A FUZZY MULTI-CRITERIA DECISION-MAKING MODEL FOR PROJECT SELECTION

Abstract

In the contemporary world any activity is involuntarily and often decisively influenced by two phenomena: globalization and sustainable development. Each of them gives a special complexity and uncertainty to the business environment. While globalization requires an organization to adapt to new markets and new cultures, sustainable development outlines the need to respect and support the sustainability of these cultures, by attaching particular importance to socio-economic and ecological issues. Our paper presents the analysis of the main theoretical and empirical approaches outlined in relevant literature and by practitioners, highlights the methods used in project evaluation and selection, and proposes a hybrid model based on interdependent elements specific to project management, mathematical optimization and sustainability. The approach presented in the paper is based on a Fuzzy Multiple Criteria Group Decision Making model that allows the inclusion of complexity and uncertainty in the evaluation and selection of projects, in order to obtain a high level of performance, during the contemporary economic and social conditions. The model proposed for the optimal project portfolio selection involves a methodology composed of two decision-making methods: fuzzy AHP for determining the relative importance of the criteria considered within the evaluation model andfuzzy TOPSIS for objective, structured and analytical prioritization of the portfolio projects.

Headnote

Abstract

In the contemporary world any activity is involuntarily and often decisively influenced by two phenomena: globalization and sustainable development. Each of them gives a special complexity and uncertainty to the business environment. While globalization requires an organization to adapt to new markets and new cultures, sustainable development outlines the need to respect and support the sustainability of these cultures, by attaching particular importance to socio-economic and ecological issues.

Our paper presents the analysis of the main theoretical and empirical approaches outlined in relevant literature and by practitioners, highlights the methods used in project evaluation and selection, and proposes a hybrid model based on interdependent elements specific to project management, mathematical optimization and sustainability. The approach presented in the paper is based on a Fuzzy Multiple Criteria Group Decision Making model that allows the inclusion of complexity and uncertainty in the evaluation and selection of projects, in order to obtain a high level of performance, during the contemporary economic and social conditions. The model proposed for the optimal project portfolio selection involves a methodology composed of two decision-making methods: fuzzy AHP for determining the relative importance of the criteria considered within the evaluation model andfuzzy TOPSIS for objective, structured and analytical prioritization of the portfolio projects.

Keywords: decision-making, project management, selection methods, multicriteria, fuzzy sets.

1. Introduction

With a recent history, of only a few decades, project management has quickly become one of the most used concepts in the development of more and more activities. Their proper implementation within contemporary organizations supports their successful development. The complexity and the high degree of uncertainty that characterizes today the activity of any company, can represent sources of important opportunities, associating the business with project orientation. The attempt to achieve the objectives without adequate planning, organization and monitoring, is certainly materialized in a result that emphasizes failure, unsustainable consumption of resources and diminished chances for a successful future. The simple application of routine project management techniques determines the change of the entire vision and culture of an organization, of the staff involved, the organization, the quality, efficiency and effectiveness of the activity carried out.

Impressive or small projects, complex or lowcomplexity projects, educational, business or social projects, all involve investment. Investments have their source in the achievements (or failures) of the past, they are built in the present, but they always aim at the future (Românu 2005). Financial resources, personnel, material resources, time, are invested to fulfill the purpose of the project and obtain the desired results. Projects represent a unique set of interrelated objectives, activities and resources in order to achieve a predetermined goal. Project Management Institute proposes the following definition of the project: "A temporary effort made to create a unique product or service" (PMI 2008).

The rapid change of the global environment, technological development and competitive intensity are elements that influence project management in order to constantly redefine how budgets are set or how resources are allocated, response time, quality and characteristics of designed products.

In this sense, one of the great importance decision categories for the performance of an organization involves identifying the optimal portfolio of implemented projects, through the evaluation and selection of the variants that are expected to meet the objectives set in the most efficient way known and analyzed.

The selection of the optimal portfolio implies the evaluation of the projects and the choice of the variants implementation that contribute in the most adequate way to the fulfillment of the organizational objectives (Mantel et al. 2011). Before financing a project, the level of performance that can be supported by the implementation of that variant must be evaluated. Whether it is proposed by a person within the organization, or it is the wish of a client, or it requires more or less formal approvals, the committee appointed to select the optimal option for a project must ensure that it meets certain conditions.

Thus, special attention must be paid to the issue of subjectivity and decision-making freedom (Damodaran et al. 2005). People think differently, they have different education, culture and principles. That is why the evaluation process must lead to a decision that is as objective as possible. In this regard, in order to support the work of management, in order to optimize the investment decision in a project or a portfolio of projects, I consider it to be necessary the use of appropriate mathematical tools to manage the uncertainty and subjectivity in evaluation.

2. Project selection methods

Planning and developing activities, designing the structure and culture of an organization, providing early responses to environmental events, choosing a technology for products and services, defining a strategy to maximize the value of knowledge and skills of staff to achieve performance, are elements which involve making decisions. Any activity carried out in the organization involves decisions, even if they differ in complexity and importance. The organization can be considered a real decision-making machine. At all levels and in any department, people make decisions constantly, and their degree of optimality significantly determines the level of value created by the organization.

Through the decision-making process, problems are answered, searching and selecting a solution and the mode of action that creates value for the organization and implicitly for its stakeholders. Whether it's choosing the best resources, finding the best way to interact with customers, or adopting the best position in front of a competitor, management must decide.

Strategy, objectives, and why not, the entire contemporary organizational activity, are elements increasingly fulfilled through project management. The multitude and diversity of projects carried out in a society have even determined the development of a new type of organization, the project-oriented organization.

Given the large number of projects and implementation options available, taking into account the simultaneity of development and the interdependencies between them within the organization, a high-performance evaluation is required in order to determine the optimal portfolio.

Project evaluation and selection is a decisionmaking issue of strategic importance, which forms the basis for studies conducted by many researchers for over 50 years.

The key challenges of project portfolio management include a sensitivity analysis of its interdependencies, traceability, simplicity, supporting quantitative and qualitative data, project quantity, trade-offs, group decision making and the mutual links between portfolio levels (Danesh et al. 2018).

Characterized by multiple criteria, contradictory and often difficult to measure (Liesio et al. 2007), the process requires to pay special attention from the decision maker in order to establishing the most effective alternative in terms of the various aspects considered (Mavrotas et al. 2008). The permanent interest for this subject is also reflected in the variety of methods existing in literature (Heidenberger and Stummer 1999). Hundreds of models and methodologies for project evaluation and selection are grouped into: scoring, classification, mathematical programming, fuzzy logic and AHP (Badri et al. 2001); non-numerical (the sacred cow, operational / competitive need, potential benefits) and numerical (financial evaluation, financial options, opportunity cost, scoring) (Meredith and Mantel, 2010); scoring, ad-hoc, comparative, economic, portfolio, mathematical optimization and simulation (Tavana et al. 2013). One of the most comprehensive classifications is the one made by Iamratanakul (2008), which summarizes the entire relevant literature of project selection methods in: ad-hoc, options, cognitive analysis, mathematical programming, benefit measurement.

2.1. Ad-hoc methods

Ad-hoc methods are the simplest and involve a minimum of effort in decision making.

"The Sacred Cow". Sometimes shareholders or business owners suggest a potential product or service that the organization could offer to customers. In most cases, practice demonstrates that regardless of the selection process and results, the proposed project is approved, becoming a "holy cow", whose technical, economic or any other feasibility must be demonstrated. Even if it seems irrational, such practices are often encountered in practice. Total ignorance of employees, their knowledge and experience in favor of supporting their own idea is often the source of failure in business. This type of "evaluation" of an investment idea disregards the need and special value given by the management and employees support in the successful development of the project (Green 1995). The method highlights that type of "not so" in the evaluation. It is desirable to try the correct approach to the business idea and the proper evaluation of the projects carried out, in order to eliminate waste and adopt a sustainable management.

Operational/competitive necessity. Most times, within organizations, projects are selected due to the need to support the continuity of the business processes or to keep the business within the limits of competitiveness. This category of "mandated" projects must be selected for further use of facilities. In an environment characterized by uncertainty and change, both opportunities and threats are becoming more common. The argument regarding the need for operation, involves the selection of projects in order to avoid or counter threats, which emphasizes the fact that it is not the management that dictated the implementation but the external environment, the competitiveness imprinted by it. However, a project that supports the performance of a contemporary organization aims, mainly, to capitalize on opportunities or create them by implementing projects that involve new technologies, innovation and sustainability.

Compared to the first selection model, this one, although it also involves the implementation of an imposed idea (this time by maintaining the level of competitiveness or avoiding the risk of business failure and not by the owner's desire), can still be subject to an objective evaluation, regarding the selection of the most efficient realization variation model available.

2.2. Financial options

Opportunity cost. A more recent approach to project selection, includes within the evaluation process a type of financial analysis, which recognizes the value of the organization's positioning in order to capitalize on future opportunities. The financial options specific principles are used as working grounds, in order to capitalize on future investment opportunities. The concept of financial option (Xie 2009) expresses the acquisition by an organization or person of the right to act in a certain direction, without requiring the mandatory exercise of that right. For example, when purchasing a stock option, the holder is given the right to buy a number of shares at a certain price within a specified period of time. Thus, if the market price of those shares becomes higher than the specific price of the option within the specified time frame, the entity holding the option may exercise its right, thus gaining from the price difference. However, at an inverse relationship between the market price and the option price, the holder may choose not to exercise his right to buy the share. Thus, if the market price of those shares becomes higher than the specific price of the option within the specified time frame, the entity holding the option may exercise its right, thus gaining from the price difference. However, at an inverse relationship between the market price and the option price, the holder may choose not to exercise his right to buy the share.

In the field of projects, options represent the response modalities planification to various likely future states that may be transmitted by the environment. If the event occurs, then one of the options provided will be used, if not, they will be waived.

In an environment of uncertainty and rapid change, identifying the right options is of high importance. If none of the options appear, the cost of determining them is useless. The time, money and resources spent in order to substantiate the options can only be quantified in terms of waste. We believe that options are one of the main methods of responding to potential risks, but their success depends to a large extent on quantifying the elements that can determine the triggering events for implementing the option. The main weakness of this method derives from considerations related to uncertainty. However, uncertainty is taken into account in methods that use fuzzy variables. In order to fulfill the purpose of optimizing the investment decision and implicitly the process of evaluation and selection of projects, forecasts and plans can be made in order to determine the evolution of influencing factors and to establish their ranges of variation and response strategies.

In addition to taking into account the value of future opportunities and threats, in relevant literature (ACCA 2011) the analysis of the cost of giving up the project is also presented, method based on elements characteristic to the concept called "opportunity cost". For example, if a decision has to be made in order to choose an investment with two competing projects at its disposal (choosing one of them involves giving up the other) and the rate of return for the first project is 15%, the choice to invest in -the second will have an opportunity cost of 15%, represented by the lost opportunity cost. If the profitability of the number two project is higher than 15%, it will be selected against the choice of the first project. The decision is based on a basic economic principle, which aims to minimize costs. Choosing the right time to make an investment can also be supported by similar principles. Given a project to implement a new technology in an organization, the value of the investment made differs considerably over time. Mantel et al. (2011) argue that the passage of time reduces the level of uncertainty involved in a project, whether we refer to technological or commercial elements. "The value of making the investment now may be higher than the value of making it later." From a mathematical and economic point of view, an investment made today is more expensive than if we make it in a future period. That is why some specialists recommend placing large sums in the last years of implementation for the investment staggering. But delaying the adoption of new technologies can cost more than an increase in inflation or the discount rate. However, there are situations when the simple maximization or minimization of the values of some indicators does not reflect the real performance of the project. Projects that predict negative cash flows are sometimes approved, these decisions not necessarily reflecting mistakes, but the existence of elements considered by evaluators much more important than profit positivity: acquiring knowledge about a new technology, implementing innovative elements that can support the organization's competitiveness, obtaining the necessary parts to continue the business, the possibility to bring in the future new contracts or profitable investments, improving the competitiveness of the organization, expanding a range of products or a business field.

2.3. Measuring benefits

Benefit-based methods involve an additional dose of effort on the part of decision-makers to establish the selection criteria and evaluate the portfolio accordingly. In cost-benefit analyzes, the classification of projects involves comparing the expected effects with the efforts required to achieve them.

Potential benefits. Another situation an organization may face, in terms of establishing the optimal portfolio, is the situation characterized by the existence of a whole list of potential projects, each of which involves different ideas, objectives, benefits and costs. Under such conditions, the evaluation and selection process is much more complex and difficult than in the case of the models presented above. For selection, commissions are often set up, with each of its members responsible for ordering a number of potential projects and providing the resulting ranking in support of the selection decision. The element that implies a substantial dose of uncertainty is the freedom to choose the criteria used, each evaluator judging based to his own beliefs; thus, while some of them focus on technical specifications, others consider the relationship with the organization's strategy or the degree of compliance with environmental standards to be particularly important.

In order to be able to quantify the results, the proposal of certain criteria by each member of the commission must be followed by the establishment of a common list, based on which to continue the individual evaluation and the desired classification. According to the Q-sort method (Helin and Souder 1974), first of all the projects must be divided into three categories (good, medium and poor) according to the established criteria. If there are more than 7-8 projects in a category, the group must be divided into two subgroups, for example good plus and good minus. Subdivision continues until no class consists of more than 7-8 elements. The next stage is the one in which each subcategory must be presented in the form of a ranking, and to complete the individual evaluation, the subcategories are ordered according to the rank already established. The global ranking is established by calculating the composite rank of each project, depending on the serial number assigned by each evaluator.

2.4. Mathematical programming

Mathematical programming optimizes the fulfillment of an objective function of maximizing or minimizing some effects, under the conditions imposed by the identified constraints (Romero and Rehman 1989). The options/scenarios and the simulation allow to estimate the relationship between efforts and effects and the analysis of the evolution of some categories of benefits when the influencing factors change; presumed change, expected by the decision maker or randomly generated. Among the linear programming methods found in the selection of projects we can identify the linear programming, purpose programming, dynamic programming, stochastic programming, but also the use of fuzzy sets.

3. The proposed FMCGDM model

Organizational performance is conditioned by permanent investments in consecutive and simultaneous projects, which are often also competing (Ghasemzadeh et al. 1999). Thus, managers must solve a complex decision-making situation, in order to allocate limited resources to the multitude of projects with a superior contribution to meeting the organization's objectives (Cheng and Li 2005, Medaglia et al. 2007). Making wrong decisions when selecting projects involves two negative consequences: on the one hand resources are consumed for the implementation of inappropriate projects, and on the other hand the organization loses the potential benefits of allocating resources to appropriate projects (Martino 1995). Thus, the optimization of the investment decision and choosing the correct project portfolio guarantees efficiency considering the level of the developing organization, effectiveness and therefore performance, profitability and capitalization of the efforts of the stakeholders involved.

The analysis of the specialty literature shows that most evaluations are based on cost-benefit analysis, mathematical programming or simulation that is limited exclusively to economic and financial indicators, monetary, thus neglecting the difficult to capitalize effects in monetary terms. Moreover, many of them are strictly based on the opinion of the evaluator, not allowing the participation of other stakeholders or groups of specialists (Sieber and Braunschweig 2005). Many topics in the practice of project portfolio management have been studied in qualitative settings, with selected case companies and portfolios as the source of data. However, also questionnaire-based hypothetic-deductive studies have been carried out (Martinsuo 2013). Project portfolio management is a process for and between people, and for and between organizations, besides its service to strategy and products within one organization. Despite the quite obvious linkages between, e.g., project selection and managers' interaction, or project portfolios and project offices, the behavioral and organizational viewpoints have received far too little attention and may well explain some of the problems in achieving PPM success (e.g. Elonen and Artto 2003, Engwall and Jerbrant 2003, Zika-Viktorsson et al. 2006). If previous frameworks have portrayed project portfolio management as a systemic solution to goals and environments that are assumed as static, future research could explore the behavioral and organizational viewpoints that embrace the dynamic and complex nature of practice and context (Geraldi 2008).

Organizing a personal event, writing a book, reorganizing a company or building a bridge over the Danube, are significantly different projects, which still have common characteristics in terms of uniqueness, specificity and necessity. Another element common to many projects is multidisciplinarity (Mantel et al. 2011), reflected in the need to involve the knowledge and experience of people with different specializations. Multidisciplinarity implies a high degree of complexity of the projects, through the existing correlations between the component elements, but especially through the success dependence on the performance of the relationship between the know-how possessors necessary for obtaining the expected results. These explanations emphasize once again the importance of the staff, more precisely of the ways to capitalizing their knowledge in the relationships developed between the team members and between them and the other categories of the social environment of a project. For this reason, we consider it particularly important to take into account, for a successful evaluation, the elements related to the complexity of teamwork, team management, conflicts and resistance to change.

In terms of this paper, the decision regarding the selection of projects must not include strictly financial elements, but also indicators based on the interdependence of dimensions and principles of sustainable development. Project multicriteria management thus involves the use of advanced scoring methods (DePiante and Jensen 1999, Coldrick et al. 2005), multi-attribute (Duarte and Reis 2006), which quantifies the opinion of several decision makers (Carazo et al. 2010, Khalili -Damghani and SadiNezhad 2013).

Scoring based methods. In order to overcome the main disadvantage of financial evaluation methods (one-dimensional focus) and to improve the methodology used by adding more criteria in the analysis, scoring-based methods have been developed. The simplest form of evaluation based on grading, the unweighted factorial method 0-1, uses in the evaluation a list of criteria of significant interest to management, criteria that reflect the purpose and objectives of the organization. The analyzed projects receive from the selection committee 1 if the criterion is met or 0 if it cannot be met by the evaluated project variant. The selection depends on maximizing the number of points accumulated.

Although it seems easy to adopt, due to its characteristic simplicity, the method has many disadvantages: it does not take into account the importance of each criterion and the level of compatibility of a project with the intended objective. However, these deficiencies are corrected by the weighted average mean, based on a number n of evaluation criteria and on estimates of the weight of the relative importance of each of them wj, the sum of the weights of the j criteria being usually 1. Some specialists recommend limiting the computations only the criteria that reflect important factors and not including those marginal criteria of the investment decision (considered to have an importance of 2-3%), resulting thus in n <8 factors with weights greater than 10-15%, which involves reducing the significance of low-weight criteria.

The level of importance and the weighted mean of each factor reflect the difficulty of implementing weighted scoring in project evaluation. For their selection, it is possible to use the direct transformation into criteria of some organizational objectives and indicators or to determine the importance of specific factors of the projects evaluated through individual methods (which involve substantiating the necessary elements based on a particular subjective opinion), and/or methods group. One of the most sophisticated versions of scoring methods is the AHP (Analytic Hierarchy Process) method.

Within the pages of this paper, the notion of group and the multidisciplinarity involvement in all phases of a project are considered of particular importance. In a world dominated by specialists, managers can not only rely on their own beliefs in the decision-making process, but must take into account, in order to optimize the decisions taken, the knowledge and experience of the staff they work with, their work "summing up" to create environment conducive to group performance. Mission, strategy, purpose, objectives and activities, all these elements are based on and for employees, but important is the value printed by them to the organization.

The second element included in the calculation of the score of the evaluated projects is the score sij (usually a scale from 1 to 5 is used), which reflects the degree of fulfillment of each criterion j by project i. For each project, it is weighted the score sij with the level of importance wj, resulting by summing its final grade. The projects with the highest score are selected, or, under the conditions of setting a constraint, the projects with the highest score from those that exceed the required factor.

The calculation formula used is:

(ProQuest: ... denotes formula omitted.)

where:

Si = project i total score

sij = the grade received by project i for criterion j

wj = weight, importance of criterion j

A disadvantage identified in the use of this method concerns the subjectivity of notation; for different evaluators, the excellent grade can have various meanings. Criteria such as correlation with organizational objectives, compliance with ethical principles or ease of implementation and acceptance by employees, are subject to a high degree of subjectivity in the evaluation. It is therefore desirable to include quantifiable elements among the criteria, but also the concrete way of quantification.

However, we consider that modeling and mathematization are the most suitable elements to combat subjectivity, and therefore the proposed model uses fuzzy variables both for modeling multicriteria and for modeling the process of optimal projects evaluation and selection.

Projects' selection process includes complex decision variables. Thus, there is a need to solve selection problems by an integrated approach, considering that a single technique is not adequate to address this issue (Vinodh, Prasanna, and Hari Prakash 2014). Therefore, based on the literature review, fuzzy AHP and fuzzy TOPSIS have been used by several researchers for various applications. However, the use of fuzzy AHP-TOPSIS portfolio management of projects is found to be scant (Mohammed 2021).

Optimizing the project portfolio selection decision in the current context involves correlating the society complexity and uncertainty, the environment, the organization, the project, decision makers and specialists. It implies thus a multicriteria, multidimensional approach, and the involvement of groups of specialists to validate the model and give special importance to the projects business environment.

Under these conditions, the approach proposed in this paper for the evaluation and selection of projects is based on a group decision model, multicriteria, with fuzzy variables (eng. FMCGDM - Fuzzy Multiple Criteria Group Decision Making), whose development required modeling the decision multicriteria, within the current context, by applying the fuzzy AHP method and modeling the evaluation process and optimal projects selection considering the uncertainty of the environment, through the TOPSIS method with vague variables. The detailed presentation of each applied method shall be included in future works.

4. Conclusions

The permanent change of the economic environment and the complexity of the business world are reflected, but it is also the effect of the emergence of managerial ideas and values (Clarke and Clegg 2000). The emergence of new paradigms in management and their evolution is increasing in speed; by paradigm understanding a model of thinking, a "constellation of concepts, values, perceptions and practices that form the vision of the reality of a community and dictates the way it is organized" (Kuhn 1970).

In terms of project management, their selection has evolved from static evaluation methods that use cost analysis or linear programming with real or integer numbers, to much more flexible methods such as fuzzy multi-attribute. The classical, quantitative decision criteria based on discounted cash flows and purely financial indicators such as net present value (NPV) or internal rate of return (Brigham 1975, Boer 1999, Copeland et al., 2000) are no longer sufficient to correctly reflect the image imposed by the current economic environment. Qualitative and difficult-tomeasure indicators, which quantify the impact of the project on the environment and society, give the investment decision an additional degree of difficulty. The recognition of the need to use models composed of subjective, qualitative indicators is highlighted by the methods adopted by many authors in their work. By introducing the theory of multi-attribute utility (Moselhi and Deb 1993, Dozzi et al. 1996, Fayek 1998), the Gray method (Chua and Li 2000), TOPSIS (Chua et al. 2000), ELECTREII (Wong et al. 2000) they include in the evaluation and selection of projects criteria such as profit, cost, social benefits, risk, but also other factors considered relevant by the decision maker.

The analytical hierarchy process (AHP) suggested by Thomas Saaty and used for the first time in selecting the Cascio projects (1995), is also a mathematical method of substantiation of the decision, which covers both quantitative and qualitative aspects from the decision-making process. The method reduces complex decisions to a series of pairwise comparisons and then synthesizes the results, giving the author the opportunity to measure the influence of a multi-criteria system of indicators. In order to properly manage the interdependencies between the indicators included in the decision model, some authors (Cheng and Li 2005) work with a variant of the method that consists of a process of analytical network process (ANP).

However, the use of multi-attribute analysis (Molenaar and Songer 1998), although they report the project evaluation to a large number of indicators, does not capture the appearance of multiple constraints (limited resources, strategic, political constraints) (Mavrotas et al. 2006). Therefore, in order to establish the project portfolio, the list of selection methods shall be filled up with the methods specific to multi-objective programming. To this end, some authors apply the goal programming method (Lee and Kim 2001, Wainwright et al. 2003), undertaking the responsibility to establish in collaboration with decision makers the levels of aspiration for the objectives considered, based on their preferences, experience accumulated, or linear programming. The construction of a useful synthesis function that allows all objectives integration, weighted according to the importance given to them by the decision maker, is the choice of other authors (Ghasemzadeh et al. 1999, Medaglia et al. 2008). And other authors choose to minimize the distance to the ideal point (Klapka and Pinos 2002), or to implement metaheuristic algorithms such as Scatter Search Procedure for Multiobjective Optimization (SSPMO) (Carazo et al. 2010) to solve the problem of multiple objectives involved in determining the most efficient project portfolios.

In order to have a complete picture of the investment project selection methodology, we must also consider the proposals for managing an element present in all activities of the contemporary world, difficult to quantify, but extremely important: uncertainty. Shakhsi-Niaei et al. (2011) considers the constraints of the environment and the uncertainty of the inputs of the selection problems through a model composed of three methods (PROMETHEE - Preference Ranking Organization Method for Enrichment Evaluations, Monte Carlo simulation and mathematical programming).

In another approach, Machacha and Bhattacharya (2000) emphasizes that most classical methods of project selection ignore the behavior of the people involved and the differences in managers culture and experience, and support the use of fuzzy logic in managing uncertain data. Expressions by fuzzy units are not as rigid and complicated as mathematical algorithms and allow the selection of projects in an uncertain environment reducing the risk of projects. Moon and Kang (1999), Hsueh and Yan (2011), Khalili-Damghani and Sadi-Nezhad (2013) apply multicriteria fuzzy evaluation models to choose the most suitable projects. Studies such as those of Liu (2008), Guo and Tanaka (2001), Leon et al. (2003), Lertworasirikul et al. (2003), Kao and Liu (2000) introduce fuzzy logic into the relatively new DEA methodology, in order to quantify the inaccuracy of its specific inputs and outputs.

Concluding on the existing methodology mentioned in the specialty literature and in practice, specific to the evaluation and selection of projects/project portfolios, we can emphasize the outline of a mature theoretical and methodological tools, based on numerous researches and proposals.

Multicriteria decision models, such as AHP or TOPSIS, are among the most appreciated and used in various fields of activity, but for their successful implementation there will always be questions in terms of establishing the criteria and their corresponding weights within the created interdependencies system of (Saghaei and Didehkhani 2011).

Thus, the support model of the decision to select the optimal portfolio of projects proposed by this paper is based on a methodology composed of two methods: fuzzy AHP to determine the relative importance of the criteria considered in the evaluation model and fuzzy TOPSIS for objective, structured and analytical prioritization of the portfolio projects.

Nonetheless, it remains a permanent challenge to identify the optimal set of techniques and methods for project evaluation and selection, and it is amplified by the need for permanent adaptation to the context imposed by the current business environment.

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