Logarithmic Fuzzy Preference Programming Research Articles

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.

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.

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.

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.

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.

Decision making under uncertainty in the alarm systems response

Purpose The purpose of this paper is to develop an advanced decision-making support for the appropriate responding to critical alarms in the hazardous industrial facilities. Design/methodology/approach A fuzzy analytical hierarchy process is suggested by considering three alternatives and four criteria using triangular fuzzy numbers to handle the associated uncertainty. A logarithmic fuzzy preference programming (LFPP)-based nonlinear priority method is employed to analyze the suggested model. Findings A quantitative decision-making support is not only a necessity in responding to critical alarms but also easy to implement even in a relatively short reaction time. Confirmation may not be the appropriate option to deal with a critical alarm, even with the availability of the needed resources. Practical implications A situation related to a flammable gas alarm in a gas plant is treated using the developed model showing its practical efficiency and practicality. Originality/value The proposed model provides a rational, simple and holistic fuzzy multi criteria tool with a refined number of criteria and alternatives using an LFPP method to handle process alarms.

Sustainability performance assessment of industrial corporation using Fuzzy Analytic Network Process

Abstract Nowadays, sustainability is one of the leading value-increasing strategies of industrial corporations. In order to implement a sustainable strategy, it is necessary to assess the sustainability performance. Nevertheless, this still represents a relevant gap in the literature and practice. There are a number of frameworks and tools for sustainability performance assessment. Most of them are based on a set of indicators. However, in many corporations the sets are applied and kept disaggregated. Due to this deficiency, aggregated sustainability assessment approaches are often explored by researchers and practitioners. One of the main approaches is the use of multi criteria decision making methods which transform multiple indicator values to a single dimension. Since there are many complex interdependencies among the used sustainability key performance indicators and their relations are uncertain, Fuzzy Analytic Network Process (FANP) appears to be an appropriate tool for aggregated assessing the sustainability performance. The aim of this paper is to develop and verify a methodology for aggregated sustainability performance assessment of an industrial corporation using the FANP approach. Based on analysis of the existing FANP approaches and discussion of their advantages and disadvantages, Logarithmic Fuzzy Preference Programming Methodology was selected for this purpose. Our developed methodology provides a comprehensive aggregated sustainability performance assessment system based on combining three evaluation methods (basic evaluation, trend evaluation, and categorization), and Action matrix, which defines appropriate corrective actions level to achieve the sustainability performance targets. A case study from metallurgical industry was used to verify the developed methodology and to identify critical points and recommendations for its implementation.

Ranking Strategic Objectives in a Strategy Map Based on Logarithmic Fuzzy Preference Programming and Similarity Method

AbstractThis paper aims to rank strategic objectives in a strategy map to improve the efficiency of strategy implementation. Objectives are ranked based on strategic destinations using the combination of Logarithmic Fuzzy Preference Programming (LFPP) and similarity method. In the first step, the weight of strategic destinations is obtained using LFPP technique; then objectives are ranked by similarity method. Similarity method uses the concept of alternative gradient and magnitude for effectively solving the general multi-criteria analysis problem. Finally, objectives are ranked in an actual strategy map. As a practical and efficient tool, the proposed approach can assist managers and decision-makers in drawing more efficient output from strategy maps.

An Extended Consistent Fuzzy Preference Relation to Evaluating Website Usability

In the current era, website developers recognize usability evaluation as a significant factor in the quality and success of e-commerce websites. Fuzzy Analytical Hierarchy Process (FAHP) is one method to measure the usability of the website. Several researchers have applied Logarithmic Fuzzy Preference Programming (LFPP) approach to deriving crisp weight from fuzzy pairwise comparison matrix of FAHP approach. However, there is a lack of LFPP method in determining the consistency index of the decision-maker judgment. In some cases, LFPP method will produce a consistency value of 0 from consistent fuzzy comparison matrices. This value indicates there is a contradiction with what the previous researchers have said, that a constant matrix value should be more than 0. This research proposes the extended Consistent Fuzzy Preference Relation (ECFPR) to assist the regular judgment for specifying the weights in measuring e-commerce website usability. The CFPR method used to form a new pairwise comparison matrix. ECFPR was calculating the lower and upper values at the fuzzy triangular number from the only n-1 comparison, where n is the number of criteria. The numerical experiment showed that the consistency index obtained by extended CFPR method was more significantly better than LFPP method. It was revealed that the optimal value always more than 0. The consistency index of ECFPR method has a higher mean value than LFPP, so that the use of the ECFPR method can improve the amount of consistency comparison matrices. The ECFPR method was also successfully implemented with the experimental case on evaluating e-commerce website usability.

Improvement of risk assessment in the FMEA using nonlinear model, revised fuzzy TOPSIS, and support vector machine

In every organization, performing accurate risk assessment along with consideration of increasing accidents is a necessary tool to prevent and reduce the fatal and non-fatal consequences of their occurrence. One of the most popular methods of risk assessment is Failure Mode and Effects Analysis, which evaluate failure modes in a system by using risk priority numbers (RPNs). These methods have been criticized for including several deficiencies such as the effect of personal ’opinions, the same importance of the factors and risk rating. The present work utilizes a hybrid approach based on support vector machine and fuzzy inference system to decrease the effect of personal's opinions in determining the factors of the severity and occurrence. Also, Logarithmic Fuzzy Preference Programming is used to determine the crisp weight of dependent factor of FMEA and revised fuzzy TOPSIS used for more accurate ranking of risks. One main feature of the proposed model is that it can be used to evaluate safety risks in all organizations. To investigate the suitability of this approach, the proposed model was presented in the Copper leaching factory, Kerman, Iran. The results showed that this model has the ability to predict severity and occurrence refers to occupational accidents which occurred in a 5-year period (2012–2017) with accuracy of 87% and 95%, respectively. Also, based on the results, it was found that the weights of severity, occurrence, and detection were 0.479, 0.335, and 0.186, respectively. Results of the ranking process showed that the risk of fall from height and stucking between the objects had the highest and the lowest priority, respectively.

University Website Quality Ranking using Logarithmic Fuzzy Preference Programming

The current tight competition in developing University websites forces developers to create better products that meet users needs and convinient. There are at least two factors representing university websites; accessibility and usability. We test three criteria of accessibility and usability that are called stickiness, backlink, and web page loading time. Usability and accessibility are closely related to subjective user judgments. Human judgment cannot be valid. Thus the use of fuzzy numbers are expected to provide solutions in calculating the results. In this research, the question of usability is a multi criteria decision-making problem that is caused by its complex structure. We use the Logarithmic Fuzzy Preference Programming (LFPP) method, which is a refinement of the Fuzzy Analytical Hierarchy Process method, to solve this problem. This research aims to re- assess the rank of five Indonesian university websites. Based on LFPP method, we obtain that the equation of model gets high consistency of the set priority matching to fuzzy pairwise comparison matrix of three selection criteria. The calculation results show that stickiness is the most significant factor that affects the quality of the websites.

A hybrid hierarchical fault diagnosis method under the condition of incomplete decision information system

Processing attribute reduction plays a key role in the fault diagnosis of incomplete decision information system (IDIS), and it improves the efficiency and accuracy of fault diagnosis. Tolerance relation-based attribute reduction is widely used in the IDIS. However, the fuzziness of relation-based classification always exists in the practical attribute reduction problems of fault diagnosis as the incompleteness and uncertainty of data information, and the traditional tolerance relation-based attribute reduction methods are not suitable for fault diagnosis of IDIS. Therefore, this paper proposes a hybrid hierarchical fault diagnosis method with the combination of tolerance relation-based attribute reduction method and integrated logarithmic fuzzy preference programming (LFPP) based methodology. The method utilizes both qualitative and quantitative data information and constructs the hierarchical structure of fault diagnosis in IDIS. The integrated LFPP based methodology obtains the unique normalized optimal significance priorities vector for attribute fuzzy pairwise comparison matrices simultaneously and directly as the sorting part of proposed method. The tolerance relation-based attribute reduction method decomposes the fault attributes reduction problem into multiple sub-problems, which is the decomposing part of proposed method. Hence, the proposed hybrid method can handle the fuzziness of relation-based classification and mitigate complexity attribute reduction for fault diagnosis of IDIS. Finally, an engineering case for strategy reduction of fault diagnosis is provided to demonstrate the feasibility of the proposed method and obtain the reduction diagnosis strategies. Another test case is given for verifying the validity of the reduction results and for comparison between the proposed method and other different methods, which shows that the method is indeed efficient and has greater advantages at producing higher accuracy, reducing difficulty and mitigating complexity in fault diagnosis.

Fuzzy AHP approach to passenger aircraft type selection

In order to choose an aircraft to operate a defined set of routes, an airline needs to consider passenger's interests in addition to its own interests. The goal of the paper is to propose a methodology which will help choose aircraft type(s) that meets the market conditions and airline's requirements best for known route network and known forecasted air travel demand by routes. Although this is a multi-criteria problem, planners usually have to make decisions under uncertainty, incorporating vagueness of human thinking. Therefore, we proposed the Fuzzy Analytic Hierarchy Process (FAHP) based approach. To derive crisp priorities from fuzzy pairwise comparison matrices, a logarithmic fuzzy preference programming (LFPP) method is employed, as well as an improved LFPP method. With regard to different criteria that involve quantitative and qualitative aspects, three criteria and ten sub-criteria are selected. Experts from different airlines and universities responded to an interview, contributing to the design of pairwise comparison matrices for the criteria and sub-criteria. The regional airline case study is used to show the applicability of the methodology proposed.

An integrated logarithmic fuzzy preference programming based methodology for optimum maintenance strategies selection

Selecting optimum maintenance strategies plays a key role in saving cost, and improving the system reliability and availability. Analytic hierarchical process (AHP) is widely used for maintenance strategies selection in the Multiple Criteria Decision-Making (MCDM) field. But the traditional or hybrid AHP methods either produce multiple, even conflict priority results, or have complicated algorithm structures which are unstable to obtain the optimum solution. Therefore, this paper proposes an integrated Logarithmic Fuzzy Preference Programming (LFPP) based methodology in AHP to solve the optimum maintenance strategies selection problem. The multiplicative constraints and deviation variables are applied instead of additive ones to utilize both qualitative and quantitative data, and process the upper and lower triangular fuzzy judgments to obtain the same priorities. The proposed methodology can produce the unique normalized optimal priority vector for fuzzy pairwise comparison matrices, and it is capable of processing all comparison matrices to obtain the global priorities simultaneously and directly in the form of super-matrix according to the different requirements and judgments of decision-makers. Finally, an example is provided to demonstrate the feasibility and validity of the proposed methodology.

Logaritmic Fuzzy Preference Programming Approach for Evaluating University Ranking Optimization

Assesing quality university’s website trough webometrics is becoming one of many measures in World Class University. To get good grades, so that it can compete with other universities in the world, it needs to be pursued strategies based on the achievement of the perspective of cost (expenses) and the condition of the availability and readiness of human resource (HR owned) by the institution. Webometrics ranking optimization tailored to the institutional capacity is absolutely necessary, in order to achieve the expected goals effectively and fuel-efficient. Therefore, this paper discussed the application of the Analytical Hierarchy Process with Logarithmic Fuzzy Preference Programming combination proved to covered of the methods FPP on the university web ranking optimization. From the results of sub-criteria weighting based on the perspective of cost and human resources, earned the highest ranking among other factors recommended monitoring the ranking of sites ahrefs (C332) and majesticseo (C331) as well as increasing the number of links from other websites (C321).

Multi-criteria sustainability assessment of urban sludge treatment technologies: Method and case study

This study aims at developing a sustainability assessment framework for assessing the technologies for the treatment of urban sewage sludge based on the logarithmic fuzzy preference programming based fuzzy analytic hierarchy process (LFPPFAHP) and extension theory. LFPPFAHP was employed to determine the weights of the criteria for sustainability assessment, and extension theory was used to prioritize the alternative technologies for the treatment of urban sewage sludge and grade their sustainability performances. An illustrative case including three technologies (compositing, incineration, and resource utilization) was studied by the proposed method, and compositing, incineration, and resource utilization are recognized as “Moderately Sustainable”, “Not Sustainable”, and “Highly Sustainable”, respectively. The sustainability sequence in the descending order is resource utilization, compositing and incineration, and the result is consistent to that determined by the sum weighted method.