Fuzzy Preference Programming Method 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.

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.

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 new fuzzy approach based on BWM and fuzzy preference programming for hospital performance evaluation: A case study

Hospital performance evaluation (HPE) has a major role in improving the quality, safety, and effectiveness of health care services, so it is indispensable for proper and continuous operation of hospitals. Although several studies have been performed on HPE, few have used group decision-making (GDM). This study presents a comprehensive multi-criteria GDM model for HPE under uncertain conditions. In this model, we have combined the group best–worst​ method (GBWM) and fuzzy preference programming (FPP) method to create an applicable framework for GDM in which members of a decision-making group including decision-makers (DMs) have different expertise and the importance of criteria and DMs opinions are determined by a supervisor. The advantages of the proposed method include the integration of the GDM process in the form of a single model and there is no need to calculate separately the consistency of the decisions of the decision-making team members. Finally, a case study conducted on 5 hospitals in Tehran is presented to demonstrate the applicability and effectiveness of the proposed method. The results show that Sina Hospital, Baharloo Hospital, and Tehran Heart Center were ranked first to third, respectively. Also, we can conclude from this study that the proposed integrated framework is capable to address the HPE problem by using a GDM and considering the uncertainty of the comparisons made by decision-making team members.

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.

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.

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.

Comprehensive fuzzy FMEA model: a case study of ERP implementation risks

Failure mode and effects analysis (FMEA) is a prominent engineering technique for eliminating the potential failures emerged from various systems such as products, processes, designs, or systems. In the traditional FMEA, for each risk factor, severity, occurrence, and detectability ratings are multiplied and risk ranking number (RPN) is calculated. However, traditional FMEA has been subject of severe criticism in the literature and significant efforts have been made to overcome the shortcomings of the RPN. The present paper aims to put a step forward to enhance fuzzy FMEA by proposing a hybrid multi-attribute decision making model by combining fuzzy preference programming, fuzzy cognitive maps, and fuzzy graph-theoretical matrix approach. Fuzzy preference programming method is used to derive ratings of risk factors from incomplete, imprecise, and reciprocal pairwise comparison judgments. The causal dependencies among failure modes are modelled via fuzzy cognitive maps in order to capture the long term influences. Finally, fuzzy graph-theoretical matrix approach is employed to calculate risk priority indices of failure modes by taking into account interactions among risk factors. Although the FMEA method has been implemented in variety of technical problems, its potential in analyzing complex information systems have not been fully explored. Therefore, the proposed model is implemented in evaluating enterprise resource planning implementation risks in a real life case study.

An application of nonlinear fuzzy analytic hierarchy process in safety evaluation of coal mine

Thousands of mining accidents occur each year, especially in developing countries like China. To guarantee the safety and health of workers and reduce the probability of productivity decrease, this paper utilizes a nonlinear methodology to find the precedence of risk factors. Based on existing simulate experiments and relevant literatures, we draw a concept mapping of risk factors which involves managerial, environmental, operational and individual criteria. By using the fuzzy analytic hierarchy process (FAHP), we estimate and rank all of these factors to develop a management model and guide the safety managers in mining process. The logarithmic fuzzy preference programming (LFPP) method is applied to analyze the data. This method is new in risk assessment in coal mining process. The results are compared with that derived from the extent analysis (EA) method and with the help of the IBM SPSS Statistics the results are confirmed to be available in safety evaluation of coal mine. In addition, the proposed evaluation system is found out to be more convenient, precise and complete during the evaluation process, compared to traditional AHP and FAHP based on EA method.

An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics

We consider the problem faced by a company that must outsource reverse logistics (RL) activities to third-party providers. Addressing RL outsourcing problems has become increasingly relevant issue in the management science and decision making literatures. The correct evaluation and ranking of the decision criteria/priorities determining the selection of the best third-party RL providers (3PRLPs) is essential for the competitive performance of the outsourcing company. The method proposed in this study allows to identify and classify these decision criteria. First, the relevant criteria and sub-criteria are identified using a SWOT analysis. Then, Intuitionistic Fuzzy AHP is used to evaluate the relative importance weights among the criteria and the corresponding sub-criteria. These relative weights are implemented in a novel extension of Mikhailov's fuzzy preference programming method to produce local weights for all criteria and sub-criteria. Finally, these local weights are used to assign a global weight to each sub-criterion and create a ranking. We discuss the results obtained by applying the proposed model to a case study of a real company. In particular, these results show that the most important priority for the company when delegating RL activities to 3PRLPs is to focus on the core business, while reducing costs constitutes one of its least important priorities.

Supplier selection for competitive advantage in supply chain: an integrated fuzzy analytic hierarchy process using simulation approach

In today's global competitive scenario, supplier selection in the supply chain is very crucial for a competitive advantage in the manufacturing sector in India. The study presents an integrated approach for selection of the appropriate supplier to addressing the issues like innovation, after sale services and pricing. The paper proposed to solve the fuzzy analytic hierarchy process (FAHP) problem that combines the fuzzy preference programming (FPP) method to derive a crisp priority vector straightforward way from a set of fuzzy judgments by using simulation. This paper focused on the real situation, to demonstrate the effectiveness of the proposed model. The effectiveness of the model is discussed through a case conducted for Indian-based electrical manufacturing (ABC) company. For the preparation of their products, the company procures raw materials from different suppliers. Merely, a question arises which supplier is the best for the company.

Prioritizing Critical Barriers of Computerized Maintenance Management System ( CMMS ) by Fuzzy Multi Attribute Decision Making ( F-MADM ) ( Using LFPP )

The main aim of this study, are identifying and ranking effective barriers on setup Computerized Maintenance Management System (CMMS) in Shoa-e-Shargh Concrete plant as a case study. Proposed approach is based on Logarithmic fuzzy preference programming method. For rating the factors, with LFPP techniques modeling was performed and with LINGO software weight of each factor was determined. As a result, according to the experts opinion, the critical barriers were rated as F4: Management support > F1: Equipment availability > F3: Maintenance information > F2: Labor productivity > F5: Inventory control > F6: Environment controls.

A decision support system based on fuzzy reasoning and AHP–FPP for the ecodesign of products: Application to footwear as case study

The design stage represents one of the most critic steps for product development. Here, a great number of considerations have to be borne in mind, e.g., technical, functional, aesthetic or economic criteria. More recently, the increasing concerns on environmental aspects have added complexity to the process, known as ecodesign. In this respect, a framework to integrate the criteria provided by quantitative environmental indicators has been proposed on the basis of Fuzzy Preference Programming method features and fuzzy logic reasoning. As a result, an integrated Ecodesign Index (EcoInd) is obtained. This idea enables the decision making at process and product level taking into account different indicators at a time. The ecodesign of children's footwear was taken as case study and an ecodesign tool (decision support system) that included the estimation of environmental indicators and their integration was developed. Different models of shoes were analyzed to identify the most environmentally friendly design and to test the tool. In this case, the Ecological Footprint and two Environmental Risk Assessment indicators, namely Hazard Quotient and Cancer Risk, were selected as relevant environmental indicators and they were computed from data provided by a shoes manufacturer. Then, these indicators were integrated in the ecodesign tool and the EcoInd values were appraised for the children's footwear models analyzed. According to these figures, they were ranked as Red Leather>White Leather>White Synthetic>Pink Synthetic, from best to worst.

Method of Selection Manufacturing Projects Based on FANP Model

Selecting an optimal manufacturing project is a very important link in the production and operation chain. Only by the scientific system of decision-making process can the experts find out the best project which is not only quality and economical, but also safe and ecological. Considering the interaction and dependence of the indices and the uncertain and imperfect information during the process, a model for selecting an optimal manufacturing project based on fuzzy analytic network process (FANP) is established. Firstly an evaluation index system is built by taking into account the interaction and dependence relationship between indices. Secondly the weights of each level are determined by fuzzy preference programming (FPP) method and Matlab program .Then the alternatives are calculated by this system to find out which is the best one. Finally by comparing the result with that of ANP and FAHP, to prove the applicability and effectiveness of this decision-making methods.

ERP System Flexibility Measurement Based on Fuzzy Analytic Network Process

To meet the changes of internal and external environment, Enterprise Resources Planning (ERP) system needs to have a good flexibility. Flexibility is an indispensable request and is also a way that must be taken during the establishment process of ERP. Flexibility measurement is an important item for the implementation of ERP flexibility. According to the characteristics of ERP system, an index system for flexibility measurement of ERP system is presented with the interdependence and feedback relationships among criteria and/or indices being taken into account. Due to the vagueness and uncertainty information during the process of flexibility measurement, triangular fuzzy numbers are used to indicate the preference opinions of experts and decision makers. A flexibility measurement model of ERP system based on fuzzy analytic network process (FANP) is proposed. The local weights of criteria and indices are derived by fuzzy preference programming (FPP) method. An unweighted supermatrix based on the network structure of index system is developed , and the limit supermatrix is generated . The flexibility level of ERP system can be measured by the weights and scores of ERP. Finally, a case is given by the proposed method.

WEIGHTED PRIORITIZATION MODELS IN THE FUZZY ANALYTIC HIERARCHY PROCESS

The paper deals with the group prioritization problem in the fuzzy analytic hierarchy process. We extend the fuzzy preference programming method to fuzzy group prioritization by introducing important weights of decision-makers (DMs). The modified prioritization problem is represented as a weighted fuzzy goal programming model. Additionally, we represent the uncertain DMs' importance weights as fuzzy numbers and modify the goal programming model by a possibilistic approach. Both proposed models transform the initial prioritization problems with fuzzy or crisp important weights into equivalent crisp linear programs. Unlike the known fuzzy prioritization methods, the proposed approach does not require an additional defuzzification procedure for final ranking of alternatives and can deal with incomplete set of comparison judgments.

Validating AHP, fuzzy alpha cut and fuzzy preference programming method using clustering technique

Fuzzy multicriteria analysis (MA) methods suitable for a given decision problem usually differ in aggregation processes for handling the alternatives’ performance ratings and criteria weights. Due to their structural differences, these methods often produce inconsistent ranking results for the same fuzzy MA problem. This paper presents a validation procedure using fuzzy clustering for selecting among inconsistent ranking results produced by analytical hierarchy process (AHP), fuzzy alpha cut and fuzzy preference programming (FPP) methods for a given problem. The procedure compares the ranking results obtained by fuzzy MA methods using different aggregation algorithms with the clustering results of the alternatives by fuzzy clustering. An empirical study of evaluating major port trusts of India is conducted to demonstrate the effectiveness of the validation procedure.