.
Kersuliene, V.; Zavadskas, E. K.; Turskis, Z. 2010. Selection of rational dispute resolution method by applying new step-wise weight assessment ratio analysis (SWARA), Journal of Business Economics and Management 11(2): 243-258. doi:10.3846/jbem.2010.12
Korth, H. 1969. Zur Berucksichtigung mehrer Zielfunktionen bei der Optimierung von Produktionsplanen, Mathematik und Wirtschaft 6: 184-201
Larichev, O. I.; Brown, R. V. 2000. Numerical and verbal decision analysis: comparison on practical cases, Journal of Multi-Criteria Decision Analysis 9(6):263-273. doi:10.1002/1099-1360(200011)9:6<263::AID MCDA280>3.0.CO;2-W
Larichev, O. I.; Moshkovich, E. M. 1997. Verbal decision analysis for unstructured problems. Boston: Kluwer Academic Publishers.
Larichev, O. 2000. Decision-making theory and methods. Moscow: Logos. 295.
Leclercq, J. P. 1984. Propositions d'extension de la notion de dominance en presence de relations d'ordre sur les pseudo-criteres: MELCHIOR, Revue Belge de Recherche Operationnelle, de Statistique et dInformatique 24(1): 32-46.
Liaudanskiene, R.; Ustinovicius, L.; Bogdanovicius, A. 2009. Evaluation of Construction Process Safety Solutions Using the TOPSIS Method, Inzinerine Ekonomika-Engineering Economics (4): 32-40.
Liu, P. D. 2009. Multi-attribute decision-making method research based on interval vague set and TOPSIS method, Technological and Economic Development of Economy 15(3): 453-463. doi:10.3846/1392-8619.2009.15.453-463
Liu, P.; Zhang, X. 2011. Investigation into evaluation of agriculture informatization level based on twotuple, Technological and Economic Development of Economy 17(1): 74-86. doi:10.3846/13928619.2011.554007
Liu, W.; Liu, P. 2010. Hybrid multiple attribute decision making method based on relative approach degree of grey relation projection, African Journal of Business Management 4(17): 3716-3724.
Lootsma, F. A. 1990. The French and the American School in Multi-criteria Decision Analysis, in 9th International Conference on Multiple Criteria Decision Making--Theory and applications in business, industry, and government, Fairfax, Virginia, USA, 253-267.
Lootsma, F. A. 1992. The REMBRANDT systemfor multi-criteria decision analysis via pairwise comparisons or direct rating: Technical Report 92-05, Faculty of Technical Mathematics and Informatics, Delft University of Technology, Delft, Netherlands.
Lootsma, F. A. 1993. Scale sensitivity in the multiplicative AHP and SMART, Journal of Multi-Criteria Decision Analysis 2(2): 87-110. doi:10.1002/mcda.4020020205
Lootsma, F. A.; Mensch, T. C.A.; Vos, F. A. 1990. Multi-Criteria Analysis and Budget Reallocation in Long-Term Research Planning, European Journal of Operational Research 47: 293-305. doi:10.1016/0377-2217(90)90216-X
MacCrimon, K. R. 1968, Decision Marking Among Multiple-Attribute Alternatives: A Survey and Consolidated Approach, RAND Memorandum, RM-4823-ARPA. The Rand Corporation, Santa Monica, Calif.
Maskeliunaite, L.; Sivilevicius, H.; Podvezko, V. 2009. Research on the quality of passenger transportation by railway, Transport 24(2): 100-112. doi:10.3846/1648-4142.2009.24.100-112
Matarazzo, B. 1986. Multicriterion Analysis of Preferences by means of Pairwise Actions and Criterion comparisons (MAPPAC), Applied Mathematics and Computation 18(2): 119-141. doi:10.1016/0096-3003(86)90020-2
Matarazzo, B. 1988a. Preference Ranking Global frequencies in Multicriterion Analysis (PRAGMA), European Journal of Operational Research 36(1): 36-49. doi:10.1016/0377-2217(88)90005-7
Matarazzo, B. 1988b. A more effective implementation ofthe MAPPAC and PRAGMA methods, Foundations of Control Engineering 13: 155-173.
Mitkova, V.; Mlynarovic, V. 2007.A Performance and Risk Analysis on the Slovak Private Pension Funds Market, Ekonomicky casopis / Journal of Economics 55(3): 215-231.
Nowak, M. 2005.Investment projects evaluation by simulation and multiple criteria decision aiding procedure, Journal of Civil Engineering and Management 11(3): 193-202.
Olson, D. L.; Fliedner, G.; Currie, K. 1992. Comparison of the REMBRANDT System with Analytic Hierarchy Process, European Journal of Operational Research 82: 522-541. doi:10.1016/0377-2217(93)E0340-4
Opricovic, S. 1998. Multiple criteria optimization of civil engineering systems. Belgrade: Faculty of Civil Engineering.
Palma, J.; Graves, A. R.; Burgess, P. J.; Werf, W. van der; Herzog, F. 2007. Integrating environmental and economic performance to assess modern silvoarable agroforestry in Europe, Ecological Economics 63(4): 759-767. doi:10.1016/j.ecolecon.2007.01.011
Pareto, V. 1971. Manual of Political Economy. New York: A. M. Kelley.
Pawlak, Z.; Grzymala-Busse, J.; Slowinski, R.; Ziarko, W. 1995. Rough Sets, Communications of the ACM 38(11): 89-95. doi:10.1145/219717.219791
Peldschus, F. 2008. Experience of the game theory application in construction management, Technological and Economic Development ofEconomy 14(4): 531-545. doi:10.3846/1392-8619.2008.14.531-545
Peldschus, F. 2009. The analysis of the quality of the results obtained with the methods of multi-criteria decisions, Technological and Economic Development of Economy 15(4): 580-592. doi:10.3846/1392-8619.2009.15.580-592
Pitz, G. F. 1987. DECAID Computer Program. Carbondale, IL: Univ. Of Southern Illinois.
Podvezko, V. 2011. The Comparative Analysis of MCDA Methods SAW and COPRAS, Inzinerine Ekonomika-Engineering Economics 22(2): 134-146.
Podvezko, V. 2009. Application of AHP technique, Journal of Business Economics and Management 10(2): 181-189. doi:10.3846/1611-1699.2009.10.181-189
Podvezko, V.; Mitkus, S.; Trinkuniene, E. 2010. Complex evaluation of contracts for construction, Journal of Civil Engineering and Management 16(2): 287-297. doi:10.3846/jcem.2010.33
Podvezko, V.; Podviezko, A. 2010. Dependence of multi-criteria evaluation result on choice of preference functions and their parameters, Technological and Economic Development ofEconomy 16(1): 143-158. doi:10.3846/tede.2010.09
Radziszewska-Zielina, E. 2010.Methods for selecting the best partner construction enterprise in terms of partnering relations, Journal of Civil Engineering and Management 16(4): 510-520. doi:10.3846/jcem.2010.57
Roubens, M. 1982. Preference relations on actions and criteria in multi-criteria decision making, European Journal of Operational Research 10(1): 51-55. doi:10.1016/0377-2217(82)90131-X
Roy, B. 1996. Multicriteria Methodology for Decision Aiding. Dortrecht: Kluwer Academic Publishers.
Roy, B. 1968. Classement et choix en presence de point de vue multiples: Le methode ELECTRE, Revue Francaise d'Informatique et de Recherche Operationnelle (RIRO) 8: 57-75.
Roy, B. 1978. ELECTRE III: Un algorithme de rangement fonde sur une representation floue des preferences en presence de criteres multiples, Cahiers du Centre detudes de recherche operationnelle 20: 3-24.
Roy, B. 1988. Des criteres multiples en recherche operationnelle: pourquoi ?, in G. K. Rand (Ed.), Operational Research '87, 829-842, North-Holland, Amsterdam. doi:10.1016/0377-2217(90)90196-I
Roy, B. 1990. Decision-aid and decision making, European Journal of Operational Research 45(2-3): 324-331. doi:10.1007/BF00134132
Roy, B. 1991. The outranking approach and the foundations of ELECTRE methods, Theory and Decision 31: 49-73
Rudzianskaite-Kvaraciejiene, R.; Apanaviciene, R.; Butauskas, A. 2010. Evaluation of Road Investment Project Effectiveness, Inzinerine Ekonomika-EngineeringEconomics 21(4): 368-376.
Saaty, T. L. 1977. A Scaling Method for Priorities in Hierarchical Structures, Journal of Mathematical Psychology 15: 234-281. doi:10.1016/0022-2496(77)90033-5
Saaty, T. L. 1980. The Analytical Hierarchy Process. New York: McGraw-Hill.
Saaty, T. L.; Vargas, L. G.; Dellmann, K. 2003. The allocation of intangible resources: the analytic hierarchy process and linear programming, Socio-Economic Planning Sciences 37(3): 169-184. doi:10.1016/S0038-0121(02)00039-3
Savage, C. J. 1954. Foundation of statistics. New York: Wiley & Sons.
Seo, F. 1981. Organizational aspects of multicriteria decision making, in Lecture Notes in Economics and Mathematical System. Berlin, Heidelberg, New York, 363-379.
Shevchenko, G.; Ustinovicius, L.; Andruskevicius, A. 2008. Multi-attribute analysis of investments risk alternatives in construction, Technological and Economic Development of Economy 14(3): 428-443. doi:10.3846/1392-8619.2008.14.428-443
Siskos, Y.; Spyridakos, A. 1999. Intelligent multicriteria decision support: Overview and perspectives, European Journal of Operational Research 113(2): 236-246. doi:10.1016/S0377-2217(98)00213-6
Sivilevicius, H.; Maskeliunaite, L. 2010. The criteria for identifying the quality of passengers' transportation by railway and their ranking using AHP method, Transport 25(4): 368-381. doi:10.3846/transport.2010.46
Smith, G. R.; Speiser, F. 1991. Logical Decision: Multi-Measure Decision Analysis Software. Golden, CO: PDQ Printing.
Srinivasan, V; Kim Y. H. 1987. Credit granting: a comparative analysis of classification procedures, Journal of Finance 42(3): 665-683. doi:10.2307/2328378
Srinivasan, V.; Shocker, A. D. 1973. Linear Programming techniques for multidimensional analysis of privileged, Psychometrika 38: 337-369. doi:10.1007/BF02291658
Stein, H. D. 2010. Allocation rules with outside option in cooperation games with time-inconsistency, Journal of Business Economics and Management 11(1): 56-96. doi:10.3846/jbem.2010.04
Stein, H. D.; Ginevicius, R. 2010. The experimental investigation of the profit distribution in industrial supply chains with an outside option, Technological and Economic Development of Economy 16(3): 487-501. doi:10.3846/tede.2010.30
Stemberger, M. I.; Bosilj-Vuksic, V.; Jaklic, J. 2009. Business process management software selection-two case studies, Economic Research 22(4): 84-99.
Steuten, L. M. G.; Hummel, M. J. M.; Izerman, M. J. 2010. Using AHP weights to fill missing gaps in Markov decision models, in Value in health 13, 241. Prague.- UT-I-IGS-GoI, UT-I-IGS-MoI.
Stopp, F. 1975. Variantenvergleich durch Matrixspiele, Wissenschaftliche Zeitschrift der Hochschule fur Bauwesen Leipzig 2, 117.
Tamosaitiene, J.; Bartkiene, L.; Vilutiene, T. 2010. The New Development Trend of Operational Research in Civil Engineering and Sustainable Development as a result of collaboration between German-Lithuanian-Polish Scientific Triangle, Journal of Business Economics and Management 11(2): 316-340. doi:10.3846/jbem.2010.16
Tanino, T.; Nakayama, H.; Swaragi, Y. 1981. Methodology for group decision support, in Lecture Notes in Economics and Mathematical System. Berlin, Heidelberg, New York, 409-423.
Thiel, T. 2008. Determination of the relative importance of criteria when the number of people judging is a small sample, Technological and Economic Development of Economy 14(4): 566-577. doi:10.3846/1392-8619.2008.14.566-577
Tomic-Plazibat, N.; Aljinovic, Z.; Pivac, S. 2010. Risk Assessment of Transitional Economies by Multivariate and Multicriteria Approaches, PANOECONOMICUS 57(3): 283-302. doi:10.2298/PAN1003283T
Tsoukias, A.; Vincke, P. A survey on non conventional Preference Modeling [online], [accessed 9 May 2011]. Available from Internet: <http://www.lamsade.dauphine.fr/~tsoukias/papers/SURVEY.pdf>.
Turskis, Z. 2008. Multi-attribute contractors ranking method by applying ordering of feasible alternatives of solutions in terms of preferability technique, Technological and Economic Development of Economy 14(2): 224-239. doi:10.3846/1392-8619.2008.14.224-239
Turskis, Z.; Zavadskas E. K. 2010b. A Novel Method for Multiple Criteria Analysis: Grey Additive Ratio Assessment (ARAS-G) Method, Informatica 21(4): 597-610.
Turskis, Z.; Zavadskas, E. K. 2010a. A new fuzzy additive ratio assessment method (ARAS-F). Case study: The analysis of fuzzy multiple criteria in order to select the logistic centers location, Transport 25(4): 423-432. doi:10.3846/transport.2010.52
Ulubeyli, S.; Kazaz, A. 2009. A multiple criteria decision-making approach to the selection of concrete pumps, Journal of Civil Engineering and Management 15(4): 369-376. doi:10.3846/1392-3730.2009.15.369-376
Ustinovichius, L.; Barvidas, A.; Vishnevskaja, A.; Ashikhmin, I. V. 2009. Multicriteria verbal analysis for the decision of construction problems, Technological and Economic Development of Economy 15(2): 326-340. doi:10.3846/1392-8619.2009.15.326-340
Ustinovichius, L.; Barvidas, A.; Vishnevskaja, A.; Ashikhmin, I. V. 2011. Multicriteria verbal analysis of territory planning system's models from legislative perspective, Journal of Civil Engineering and Management 17(1): 16-26. doi:10.3846/13923730.2011.554173
Ustinovichius, L.; Shevchenko, G.; Barvidas, A.; Ashikhmin, I. V.; Kochin, D. 2010. Feasibility of verbal analysis application to solving the problems of investment in construction, Automation in Construction 19(3): 375-384. doi:10.1016/j.autcon.2009.12.004
Uzsilaityte, L.; Martinaitis, V. 2010. Search for optimal solution of public building renovation in terms of life cycle, Journal of Environment Engineering and Landscape Management 18(2): 102-110. doi:10.3846/jeelm.2010.12
Vallee, D.; Zielniewicz, P. 1994. ELECTRE III-IV, version 3.x, Aspects Methodologiques (tome 1), Guide dutilisation (tome 2). Document du LAMSADE 85 et 85bis, Universite Paris Dauphine.
Vansnick, J. C. 1986. On the problem of weights in multiple criteria decision making (the non-compensatory approach), European Journal of Operational Research 24: 288-294. doi:10.1016/0377-2217(86)90051-2
Vincke, P. 1992. Multicriteria Decision Aid. Wiley: New York.
Von Neumann, J.; Morgenstern, O. 1944. Theory of games and economic behaviour. Princeton: Princeton University Press
Von Winterfeldt, D.; Edwards, W. 1986. Decision Analysis and Behavioural Research. Cambridge: Cambridge University Press.
Wachowicz, T. 2010. Decision support in software supported negotiations, Journal of Business Economics and Management 11(4): 576-597. doi:10.3846/jbem.2010.28
Wang, J.-J.; Jing, Y.-Y.; Zhang, C.-F.; Zhao, J.-H. 2009. Review on multi-criteria decision analysis aid in sustainable energy decision-making, Renewable and Sustainable Energy Reviews 13(9): 2263-2278. doi:10.1016/j.rser.2009.06.021
Weber, M. 2011. Sociology 3210-Sociological Theory: Weber [online], [accessed 4 May 2011]. Available from Internet: <http://www.umsl.edu/~keelr/3210/3210_lectures/weber.html>.
Weintraub, E. R. 2007. Neoclassical Economics. The Concise Encyclopedia of Economics [online], [ccessed May 4 2011].Available from Internet: <http://www.econlib.org/library/Enc1/ NeoclassicalEconomics.html>.
Weitendorf, D. 1976. Beitrag zur Optimierung der rdumlichen Struktur eines Gebaudes. Dissertation A, Hochschule fur Architektur und Bauwesen. Weimar.
Wu, H.-Y.; Tzeng, G.-H.; Chen, Y.-H.2009. A fuzzy MCDM approach for evaluating banking performance based on Balanced Scorecard, Expert Systems with Applications 36(6): 10135-10147. doi:10.1016/j.eswa.2009.01.005
Yan, M. R.; Pong, C. S.; Lo, W. 2011. Utility-based multicriteria model for evaluating BOT projects, Technological and Economic Development of Economy 17(2): 207-218.
Zadeh, L. A. 1975a. Fuzzy logic and its application to approximate reasoning, Part I, Information Science 8(3): 199-249. doi:10.1016/0020-0255(75)90036-5
Zadeh, L. A. 1975b. Fuzzy logic and its application to approximate reasoning, Part II, Information Science 8(4): 301-357. doi:10.1016/0020-0255(75)90046-8
Zadeh, L. A. 1975c. Fuzzy logic and its application to approximate reasoning, Part III, Information Science 9(1): 43-80. doi:10.1016/0020-0255(75)90017-1
Zahedi, F. 1986. The analytic hierarchy process--a survey of the method and its applications, Interfaces 16(4): 96-108. doi:10.1287/inte.16.4.96
Zanakis, S. H.; Solomon, A.; Wishart, N.; Dublish, S. 1998. Multi-attribute decision making: A simulation comparison of select methods, European Journal of Operational Research 107: 507-529. doi:10.1016/S0377-2217(97)00147-1
Zapounidis, C.; Doumpos, M. 2002. Multicriteria classification and sorting methods: a literature review, European Journal of Operational Research 138(2): 229-246. doi:10.1016/S0377-2217(01)00243-0
Zavadskas, E. K. 1987. Multiple criteria evaluation of technological decisions of construction. Dissertation of Dr. Sc. Moscow Civil Engineering Institute, Moscow.
Zavadskas, E. K.; Kaklauskas, A. 1996. Determination of an efficient contractor by using the new method of multicriteria assessment. In Langford, D. A.; Retik, A. (Eds.) International Symposium for "The Organisation and Management of Construction". Shaping Theory and Practice2: Managing the Construction Project and Managing Risk. CIB W 65; London, Weinheim, New York, Tokyo, Melbourne, Madras.--London: E and FN SPON: 94-104.
Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamosaitiene, J. 2009b. Multi-Attribute Decision-Making Model by Applying Grey Numbers, Informatica 20(2): 305-320.
Zavadskas, E. K.; Kaklauskas, A.; Turskis, Z.; Tamosaitiene, J. 2008. Selection of the effective dwelling house walls by applying attributes values determined at intervals, Journal of Civil Engineering and Management 14(2): 85-93. doi:10.3846/1392-3730.2008.14.3
Zavadskas, E. K.; Turskis, Z. 2008. A new logarithmic normalization method in games theory, Informatica 19(2): 303-314.
Zavadskas, E. K.; Turskis, Z. 2010. A new additive ratio assessment (ARAS) method in multicriteria decision-making, Technological and Economic Development of Economy 16(2): 159-172. doi:10.3846/tede.2010.10
Zeleny, M. 1977. Multidimensional measure of risk: the prospect ranking vector. In: Multiple Criteria Problem Solving, Zionts, S. (Ed.), Springer: Heidelberg; 529-548.
Zeleny, M. 1982. Multiple criteria decision making. New York: McGraw-Hill.
Zhu, W. 2009. Relationship among basic concepts in covering-based rough sets, Information Sciences 179(14): 2478-2486. doi:10.1016/j.ins.2009.02.013
Zimmermann, H. - J. 2000. An application-oriented view of modelling uncertainty, European Journal of Operational Research 122(2): 190-198. doi:10.1016/S0377-2217(99)00228-3
Zimmermann, H. J. 1985. Fuzzy set theory and its applications. Dordrecht: Kluwer Academic.
Zopounidis, C.; Doumpos, M. 2002. Multi-criteria Decision Aid in Financial Decision Making: Methodologies and Literature Review, Journal of Multi-Criteria Decision Analysis 11: 167-186. doi:10.1002/mcda.333
Zvirblis, A.; Buracas, A. 2010. The consolidated measurement of the financial markets development: the case of transitional economies, Technological and Economic Development ofEconomy 16(2): 266-279. doi:10.3846/tede.2010.17
Zvirblis, A.; Zinkeviciute, V. 2008. The integrated evaluation of the macro environment of companies providing transport services, Transport (23)3: 266-272. DOI: 10.3846/1648-4142.2008.23.266-272. doi:10.3846/1648-4142.2008.23.266-272
[TEXT NOT REPRODUCIBLE IN ASCII.]. (Vaigauskas, E.; Zavadskas, E. 1980. Use of utility function for an optimum variant of building choice. Vilnius Civil Engineering Institute, Vilnius.)
Edmundas Kazimieras Zavadskas (1), Zenonas Turskis (2)
Vilnius Gediminas Technical University, Faculty of Civil Engineering, Sauletekio al. 11, LT-10223 Vilnius, Lithuania
E-mails: (1) email@example.com (corresponding author); (2) firstname.lastname@example.org
Received 10 January 2011; accepted 5 May 2011
Edmundas Kazimieras ZAVADSKAS. Prof., Head of the Department of Construction Technology and Management at Vilnius Gediminas Technical University, Vilnius, Lithuania. He has a PhD in Building Structures (1973) and Dr Sc. (1987) in Building Technology and Management. He is a member of the Lithuanian and several foreign Academies of Sciences. He is Doctore Honoris Causa at Poznan, Saint-Petersburg, and Kiev universities as well as a member of international organisations; he has been a member of steering and programme committees at many international conferences. E. K. Zavadskas is a member of editorial boards of several research journals. He is the author and co-author of more than 400 papers and a number of monographs in Lithuanian, English, German and Russian. Research interests are: building technology and management, decision-making theory, automation in design and decision support systems.
Zenonas TURSKIS has a PhD and is a chief research worker at Laboratory of Construction Technology and Management in Vilnius Gediminas Technical University, Lithuania. His research interests include building technology and management, decision-making theory, computer-aided automation in design and expert systems. He is the author of more than 80 research papers.
Table 1. Dynamics of multiple criteria decision making applications in economics (this table is based on the search in sciencedirect.com accessed on 9 May 2011) Decision-making & economics (keywords) Year & & & of multiple multi multi- publication criteria criteria attribute A B C D 2011 6688 2280 358 110 2010 9694 3119 316 101 2009 8965 2870 262 99 2008 8264 2498 242 89 2007 7284 2260 182 70 2006 6416 1866 123 51 2005 5294 1494 125 60 2004 5266 1360 102 55 2003 4510 1164 88 43 2002 3760 992 88 55 2001 3645 916 68 41 2000 3165 788 52 50 1999 2796 691 73 54 1998 2879 724 79 71 1997 2763 649 74 70 1996 2668 641 77 90 1996 2562 565 68 59 1994 2300 515 36 47 1993 1979 433 59 50 [less than or equal to] 1992 21914 4170 370 356 Total 112812 29995 2842 1621 Decision-making & economics (keywords) Year & & & Total of multiple multiple multi- B-G publication attribute objective objective E F G 2011 4177 8429 453 15807 2010 5945 12165 497 22143 2009 5448 11441 451 20571 2008 4990 10185 375 18379 2007 4617 9197 364 16690 2006 3856 8054 280 14230 2005 3048 6562 228 11517 2004 2881 5953 193 10544 2003 2468 5056 164 8983 2002 1956 4121 132 7344 2001 1839 3838 127 6829 2000 1638 3380 135 6043 1999 1441 3000 119 5378 1998 1416 2968 142 5400 1997 1297 2688 151 4929 1996 1203 2520 180 4711 1996 1133 2246 140 4211 1994 1052 2079 165 3894 1993 888 1835 132 3397 [less than or equal to] 1992 8708 17773 1101 32478 Total 60001 123490 5529 223478 Table 2. Dynamics of fuzzy multiple criteria decision making applications in economics (this table is based on the search in sciencedirect.com (accessed on 9 May 2011)) Decision making (fuzzy) economics (keywords) Year of multiple multi- multi- publication criteria criteria attribute A B C D 2011 733 1,200 335 91 2010 827 1,295 344 91 2009 843 1,293 325 70 2008 601 936 288 54 2007 538 941 278 60 2006 432 702 214 57 2005 322 530 128 32 2004 248 466 122 39 2003 200 381 101 25 2002 159 332 77 28 2001 163 293 68 25 2000 156 283 82 19 1999 121 252 72 17 1998 139 241 88 23 1997 111 250 91 37 1996 127 238 62 28 1996 136 209 68 34 1994 120 224 63 35 1993 80 173 40 13 [less than or equal to] 1992 745 1,244 394 159 Total 6,801 11,483 3,240 937 Decision making (fuzzy) economics (keywords) Year of multiple multiple multi Total publication attribute objectives objective B-G E F G 2011 856 1,179 233 3,894 2010 885 1,315 238 4,168 2009 931 1,306 242 4,167 2008 666 952 178 3,074 2007 614 926 179 2,998 2006 450 695 139 2,257 2005 338 525 99 1,652 2004 292 475 84 1,478 2003 257 377 68 1,209 2002 202 339 58 1,036 2001 182 288 55 911 2000 172 281 55 892 1999 164 255 41 801 1998 145 252 65 814 1997 175 232 55 840 1996 137 234 53 752 1996 138 190 48 687 1994 138 224 73 757 1993 93 157 48 524 [less than or equal to] 1992 777 1,151 282 4,007 Total 7,612 11,353 2,293 36,918 Table 3. Backgrounds of multiple criteria decision making approaches and the earliest applications Multiple attribute utility theory (MAUT) Methods Studies Comments LOGICAL Keeney and Raiffa Background MAUT DECISION (1976) Smith and Speiser Decision support (1991) system based on the MAUT DECAID Pitz (1987) Decision support system based on the MAUT [TEXT NOT Investigation of REPRODUCIBLE IN MAUT practical ASCII] (1980) applications Simple Additive MacCrimon (1968) Author Weighting (SAW) Linear Srinivasan and Authors Programming Shocker (1973) Techniques for Multidimensional Analysis of Preference (LINMAP) Analytic Hierarchy Saaty (1977, 1980); Author of AHP Process (AHP) Analytic Hierarchy Lootsma (1993) Multiplicative AHP Process (AHP) is an exponential version of the simple multi- attribute rating technique (SMART) Utility Theory Jacquet-Lagreze and Authors Additive (UTA) Siskos (1982) TOPSIS Hwang and Yoon Authors (1981) TOPSIS Antucheviciene et The case study al. (2010) proved that the proposed TOPSIS-M (TOPSIS applying Mahalanobis distance measure) Multicriterion Matarazzo (1986, Author Analysis of 1988b) Preferences by means of Pairwise Alternatives and Criterion comparisons (MAPPAC) PRAGMA Matarazzo (1988a, Author 1988b) Measuring Bana e Costa and Authors Attractiveness by a Vansnick (1994) Categorical Based Evaluation TecHnique. (MACBETH) Complex Proportional Zavadskas and Authors Assessment (COPRAS) Kaklauskas (1996) Complex Proportional Zavadskas et al. Authors. Ranking of ASsessment method (2008) alternatives with Grey interval numbers (COPRAS-G) REMBRANDT Lootsma (1992) Olson etAuthor Users Multi-Objective Brauers and Authors Optimization by Zavadskas (2006) Ratio Analysis Method (MOORA) MULTIMOORA Brauers and Authors. Full Zavadskas (2010) Multiplicative Form is added to MOORA. Additive Ratio Zavadskas and Authors of new Assessment method Turskis (2010) method (ARAS) ARAS-F Turskis and Authors. Fuzzy set Zavadskas (2010a) applied to location problem. ARAS-F presented ARAS-G Turskis and Authors. Grey Zavadskas (2010b) relations applied to problem solution. ARAS-G presented Step-wise weight Kersuliene et al. Selection of assessment ratio (2010) rational dispute analysis (SWARA) resolution method by applying new step-wise weight assessment ratio analysis Elimination Et Choix Benayoun et al. First publication Traduisant la (1966) Author Explains the REalite (ELimination bases of general and Choice Roy (1968, 1991) decision making Expressing REality) methodology which (ELECTRE) Roy (1978, 1990, took shape toward 1996) end of 1960s. The evolutions have continued with ELECTRE II, ELECTRE III, ELECTRE IV, ELECTRE IS and ELECTRE TRI. ELECTRE III and IV Vallee and Practical Zielniewicz (1994) realization, provided with software Organization, Roubens (1982) Author Rangement Et Synthese de dones relaTionnElles (ORESTE) Preference Ranking Brans et al. (1984, Authors Organization Method 1986) for Enrichment Evaluation Brans and Mareschal PROMETHEE V method (PROMETHEE) (1992) presented Zahedi (1986) Reviewed the AHP and its applications in diverse decision problems. It addresses some of the major extensions and criticisms of the method, as well. MELCHIOR Authors Leclercq (1984) Tratement des Author Actions Compte Tenu Vansnick (1986) de l'Importance des Crite'res (TACTIC) ARGUS De Keyser and Peters Author (1994) VIP Dias and Clkmaco Analysis software. (2000) Authors IRIS Dias et al. (2002) Analysis software. Authors Compromise ranking Opricovic (1998) Author method (VIKOR) Table 4. Recent applications of multiple criteria decision making approaches in economics Method Reference Considered problem AHP Ananda and Herath AHP is used to (2008) synthesise stakeholder preferences related to regional forest planning and to incorporate stakeholder preferences. Cebeci (2009) Presented an approach to select a suitable enterprise resource planning system for textile industry. Fuzzy AHP method is applied. Wu et al. (2009) Fuzzy AHP (FAHP) and the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks. Podvezko (2009) Application of AHP technique to more complicated cases is considered Colombo et al. Proved that (2009) judicious use of AHP by experts can, in this instance, be used to represent citizens' views. Maskeliunaite et al. Problem of quality (2009) of passenger carriage Podvezko et al. Contracts' ranking (2010) Stemberger et al. Applied in business (2009) processes management. Sivilevicius and Problem of improving Maskeliunaite (2010) the quality for passenger transportation Bojovic et al. Determination of an (2010) optimal rail freight car fleet composition Steuten et al. AHP weights are used (2010) to fill missing gaps in Markov decision models. Hadi-Vencheh and An improved voting Niazi--Motlagh AHP-data envelopment (2011) analysis methodology for suppliers selection Yan et al. (2011) Presented new developments and maintenances of the existing infrastructures under limited government budget and time UTA Gomes and Rangel An application of (2009) the UTA method and its variant UTA-CR to determine utility functions for the multiple criteria evaluation of residential real estate. COPRAS Ginevicius and Evaluation of banks Podvezko (2008) from the Perspective of their reliability for clients Datta et al. (2009) Determining compromise towards the selection of supervisor Bindu Madhuri et al. Selection of (2010) alternatives based on COPRAS-G and AHP methods Uzsilaityte and Comparison of Martinaitis (2010) different alternatives for the renovation of buildings, taking into account energy, economic and environmental criteria while evaluating impact of renovation measures during their life cycle Chatterjee et al. Material selection (2011) based on COPRAS and EVAMIX methods Karbassi et al. Effectiveness (2011) problem of energy using in buildings Podvezko (2011) The Comparative Analysis of MCDA Methods SAW and COPRAS TOPSIS Jakimavicius and Developed approach Bu-rinskiene (2007) of automobile transport system analysis Arslan and Aydin Two real military (2009) problems are solved by an ideal point algorithm and an outranking method. Fuzzy sets are applied. Jakimavicius and Computed ranks for Burinskiene (2009) transport zones of city according to accessibility and city statistics Liaudanskiene et al. Selection of the (2009) most effective alternative in construction Wu et al. (2009) Fuzzy AHP (FAHP) and the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks. Liu (2009) Explored the multi-attribute decision making problem based on the interval vague value Cokorilo et al. Determining the (2010) optional solution from the existing fleet Rudzianskaite The problem of Kvaraciejiene et al. selecting the most (2010) effective road investment projects. Ginevicus et al. Formation of the (2010) integrated competitive strategy of an enterprise under the conditions of oligopoly market. SAW, VIKOR and TOPSIS are used. Jin and Liu (2010) The extended TOPSIS method is proposed to solve multi-attribute group decision making problems when the attribute values take the form of interval grey linguistic variables and attribute weight is unknown. Liu and Liu (2010) A relative approach degree method of grey relation projection is presented to deal with multiple attribute making, in which the attribute weight is unknown and attribute value is hybrid index. Han and Liu (2011) Modified fuzzy TOPSIS is applied ARAS Bakshi and Sarkar Performance (2011) evaluation of project Balezentis and Integrated Balezentis (2011) assessment of economic sectors SAW Jakimavicius and Developed mechanism Burinskiene (2007) of automobile transport system analysis Zvirblis and Integrated Zinkeviciute (2008) evaluation of the macro environment of freight transportation companies was conducted Jakimavicius and Computed ranks for Burinskiene (2009) transport zones of city according to accessibility and city statistics Shevchenko et al. Comparative analysis (2008) (CLARA and SAW methods) of variants of investment classified risks Wu et al. (2009) Fuzzy AHP (FAHP) and the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks. Zvirblis and Buracas Research and (2010) evaluation of State financial markets Ginevicius et al. Forming the (2010) integrated competitive strategy of an enterprise under the conditions of oligopoly market. SAW, VIKOR and TOPSIS are used. Podvezko (2011) The Comparative Analysis of MCDA Methods SAW and COPRAS ELECTRE Thiel (2008) Peculiarities of method applying Ulubeyli and Kazaz Selection problem (2009) Radziszewska-Zielina Partner selection (2010) problem Wachowicz (2010) ELECTRE-TRI method applied. Two authors introduced their own procedures that can be applied in the pre-negotiation phase for eliciting negotiators' preferences and building the offer scoring systems for the parties. Bojkovic et al. Transport as an (2010) economic activity having complex interactions with the environment was investigated. Kaya and Kahraman AHP and ELECTRE (2011) methods applied to assessment of E-banking Sector PROMETHEE Nowak (2005) Investment evaluation Mitkova and The results from two Mlynarovic (2007) methodological approaches to the analysis of performance and risk of private pension funds in the Slovak Republic are presented: (1) multiple criteria decision model, and PROMETHEE methodology, (2) modern portfolio theory to analyze pension funds in a risk-return space. Palma et al. (2007) Multi-criteria analysis was used to evaluate the integrated performance of silvoarable agro forestry on hypothetical farms in nineteen landscape test sites in Spain. Ghazinoory et al. Different areas of (2009) nanotechnology for Iranian economy considering other countries' strategies and the results of PROMETHEE method are prioritized. Tomic-Plazibat et Assessed al. (2010) country-risk of sixteen Central, Baltic and South-East European transition countries, for 2005 and 2007, using multivariate cluster analysis. Podvezko and Reveals influence of Podviezko (2010) the choice of preference functions and their parameters on the outcome of evaluation Juan (2010) Porter's diamond model of competitive advantage is applied to establish evaluating criteria on urban competitiveness quality, and a fuzzy set theory combining the PROMETHEE method is used to determine the priority of projects. MOORA Brauers and Robustness in Ginevicius (2009) regional development Brauers et al. Assessment of (2010) regional and international development Brauers and Example of project Zavadskas (2010) management under multiple objectives and MULTIMOORA is presented. Ivanov and Stanujkic Software selection (2010) Brauers and The economy of the Ginevicius (2010) Belgian regions is tested with MULTIMOORA Garcia Alcaraz et Evaluation of al. (2010) feasible alternatives and selection problem Chakraborty (2011) Applications of the method in manufacturing environment Brauers et al. MULTIMOORA with (2011) fuzzy number theory applied to EU member states assessment VIKOR Ginevicius and Evaluated financial Podvezko (2006) state of enterprises from various perspectives Antucheviciene and Modelling Zavadskas (2008) multidimensional redevelopment of derelict buildings. Fuzzy VIKOR is applied. Wu et al. (2009) Fuzzy AHP (FAHP) and the three MCDM analytical tools of SAW, TOPSIS, and VIKOR were respectively adopted to rank the banking performance and improve the gaps with three banks. Game theory Ginevicius et al. Forming the (2010) integrated competitive strategy of an enterprise under the conditions of oligopoly market. SAW, VIKOR and TOPSIS are used. Ginevicius and Duopoly market Krivka (2008) analysis Zavadskas and Peculiarities of Turskis (2008) problem solution Stein (2010) Determined agents' strategies based on intended but bounded rationality Stein and Ginevicius Presented round (2010) based games in which the present values change and influence the cooperative relationships Kaplinski and Game theory Tamosaitiene applications for management problems solution (2010)
|Printer friendly Cite/link Email Feedback|
|Author:||Zavadskas, Edmundas Kazimieras; Turskis, Zenonas|
|Publication:||Technological and Economic Development of Economy|
|Date:||Jun 1, 2011|
|Previous Article:||Household money demand in Romania. Evidence from cointegrated var/Pinigu poreikio rumunijos namu ukiuose tyrimas naudojant kointegruotus...|
|Next Article:||The method for improving stability of construction project schedules through buffer allocation/Statybos vykdymo grafiko stabilumo uztikrinimas...|