Fuzzy Preference Programming Research Articles

Interval pairwise comparisons in the presence of infeasibilities: Numerical experiments

Pairwise comparisons constitute a fundamental part of many multiple-criteria decision-making methods designed to solve complex real-world problems. One of the pervasive features associated with the complexity of any problem is uncertainty. Experts are rarely able to consistently and accurately evaluate a set of alternatives under consideration due to time pressure, cognitive bias, the intricacy or intangible essence of the problem, a lack of requisite knowledge or experience, or other reasons. Interval pairwise comparisons (IPCs) allow for this uncertainty in a natural way; however, the problem of inconsistency (or infeasibility) may arise. That is, a set of interval comparisons may not allow experts to find a solution in the form of a priority vector. The aim of this paper is to provide a comparison of existing priority deriving methods for inconsistent (infeasible) IPCs via numerical examples and simulations. Our results indicate that the Interval Stretching Method is the best with respect to preserving original preferences. In addition, the question of the uniqueness of the solution is investigated for selected methods, with the Fuzzy Preference Programming method being the best in providing a unique solution. Since the majority of examined methods provide mostly non-unique solutions, modifying these methods in order to provide unique solutions might be a research direction worth considering in the future.

Inverse Trigonometric Fuzzy Preference Programming to Generate Weights with Optimal Solutions Implemented on Evaluation Criteria in E-Learning

Nielsen’s heuristics are widely recognized for usability evaluation, but they are often considered insufficiently specific for assessing particular domains, such as e-learning. Currently, e-learning plays a pivotal role in higher education because of the shift in the educational paradigm from a teacher-centered approach to a student-centered approach. The criteria utilized in multiple sets of heuristics for evaluating e-learning are carefully examined based on the definitions of each criterion. If there are similarities in meaning among these criteria, they are consolidated into a single criterion, resulting in the creation of 20 new criteria (spanning three primary aspects) for the evaluation of e-learning. These 20 new criteria encompass key aspects related to the user interface, learning development, and motivation. Each aspect is assigned a weight to facilitate prioritization when implementing improvements to evaluate e-learning, which is especially beneficial for institutions with limited resources responsible for the relevant units. In terms of weighting, there is room for enhancement to attain more optimal weighting outcomes by employing a Fuzzy Preference Programming method known as Inverse Trigonometric Fuzzy Preference Programming (ITFPP). The higher the assigned weight, the greater the priority for implementing improvements.

Numerical fact-finding of different functions impact on the fuzzy preference programming optimality

The fuzzy analytic hierarchy process (FAHP) is a widely implemented approach for determining the weights of criteria and alternatives from pairwise comparison matrices. FAHP methodology uses triangular fuzzy as a tool for the decision-maker to arrange judgments expressed as the pairwise comparison matrix elements. FAHP is then modeled into a nonlinear constrained optimization problem to increase the decision maker’s satisfaction. However, in some cases, this model would yield multiple optimal solutions. Logarithmic fuzzy preference programming (LFPP) is offered to overcome this unfavorable situation. Nevertheless, LFPP includes a parameter in its model for which no specific method is available to determine. Meanwhile, the differences in parameter values will affect the solution. In the worst case, the weights obtained will be inaccurate. As a result, the decision taken based on the weights will be biased and unreliable. This issue is particularly important when it requires high accuracy. It includes the decisions on the portfolio investment, economic, human capital measurements, and shipping registry. Another crucial thing is that the use of the natural logarithm function in the LFPP method has the potential for an overflow effect at the computational stage when it is implemented to derive the weights. Therefore, in the application context, it is significant to improve the shortcomings of the LFPP method in terms of parameter involvement and the use of the logarithmic function. For this purpose, this paper proposes a non-parameter transcendental fuzzy preference programming (TFPP) model for deriving the optimal weights from fuzzy pairwise comparison matrices. The TFPP is obtained by investigating different functions’ effects on fuzzy preference programming (FPP). The proposed method has a more general form. One of the specific forms of the TFPP is chosen in a computational implementation. The numerical results confirm that the TFPP optimization model is reliable in obtaining the optimum global weights. The comparison with the LFPP method also shows that the TFPP is more efficient.

A new logarithmic fuzzy full consistency method for prioritizing critical success factors of technology start-ups in Thailand

Due to societal changes and modernization in the world, various technologies and businesses have changed. Technology start-up enterprises are currently increasingly gaining attention in Thailand. However, for a technology start-up to be successful is not an easy path. Critical success factors (CSFs) from experts are necessary at the start. In this research two sectors: general technology (GeneralTech) and deep technology (DeepTech) are investigated. Entrepreneurs who wish to shift their businesses from GeneralTech to DeepTech can also understand what they have to focus on in running a relevant and successful business. Fuzzy Logarithmic Full Consistency Method (FUCOM-LF) has been applied to rank the CSFs for technology start-ups. This newly developed method has been compared with the existing methods; Logarithmic Fuzzy Preference Programming method (LFPP) and Fuzzy Logarithmic Full Consistency Method (FUCOM-F). The results have shown that the FUCOM-LF has advantages over existing methods because it uses a smaller number of pairwise comparisons with full consistency and it can get reliable results based on evaluated data. It is also not necessary to defuzzify the final value like FUCOM-F. The findings reveal that the three main CSFs for GeneralTech start-ups in Thailand are market and customer, key persons, and value propositions, while CSFs for DeepTech start-ups are market and customer, value propositions, and key persons, respectively. To shift from GeneralTech start-ups to DeepTech start-ups entrepreneurs should enhance their value propositions.

A fuzzy preference programming and weighted influence non-linear gauge system for mission architecture assessment at NASA

The selection of a mission architecture is complex due to conflicting and intertwined mission operation criteria. In addition, the uncertainties inherent in human exploration missions make the evaluation process more challenging. This study presents a combination of improved fuzzy preference programming (FPP) and weighted influence non-linear gauge system (WINGS) to efficiently and effectively weigh conflicting and intertwined decision criteria and prioritize mission architecture scenarios at NASA. The improved FPP determines the weights of decision criteria, and the WINGS method considers criteria interdependencies and evaluates the alternative mission architecture scenarios. A numerical example demonstrates the proposed approach’s performance compared to three multi-criteria methods. A case study at the Johnson Space Center shows the applicability and efficacy of the proposed approach.

Group AHP framework based on geometric standard deviation and interval group pairwise comparisons

The complexity of multi-criteria decision problems requires the involvement of a group of experts, who can contribute their knowledge, experience and opinions to the decision-making process. The aim of this study is to present a new group analytic hierarchy process (AHP) framework, called GSD-IJ. The main idea is to emphasize group satisfaction with the final decision, which is measured by the group satisfaction index. GSD-IJ is based on geometric mean and geometric standard deviation and allows aggregation of individual scalar valued judgments into interval group judgments. The width of the group interval judgments is controlled by the parameter λ, which ensures the acceptable degree of uncertainty of interval judgments, while providing the highest possible value of the group satisfaction index. Fuzzy preference programming is used to derive scalar-valued group weights from group interval pairwise comparison matrix. Three examples are given to evaluate and validate GSD-IJ. The results show that GSD-IJ performs well and is suitable for solving group multi-criteria decision problems.

Software Effort Estimation Using Logarithmic Fuzzy Preference Programming and Least Squares Support Vector Machines

Purpose: Effort Estimation is a process by which one can predict the development time and cost to develop a software process or product. Many approaches have been tried to predict this probabilistic process accurately, but no single technique has been consistently successful. There have been many studies on software effort estimation using Fuzzy or Machine Learning. For this reason, this study aims to combine Fuzzy and Machine Learning and get better results.Methods: Various methods and combinations have been carried out in previous research, this research tries to combine Fuzzy and Machine Learning methods, namely Logarithmic Fuzzy Preference Programming (LFPP) and Least Squares Support Vector Machines Machine (LSSVM). LFPP is used to recalculate the cost driver weights and generate Effort Adjustment Point (EAP). The EAP and Lines of Code values are then entered as input for LSSVM. The output results are then measured using the Mean Magnitude of Relative Error (MMRE) and Root-Mean-Square Error (RMSE). In this study, COCOMO and NASA datasets were used.Result: The results obtained are MMRE of 0.015019 and RMSE of 1.703092 on the COCOMO dataset, while on the NASA dataset the results of MMRE are 0.007324 and RMSE are 6.037986. Then 100% of the prediction results meet the 1% range of actual effort on the COCOMO dataset, while on the NASA dataset, the results show that 89,475 meet the 1% range of actual effort and 100% meet the 5% range of actual effort. The results of this study also show a better level of accuracy than using the COCOMO Intermediate method.Novelty: This study uses a combination of LFPP and LSSVM, which is an improvement from previous studies that used a combination of FAHP and LSSVM. The method used is also different where LFPP produces better output than FAHP and all data in the dataset is used for training and testing, whereas in previous research it only used a small part of the data.

Selection of ERP system and the best practice by hybrid method: A case study of Thai automotive supply chain network

Enterprise Resource Planning (ERP) gives an organization a competitive edge by centralizing organization data and collaborating among supply chain networks. There are many types of software packages available, so most of the research proposed methods for prioritizing the best system. However, they did not suggest how to implement it, or which practices should be employed. So, this paper aims to propose a framework and a method for the selection of an ERP system and the best practice for implementation at the same time. A hybrid method of Logarithmic Fuzzy Preference Programming (LFPP) and Decision-Making Trial and Evaluation Laboratory (DEMATEL) is presented for solving the problem. LFPP is reliable in obtaining weights, while DEMATEL can capture interrelationships between practices. It was applied to a case study supply chain network of a Thai automotive parts manufacturer to demonstrate the applicability; it could establish the priorities of criteria, rank alternatives, and select the appropriate practices at the same time. There is no study about software and practice selection by use of this proposed method before. The comparison of LFPP and the Fuzzy Full Consistency Method (FUCOM-F) was also performed. It shows that FUCOM-F uses a smaller number of pairwise comparisons than LFPP, but the obtained weights of LFPP are more consistent with the evaluation matrix than using FUCOM-F.

Green Supplier Selection in a Fuzzy Environment: FIS and FPP Approaches

Sustainable supply chain management (SSCM) is the integration of economic, social, and environmental concerns into the supply chain practices that plays an important role to minimize industrial pollution, global warming, and carbon emission and make a supply chain green. In the present article, a novel fuzzy analytical hierarchy process (AHP) as a fuzzy preference programming (FPP) approach has been combined with fuzzy inference system (FIS) for evaluating the performance of suppliers considering carbon management (CM) indicators. To do so, 12 indicators are classified in four basic perspectives applying the Delphi method. The importance weights of perspectives and their related indicators are calculated performing FPP. Accordingly, the weighted performance values that are inputs of FIS are calculated and finally, the weighted fuzzy system will be applied to obtain the total environmental performance of the suppliers. Moreover, we implemented our model within a home appliances manufacturing company in Iran to examine its efficiency. Results approve that the proposed approach can apply efficiently in real situations and are reliable-enough. Furthermore, findings provide more insight for the managers of the company toward the concept of the CM and make them able to find their weaknesses and work with their partners to fix them.

METHOD FOR WEIGHTS CALCULATION BASED ON INTERVAL MULTIPLICATIVE PAIRWISE COMPARISON MATRIX IN DECISIONMAKING MODELS

Context. The pairwise comparison method is a component of several decision support methodologies such as the analytic hierarchy and network processes (AHP, ANP), PROMETHEE, TOPSIS and other. This method results in the weight vector of elements of decision-making model and is based on inversely symmetrical pairwise comparison matrices. The evaluation of the elements is carried out mainly by experts under conditions of uncertainty. Therefore, modifications of this method have been explored in recent years, which are based on fuzzy and interval pairwise comparison matrices (IPCMs).
 Objective. The purpose of the work is to develop a modified method for calculation of crisp weights based on consistent and inconsistent multiplicative IPCMs of elements of decision-making model.
 Method. The proposed modified method is based on consistent and inconsistent multiplicative IPCMs, fuzzy preference programming and results in more reliable weights for the elements of decision-making model in comparison with other known methods. The differences between the proposed method and the known ones are as follows: coefficients that characterize extended intervals for ratios of weights are introduced; membership functions of fuzzy preference relations are proposed, which depend on values of IPCM elements. The introduction of these coefficients and membership functions made it possible to prove the statement about the required coincidence of the calculated weights based on the “upper” and “lower” models. The introduced coefficients can be further used to find the most inconsistent IPCM elements.
 Results. Experiments were performed with several IPCMs of different consistency level. The weights on the basis of the considered consistent and weakly consistent IPCMs obtained using the proposed and other known methods have determined the same rankings of the compared objects. Therefore, the results using the proposed method on the basis of such IPCMs do not contradict the results obtained for these types of IPCMs using other known methods. Rankings by the proposed method based on the considered highly inconsistent IPCMs are much closer to rankings based on the corresponding initial undisturbed IPCMs in comparison with rankings obtained using the known FPP method. The most inconsistent elements in the considered IPCMs are found.
 Conclusions. The developed method has shown its efficiency, results in more reliable weights and can be used for a wide range of decision support problems, scenario analysis, priority calculation, resource allocation, evaluation of decision alternatives and criteria in various application areas.

Deriving and comparing priority vectors for revised‐leachate pollution index (r‐LPI) using three fuzzy analytic hierarchy process

AbstractThe revised leachate pollution index (r‐LPI) developed using the fuzzy Delphi analytical hierarchy process (FDAHP) is a technique to quantify the contamination potential of landfill leachate. The steps involved in the formulation of the r‐LPI were the selection of parameters using the fuzzy Delphi method (FDM), computing relative weights of the parameters using the fuzzy analytic hierarchy process (FAHP), development of sub‐index curves, and finally, aggregation of sub‐indices. The calculation of the weights of the parameters has been one of the most crucial steps in the formulation of any index. The analytical hierarchy process (AHP) has been one of the most widely used methods for weight calculation. However, it was observed that the traditional AHP is inadequate to deal with the imprecise or ambiguous nature of linguistic evaluation. It also involves human subjectivity, which induces ambiguity and entails the use of decision‐making with uncertainty. The fuzzy AHP method was used in this study to overcome this difficulty. Three significant issues pertinent and essential to fuzzy AHP were addressed: contradictions in expert judgment, deriving priorities from inconsistent fuzzy judgment matrices, and group decision‐making. In this study, three FAHP methods: Chang's extent analysis (EA) method, fuzzy preference programming (FPP), and logarithmic fuzzy preference programming (LFPP) were used to calculate the weights of the r‐LPI parameters. The results of these methods were then compared, and it was determined that LFPP was the most suitable method for calculating the weights of the r‐LPI parameters.

Hybrid Fuzzy Multi-Criteria Analysis for Selecting Discrete Preferable Groundwater Recharge Sites

This study proposes a hybrid fuzzy multi-criteria methodology for the selection of the most preferable site for applying managed aquifer recharge (MAR) systems by utilizing floodwaters. The use of MAR can increase water resources for later water utilization in case of drought. In this multi-criteria problem, seven recharge sites are under consideration, based on nine criteria, aiming to make a final list of their relative ranking. A fuzzy analytic hierarchy process (FAHP) based on the logarithmic fuzzy preference programming (LFFP) method is used to determine the weights of criteria. LFFP is an optimization-based method that produces a priority vector from a fuzzy pairwise comparison matrix. Furthermore, fuzzy inference systems (FIS) based on the Mamdani approach are used to estimate the rating of each alternative with respect to the criterion examined, and then the final evaluation of the alternatives is obtained. A FIS is a fuzzy if–then rule-based system where the experts’ qualitative knowledge is translated into numerical reasoning for each individual criterion. The proposed methodology is applied in the aquifer system of the agricultural plain located to the southeast of the city of Xanthi in the Prefecture of Xanthi, NE Greece.

Fuzzy preference programming formulation in data envelopment analysis for university department evaluation

PurposeUniversity department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation.Design/methodology/approachThis paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs.FindingsDue to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score.Originality/valueThis study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.

Green sourcing in the era of industry 4.0: towards green and digitalized competitive advantages

PurposeIn the last decade, sustainable sourcing decision has gained tremendous attention due to the increasing governmental restrictions and public attentiveness. This decision involves diverse sets of classical and environmental parameters, which are originated from a complex, ambiguous and inconsistent decision-making environment. Arguably, supply chain management is fronting the next industrial revolution, which is named industry 4.0, due to the fast advance of digitalization. Considering the latter's rapid growth, current supplier selection models are, or it will, inefficient to assign the level of priority of each supplier among a set of suppliers, and therefore, more advanced models merging “recipes” of sustainability and industry 4.0 ingenuities are required. Yet, no research work found towards a digitalized, along with sustainability's target, sourcing.Design/methodology/approachA new framework for green and digitalized sourcing is developed. Thereafter, a hybrid decision-making approach is developed that utilizes (1) fuzzy preference programming (FPP) to decide the importance of one supplier attribute over another and (2) multi-objective optimization on the basis of ratio analysis (MOORA) to prioritize suppliers based on fuzzy performance rating. The proposed approach is implemented in consultation with the procurement department of a food processing company willing to develop a greener supply chain in the era of industry 4.0.FindingsThe proposed approach is capable to recognize the most important evaluation criteria, explain the ambiguity of experts' expressions and having better discrimination power to assess suppliers on operational efficiency and environmental and digitalization criteria, and henceforth enhances the quality of the sourcing process. Sensitivity analysis is performed to help managers for model approval. Moreover, this work presents the first attempt towards green and digitalized supplier selection. It paves the way towards further development in the modelling and optimization of sourcing in the era of industry 4.0.Originality/valueCompetitive supply chain management needs efficient purchasing and production activities since they represent its core, and this arises the necessity for a strategic adaptation and alignment with the requirement of industry 4.0. The latter implies alterations in the avenue firms operate and shape their activities and processes. In the context of supplier selection, this would involve the way supplier assessed and selected. This work is originally initiated based on a joint collaboration with a food company. A hybrid decision-making approach is proposed to evaluate and select suppliers considering operational efficiency, environmental criteria and digitalization initiatives towards digitalized and green supplier selection (DG-SS). To this end, supply chain management in the era of sustainability and digitalization are discussed.

Selection of Wind Turbine Based on Fuzzy Analytic Network Process: A Case Study in China

Wind turbine selection is an evaluation problem involving many factors, such as technology, economy, society, etc., and there exist internal dependencies and circular relationships among these factors. This increases the complexity of the selection problem. At the same time, with the development of wind power technology, the types of wind turbines on the market are increasing. Therefore, it is necessary to establish a scientific and comprehensive evaluation system to guide the selection work. This paper extends the traditional indicator system, selecting a total of twelve evaluation indicators from three aspects: operation reliability, economy, and supplier cooperation. The selected indicators are defined in detail to clarify the relationship between them. Then the triangular fuzzy number is introduced to accurately reflect the preference information obtained from experts, and a fuzzy analytical network process (FANP) model for wind turbine unit selection is constructed by combining fuzzy preference programming (FPP) with analytic network process (ANP). In the end, a case study in China is carried out. Results show that the 2.5 W unit produced by Goldwind obtains the best comprehensive evaluation value, which is consistent with the expanding market share permanent magnet direct-drive wind turbines. This paper could provide references for future wind turbine selection questions.

Environmental Evaluation of Coal Mines Based on Generalized Linear Model and Nonlinear Fuzzy Analytic Hierarchy

To create a good working environment for workers in coal mines and ensure that they can work safely and comfortably, environmental assessments of coal mines represent an important method used to achieve this goal. In this paper, a comprehensive environmental evaluation model of coal mines was established based on generalized linear theory and fuzzy analytic hierarchy processes. First, the importance degree of each index factor was obtained by analyzing the statistical source data of air, water, soil, ecological compensation, and other indexes that affect the ecological environmental safety of coal mines using generalized linear theory. Based on the importance degree of each index factor, a pairwise comparison matrix was constructed by the logarithmic fuzzy preference programming method, and the influence weight value of each index factor was accurately obtained through MATLAB software. The results indicated that the model can effectively reduce the impact of expert subjectivity on the evaluation results, which is consistent with the reality of coal mines. In addition, in the process of environmental assessment of coal mines, the model is proven to be convenient, precise, and easy to operate.

Fuzzy Preference Programming Framework for Functional assessment of Subway Networks

The 2019 Canadian Infrastructure report card identified 60% of the subway system to be in a very poor to a poor condition. With multiple assets competing for the limited fund, new methodologies are required to prioritize assets for rehabilitation. The report suggested that adopting an Asset Management Plan would assist municipalities in maintaining and operating infrastructure effectively. ISO 55000 emphasized the importance of risk assessment in assessing the value of an organization’s assets. Subway risk assessment models mainly focus on structural failures with minimum focus on functional failure impacts and network criticality attributes. This research presents two modules to measure the functional failure impacts of a subway network, given financial, social, and operational perspectives, in addition to the station criticality. The model uses the Fuzzy Analytical Network Process with application to Fuzzy Preference Programming to calculate the weights for seven failure impact attributers and seven criticality attributes. Data are collected using questionnaires and unstructured/structured interviews with municipality personnel. The analysis identified social impacts to have the highest score of 38%, followed by operational and financial impacts at 34% and 27.65%, respectively. The subway station criticality revealed station location to have the highest impact at 35%, followed by station nature of use and station characteristics at 30.5% and 31.82%, respectively. When integrated with probability of failure, this model provides a comprehensive risk index to optimize stations for rehabilitation.

A combined multi-criteria approach to assess forest management sustainability: an application to the forests of Eastern Macedonia & Thrace Region in Greece

Sustainable forest management should be pursued in all public forests of the EU countries, as this constitutes a legal requirement within the frame of the FOREST EUROPE policy. However, the forest management sustainability assessment process is a complex task, mainly because it involves integration of multiple environmental, socio-economic and institutional impacts at different spatial scales of different forest management policies, which are considered for implementation at any forest location. Moreover, the conflicts and interests of the various stakeholders related to the forest resources should be included in the assessment process. Much of the on-going research, therefore, focuses on the development of tools that can facilitate the integration of the different type of forest resource impacts and conflicts towards achievement of forest management sustainability. This paper presents a multi-criteria approach, which combined spatial analysis, Geographic Information Systems (GIS), the Analytic Hierarchy Process (AHP), spatially referenced impact indicator models for pairwise comparisons, the fuzzy extent analysis, fuzzy preference programming and the ideal solution concept in order to assess the performance of forest management sustainability at regional level. The combined multi-criteria approach was successfully implemented to the forests of Eastern Macedonia and Thrace Region in northern Greece through the operation of the National Forest Governance Council. Seven forest management policies were subjected to sustainability assessment and an Overall Forest Sustainability Performance Index (OFSPI) was calculated for each one of them. The final ranking of the forest management policies was based on their OFSPI values. Furthermore, sensitivity analysis was conducted to explore robustness of the final solution.

The application of cloud model combined with nonlinear fuzzy analytic hierarchy process for the safety assessment of chemical plant production process

According to statistics, majority of chemical accidents were caused by human factors during the production and storage of chemicals. From the perspective of human factors, a safety assessment method for chemical plant production process based on cloud model combined with nonlinear fuzzy analytic hierarchy process is proposed in this paper. The method comprehensively considers the safety influencing elements of human factors in seven aspects: organization, information, job design, human system interface, task environment, workplace design, and operator characteristics. We use the analytic hierarchy process to establish a hierarchical structure system. Then use the triangular fuzzy numbers to quantify the indicators and the logarithmic fuzzy preference programming method to calculate the index weights. Finally, the cloud model is applied to determine the safety level of the chemical plant production process. The method is simulated and verified simultaneously. The simulation results showed that the method is reliable, practical and scientific as a safety assessment method for chemical production.

A Computational Approach to Uncovering Economic Growth Factors

The knowledge-based economy is the basis of economics in which all businesses and industries benefit from the distribution and application of knowledge in pursuit of their goals to meet their needs. But the prosperity and growth of a knowledge-based economy can only be achieved if the economic, socio-political and legal frameworks of a country have the necessary background to realize the required indicators of a knowledge-based economy. In this paper, the economy growth-effected knowledge-based indicators are classified and prioritized using logarithmic fuzzy preference programming. Based on the results, the institutional and economic regime has the priority in comparison with other measures in economic growth. The results of prioritizing alternative criteria show that the technology foundation, structure of trained manpower, trade and capital, employment and economical trademark, respectively affect economic growth. Furthermore, the trade-related indicators are a low effect on economic growth, however, the technology-related indicators are most effective on it. Therefore, today’s oil and export economies are less prioritized than the application of knowledge, and in today’s world, the industrial economy cannot be advanced and must move towards a knowledge-based economy.