Fuzzy Preference Ranking Organization Method Research Articles

Computational analysis of the Logistics Supply Chain from a global trade perspective

Global trade is based on coordinated factors, that means labor and products are moved from their point of origin to the point of use. Strategies have a significant impact on global trade because they enable the effective development of goods across international borders. The decision making is an important task for the development of Logistics Supply Chain (LSC) infrastructure and process. Decisions on supplier selection, production schedule, transportation routes, inventory levels, pricing strategies, and other issues need to be made. These decisions may have a big influence on customer service, profitability, operational efficiency, and overall competitiveness. The Artificial Intelligence (AI) approach of Fuzzy Preference Ranking Organization Method for Enrichment Evaluation (Fuzzy-Promethee-2) is used to assess the priority selection of the factors associated with the LSC and evaluate the importance in global trade. The role of AI is very useful compare to statistical analysis in terms of decision making. The computational analysis placed promotion of exports as the most important priority out of five selected attributes in LSC, with infrastructure development. The result suggests that LSC depends heavily on export promotion as the most significant attribute. Infrastructural development also appeared another factor influencing LSC. The foreign investment was ranked the lowest. The evaluated results are useful for the policy makers, supply chain managers and the logistics professionals associated with the supply chain management.

Three-tier integrated demand-supply energy management for optimized energy usage in institutional building

Sustainable Energy Plan is an indispensable field of study in the context of Building Energy Management (BEM) which yearns for a cutting-edge technique to raise consumers’ level of contentment with energy usage. This paper introduces a three-tier strategic approach for demand cum supply side management at the prosumer end named TID-SEM (Three-tier Integrated Demand cum Supply Energy Management) framework. The framework commences with data collection, machine learning, and Random Forest classifier-based Demand side management to reduce peak demand. Followingly, a feasibility study on available sources (Solar PV + Battery + Diesel Generator + Grid) was done using the HOMER simulator. The quality techno-enviro-economic shows distinguished optimal one under each criterion. Ultimately to overcome, an ideal computative multicriteria decision-making approaches Fuzzy Analytic Hierarchy Process, the Fuzzy Preference Ranking Organization Method for Enrichment Evaluations, and the Fuzzy Technique for Order of Preference by Similarity to Ideal Solution employed to highlight the most optimal configuration. The study focuses on a department building within a higher education institution in the Madurai district, Tamil Nadu, India. On implementing the TID-SEM, ‘PV + Grid’ after DSM configuration was ranked as the most optimum. Lastly, a sensitivity analysis was conducted to evaluate the techno-enviro-economic impact of uncertain parameters on the optimal configuration, ensuring the robustness of the proposed framework.

Comparative evaluation of FDA‐approved drugs for managing the symptoms of AD

AbstractBackgroundAD is a neurodegenerative disorder characterized by the gradual deterioration of mental abilities over time. According to estimates, 5.8 million Americans age 65 and older currently have AD dementia, and by the year 2050, that number is projected to reach 13.8 million. There is currently no cure for AD but medications have been shown to help some people manage the disease’s symptoms. Although there are disease modifying drugs target the amyloids such as Aducanumab and Lecanemab, high costs (around $30,000/year) and severe side effects make them unfavorable for many patients suffering from AD. However, prescribing the appropriate drug considering many factors affecting the decision‐making such as cost of the drug, safety of drug, and patient’s medical history might be a relatively challenging task for a physician and time‐consuming for patients in case the first prescribed drug is not tolerated or worsens the symptoms. Therefore, there is an urgent requirement for better tools to aid in prescribing the most appropriate drug.MethodsIn this regard, we propose using fuzzy Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), a multi‐criteria decision‐making (MCDM) method, to compare, evaluate, and rank the FDA‐approved drugs for managing AD symptoms. Drugs used in this study include Donepezil [Aricept], Rivastigmine [Exelon], Galantamine [Razadyne], Memantine [Namenda], and Memantine + Donepezil [Namzaric] with the following criteria: cost, safety of drug, tolerability, interaction with other drugs, response to cognitive impairment, symptoms management rate, and side effect. To the best of our knowledge, any decision‐making methodologies have not been proposed for use in identifying the most appropriate drugs for AD patients.ResultResults showed that [Namzaric] came first in the ranking, and followed by [Aricept], [Namenda], and [Razadyne] while [Exelon] ranked as the least favorable drug.ConclusionMCDM methods can be used as a decision‐aid system for evaluating AD drugs and this study can be extended including more criteria and new drugs as they become available.

Radiopharmaceutical selection for tau PET imaging

AbstractBackgroundThe accumulation of pathologically misfolded tau is a feature that is shared by a group of neurodegenerative disorders that are collectively referred to as tauopathies. AD is the most prevalent of these tauopathies. Neurofibrillary tangles are characterized by the presence of hyperphosphorylated protein (tau), and senile plaques are characterized by the presence of amyloid peptide aggregates. PET imaging is fully quantitative and enables accurate spatial assessment. However, as a result of non‐specific binding and the limited spatial resolution of PET scanners, this technique requires not only the presence of tau inclusions in a brain region but also sufficient density and spatial extent of those inclusions in order to generate a sufficient signal‐to‐noise ratio. With the development of tau‐specific ligands for use with PET, it is now possible to investigate the role that tau pathology plays in the development and progression of these disorders. The purpose of this study is to analyze, compare, and rank the currently available tau PET tracers for imaging in research and routine clinical settings.MethodWe proposed using the fuzzy Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE), a multicriteria decision‐making (MCDM) tool. First‐generation tau PET tracers ([18F]AV1451, [11C]PBB3, [18F]THK5117, [18F]THK5351, [18F]THK5317, and [18F]THK5105) and second‐generation compounds ([18F]MK‐6240, [18F]JNJ‐067, [18F] PI‐2620, [18F]RO‐948 (previously referred to as [18F]RO69558948), [18F]PM‐PBB3, [18F]GTP1, [18F]JNJ‐64349311 ([18F]JNJ311), [11C]RO‐6931643, and [11C]RO‐6924963) were selected to investigate in this study. The evaluation is based on relative parameters, such as specificity, target binding accuracy, neocortical uptake, radiation exposure, brain penetration, free fraction in plasma, defluorination, and adverse reactions, and the importance that is placed on each of these parameters is weighted differently.ResultThe results from this study show that [18F]RO‐948 is the most favorable tau PET ligand, while[18F]THK5105 is the least favorable one based on the considered weighted criteria in this study.ConclusionFuzzy PROMETHEE can be used as a decision‐aid system to select the proper radiopharmaceuticals for tau PET imaging. This study can be extended to include more tracers and criteria and might help researchers and concerned readers to select the optimal tau PET tracer.

Assessment of UTI Diagnostic Techniques Using the Fuzzy-PROMETHEE Model.

Accurate diagnosis of urinary tract infections (UTIs) is important as early diagnosis increases treatment rates, reduces the risk of infection and disease spread, and prevents deaths. This study aims to evaluate various parameters of existing and developing techniques for the diagnosis of UTIs, the majority of which are approved by the FDA, and rank them according to their performance levels. The study includes 16 UTI tests, and the fuzzy preference ranking organization method was used to analyze the parameters such as analytical efficiency, result time, specificity, sensitivity, positive predictive value, and negative predictive value. Our findings show that the biosensor test was the most indicative of expected test performance for UTIs, with a net flow of 0.0063. This was followed by real-time microscopy systems, catalase, and combined LE and nitrite, which were ranked second, third, and fourth with net flows of 0.003, 0.0026, and 0.0025, respectively. Sequence-based diagnostics was the least favourable alternative with a net flow of -0.0048. The F-PROMETHEE method can aid decision makers in making decisions on the most suitable UTI tests to support the outcomes of each country or patient based on specific conditions and priorities.

Minkowski distance measure in fuzzy PROMETHEE for ensemble feature selection

A data preprocessing step is necessary to modulate effective and efficient data that can be utilized in numerous data mining and machine learning problems, especially in high-dimensional datasets. Most of the feature selection methods are unstable as different subsets provide various subsets of features giving different classification accuracy. Through ensemble feature selection, higher accuracy can be achieved and it has been verified theoretically and experimentally that diversity among base classifiers further improves its accuracy. In this paper, we model the ensemble feature selection methodology as a decision-making technique. We consider Minkowski distance for fuzzy preference ranking organization method for enrichment evaluation (PROMETHEE) decision making problem as the ensemble feature selection problem. Due to its main advantages in minimizing scalability between criteria and its ease of use for mathematical computations that outrank alternatives, PROMETHEE is an effective tool. To establish the superiority of the proposed methodology, a comparison between existing methodologies has been carried out through various performance metrics.

An Integrated Interval Type-2 Fuzzy PROMETHEE-II Decision Model for the Selection of Medical Waste Treatment Techniques

The selection of medical waste treatment techniques has become a serious health and safety issue since the amount and variety of medical waste are rapidly increasing during the Corona Virus Disease 2019 (COVID-19) pandemic in the whole world. This paper aims to propose an integrated interval type-2 fuzzy Preference Ranking Organization Method for Enrichment Evaluations-II (PROMETHEE-II) decision model for medical waste treatment techniques selection problem considering interactive relationships among criteria under a high uncertain environment. First, interval type-2 fuzzy sets (IT2FSs) are introduced to express imprecision information within the context of high uncertainty. Second, a type-2 Prioritized Aggregation (PA) operator is constructed to aggregate the evaluation information considering the priority of experts. Third, the Decision Making Trial and Evaluation Laboratory (DEMATEL) method is combined with the IT2FS to calculate the weights of criteria considering the interaction relationships among them. Then, an extended interval type-2 fuzzy PROMETHEE-II method is proposed to rank each alternative, in which a distance measure-based preference function is adopted. After that, a case study of medical waste treatment selection in Jiangsu province is used to illustrate the effectiveness of the proposed approach. The results demonstrate that incineration is the most appropriate approach in handling medical waste. Finally, sensitivity analysis and comparative analysis are implemented to further test the advantages of the proposed approach.

A Multi-Criteria Decision Aid Tool for Radiopharmaceutical Selection in Tau PET Imaging.

The accumulation of pathologically misfolded tau is a feature shared by a group of neurodegenerative disorders collectively referred to as tauopathies. Alzheimer's disease (AD) is the most prevalent of these tauopathies. Immunohistochemical evaluation allows neuropathologists to visualize paired-helical filaments (PHFs)-tau pathological lesions, but this is possible only after death and only shows tau in the portion of brain sampled. Positron emission tomography (PET) imaging allows both the quantitative and qualitative analysis of pathology over the whole brain of a living subject. The ability to detect and quantify tau pathology in vivo using PET can aid in the early diagnosis of AD, provide a way to monitor disease progression, and determine the effectiveness of therapeutic interventions aimed at reducing tau pathology. Several tau-specific PET radiotracers are now available for research purposes, and one is approved for clinical use. This study aims to analyze, compare, and rank currently available tau PET radiotracers using the fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE), which is a multi-criteria decision-making (MCDM) tool. The evaluation is based on relatively weighted criteria, such as specificity, target binding affinity, brain uptake, brain penetration, and rates of adverse reactions. Based on the selected criteria and assigned weights, this study shows that a second-generation tau tracer, [18F]RO-948, may be the most favorable. This flexible method can be extended and updated to include new tracers, additional criteria, and modified weights to help researchers and clinicians select the optimal tau PET tracer for specific purposes. Additional work is needed to confirm these results, including a systematic approach to defining and weighting criteria and clinical validation of tracers in different diseases and patient populations.

A comparative analysis of DOACs vs warfarin for venous thromboembolism treatment in renal insufficiency

Aim: Annually, millions of people die from kidney diseases making it a serious public health concern. Patients with renal insufficiency have hemostatic defects that enhance their risk of thrombosis and bleeding. Evaluating the advantages and hazards of anticoagulant thromboprophylaxis is challenging. Material and Methods: By using the fuzzy preference ranking organization method for enrichment evaluation (fuzzy PROMETHEE) and fuzzy multi-criteria optimization and compromise solution (fuzzy VIKOR), which are analytical multi-criteria decision-making (MCDM) techniques, this study aims to analyze diverse types of anticoagulants; specifically, Vitamin K-Antagonist (warfarin) and direct oral anticoagulants namely; Dabigatran, Rivaroxaban, Apixaban, and Edoxaban, by considering various criteria. Results: Direct oral anticoagulants outperformed warfarin in terms of safety and effectiveness. Among all the analyzed anticoagulants, Apixaban was found to be the best-ranked anticoagulant with both fuzzy PROMETHEE and fuzzy VIKOR, with a net flow of 0.0064 and the lowest Qj value of 0.0000, respectively, compared with other alternatives depending on the selected criteria while warfarin was found to be the least ranked anticoagulant with a net flow of -0.0043 and the highest number of Qj being 1, respectively. Conclusion: The MCDM techniques used were effective, highly accurate, and sensitive in the comparison of anticoagulant therapy for venous thrombosis in renal insufficiency based on the chosen criteria. The proper use of a safe and effective anticoagulant will boost better performance not only in patients’ survival and well-being but also in the health care center.

SELECTION OF SOCIAL MEDIA PLATFORMS USING FUZZY PROMETHEE METHOD WITH DIFFERENT SCENARIO TYPES

Social media are widely and mostly utilized in many areas to increase advertising, client relations, tourism, journey and many others by means of members or organizations. Social Media Platforms (SMPs) are becoming increasingly popular in the world. The selection of SMPs, which might be widely and mostly utilized by members or organizations, becomes a critical and complex problem. Moreover, some problems that need to be investigated which consist of the reason for the use of different SMPs, purpose, and frequency of use. In this study, we have investigated to explain these problems via questionnaire which evaluates by undergraduate pupils (N = 173). Therefore, we have created twelve different scenarios that are different weights and thresholds and handled the selection and ranking of SMPs by fuzzy Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) I and II. Consequently, we able to conclude that "Which SMPs were more preferred?", "What are the reasons for selection of SMPs? and "Which criteria are more important for this selection?". In addition, this study can be guiding an infrastructure for the content of new SPMs in the future.

Mathematical Assessment of Machine Learning Models Used for Brain Tumor Diagnosis

The brain is an intrinsic and complicated component of human anatomy. It is a collection of connective tissues and nerve cells that regulate the principal actions of the entire body. Brain tumor cancer is a serious mortality factor and a highly intractable disease. Even though brain tumors are not considered a fundamental cause of cancer deaths worldwide, about 40% of other cancer types are metastasized to the brain and transform into brain tumors. Computer-aided devices for diagnosis through magnetic resonance imaging (MRI) have remained the gold standard for the diagnosis of brain tumors, but this conventional method has been greatly challenged with inefficiencies and drawbacks related to the late detection of brain tumors, high risk in biopsy procedures, and low specificity. To circumvent these underlying hurdles, machine learning models have recently been developed to enhance computer-aided diagnosis tools for advanced, precise, and automatic early detection of brain tumors. This study takes a novel approach to evaluate machine learning models (support vector machine (SVM), random forest (RF), gradient-boosting model (GBM), convolutional neural network (CNN), K-nearest neighbor (KNN), AlexNet, GoogLeNet, CNN VGG19, and CapsNet) used for the early detection and classification of brain tumors by deploying the multicriteria decision-making method called fuzzy preference ranking organization method for enrichment evaluations (PROMETHEE), based on selected parameters, in this study: prediction accuracy, precision, specificity, recall, processing time, and sensitivity. To validate the results of our proposed approach, we performed a sensitivity analysis and cross-checking analysis with the PROMETHEE model. The CNN model, with an outranking net flow of 0.0251, is considered the most favorable model for the early detection of brain tumors. The KNN model, with a net flow of -0.0154, is the least appealing option. The findings of this study support the applicability of the proposed approach for making optimal choices regarding the selection of machine learning models. The decision maker is thus afforded the opportunity to expand the range of considerations which they must rely on in selecting the preferred models for early detection of brain tumors.

Fuzzy-Based PROMETHEE Method for Performance Ranking of SARS-CoV-2 IgM Antibody Tests.

Antibody tests, widely used as a complementary approach to reverse transcriptase-polymerase chain reaction testing in identifying COVID-19 cases, are used to measure antibodies developed for COVID-19. This study aimed to evaluate the different parameters of the FDA-authorized SARS-CoV-2 IgM antibody tests and to rank them according to their performance levels. In the study, we involved 27 antibody tests, and the analyzes were performed using the fuzzy preference ranking organization method for the enrichment evaluation model, a multi-criteria decision-making model. While criteria such as analytical sensitivity, specificity, positive predictive value, and negative predictive value were evaluated in the study, the ranking was reported by determining the importance levels of the criteria. According to our evaluation, Innovita 2019-nCoV Ab Test (colloidal gold) was at the top of the ranking. While Cellex qSARS-CoV-2 IgG/IgM Rapid Test and Assure COVID-19 IgG/IgM Rapid Tester ranked second and third on the list, the InBios-SCoV 2 Detect Ig M ELISA Rapid Test Kit was determined as the least preferable. The fuzzy preference ranking organization method for enrichment evaluation, which has been applied to many fields, can help decision-makers choose the appropriate antibody test for managing COVID-19 in controlling the global pandemic.

Evaluation of irrigation methods in Söke Plain with HF-AHP-PROMETHEE II hybrid MCDM method

Söke Plain (Turkey) is one of the two plains where cotton production is the highest in Turkey, the leading country for cotton production in the Mediterranean Basin. The cropping pattern in Söke Plain is dominated by cotton with a ratio of 97%. The overall irrigation scheme is equipped with conventional systems (i.e., surface, furrow) whose efficiency is approximately 50% due to high evaporation and physical losses. Water efficiency improvement in cotton irrigation necessitates a thorough evaluation of the agricultural water management for Söke Plain, a water-scarce region under drought threat. In this paper, a hybrid multi-criteria decision making (MCDM) method is presented for the evaluation and selection of irrigation methods. This process involves various potentially conflicting qualitative and quantitative criteria, therefore, a hybrid MCDM method such as HF-AHP-PROMETHEE II is needed to make decisions. In HF-AHP-PROMETHEE II, “Hesitant Fuzzy Analytic Hierarchy Process” (HF-AHP) is first implemented to determine importance weights of criteria and then “Hesitant Fuzzy Preference Ranking Organization Method for Enriching Evaluations II” (HF-PROMETHEE II) is utilized to assess and rank the irrigation method alternatives. For comparison analysis, HF-AHP-TOPSIS (HF-AHP-Technique for Order Preference by Similarity to Ideal Solution) method is also implemented to the same problem. A case study is presented where five irrigation method alternatives in Söke Plain are assessed by five expert decision-makers (DMs), based on fifteen evaluation criteria. Sprinkler is found to be the first ranked irrigation method among five alternatives with both HF-AHP-PROMETHEE II and HF-AHP-TOPSIS resulting in the same ranking. The selection of this irrigation technique by the expert DMs is compliant with prevailing regional features related to hydrologic, climatic, environmental conditions and with regard to cotton, one of the highest water-consuming crops.

Selection of Suppliers in Industrial Manufacturing: A Fuzzy Rough PROMETHEE Approach

In supply chain management (SCM), the selection of suppliers plays a vital role in an efficient production process. Over the last few years, to form a trade-off between the quantitative and qualitative criteria, the selection of suppliers in SCM is considered very conclusive. The decisions generally demand different criteria to balance between every possible inconsistent parameters involving subjectivity and uncertainty in the process. The evaluation information mostly depends on the experience and knowledge of experts that are unsure and indistinct. This study introduces a novel decision-making method by integrating rough approximations with fuzzy numbers and preference ranking organization method for enrichment evaluation (PROMETHEE) to deal with subjective and objective vagueness in the assessment of decision makers. To minimize the dependency on experts’ judgements, entropy weights are computed from the original data set. The preference index is then computed using entropy weights and deviations among alternatives. The alternatives are ranked using the intersection of both positive flow and negative flow. To show the significance and importance of fuzzy rough PROMETHEE method, a case study of supplier selection in industrial manufacturing is discussed in detail. The developed method is effectively used to rank the supplier alternatives under given criteria. The results are then compared with different rough numbers and fuzzy numbers based on MCDM methods. The fuzzy rough PROMETHEE method can be efficiently used for the selection of the best suppliers to reduce losses and maximize the production process.

Selection of photodetectors in nuclear medical imaging using MCDM methods

Photodetectors used in nuclear medical imaging such as single photon emission computed tomography (SPECT) and positron emission tomography (PET) are an important element of radiation detection systems. Selecting the photodetector depends on many physical criteria including quantum efficiency (QE) and gain. The aim of this study is to apply multi-criteria decision-making (MCDM) methods to determine the optimum photodetector based on the evaluation and comparison of complex and multiple criteria. The photodetectors investigated in this study are photomultiplier tube (PMT), avalanche photodiode (APD) and silicon photomultiplier (SiPM). The bias voltage, gain, rise time, and QE were selected since they are considered as the key criteria for the photodetectors. Then, the corresponding values of each criteria were defined and preferred weights were assigned to each criteria based on the desired outcome. The fuzzy preference ranking organization method for enrichment of evaluations (PROMETHEE) and fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) methods were used to evaluate the alternatives. The results showed that conventional PMT came first in the ranking, followed by SiPM, while APD was the least desirable photodetector according to the fuzzy PROMETHEE and fuzzy TOPSIS methods based on the selected criteria and assigned weights. MCDM methods were used to select photodetectors used in PET and SPECT systems. One can incorporate as many alternatives and criteria as needed and assign the weights accordingly.

Fuzzy preference ranking organization method for enrichment evaluation‐based node cooperation technique for improving QoS in MANETs

SummaryCooperation between mobile nodes is the predominant factor required for achieving reliable data dissemination in mobile ad hoc networks (MANETs), since non‐cooperative nodes crumbles the routing process and degrades network performance. The degree of non‐cooperation rendered by mobile nodes need to be accurately identified by exploring feasible and comprehensive number of criteria to achieve maximized cooperation in uncertain MANET. In this paper, fuzzy preference ranking organization method for enrichment evaluation (FPROMETHEE)‐based node cooperation technique is proposed with the merits of trapezoidal fuzzy interval numbers that aids in the selection of high trustworthy mobile nodes for attaining maximized data delivery. This FPROMETHEE technique utilizes complex and multiple criteria for potential ranking in the presence of finite number of mobile nodes. It prevented the bias of information that is more common during the judgmental process associated with the cooperation estimation of mobile nodes. It includes information about the relative weights of criteria and decision maker's preferences for estimating the trust of mobile nodes to include or isolate them from the routing path. It is also useful in handling the degree of uncertainly involved in the process of routing without compromising Quality of Service (QoS) level in the network. The simulation experiments of the proposed FPROMETHEE technique conducted using ns‐2 simulator confirmed its predominance in enhancing the packet dissemination rate, throughput, energy consumptions, total overhead, and packet loss rate for different number of mobile nodes, malicious nodes, and pairs of source and destination nodes.

Retracted] Preorder of Factors Affecting Oil Prices: Fuzzy PROMETHEE Approach

Oil is a highly demanded worldwide product as oil prices can have a major economic and social impact. There are many factors affecting oil prices, but little is known about which factor mostly influenced oil prices. To understand what caused the fluctuation of oil prices, the investigation leading up to the identification of the factors must be constructed. Thus, this paper attempts to explore the factors that might have contributed to oil prices and analyze them using the fuzzy preference ranking organization method for enrichment evaluation (PROMETHEE) method. In particular, the objective of this study is to rank the influential factors affecting oil prices with the consideration of a few criteria. The fuzzy PROMETHEE model comprises four criteria and five factors as the input information. A group of four experts are invited to provide an evaluation on scales of 1 to 5 of the importance of factors with respect to criteria. In computation, the evaluations are treated as triangular fuzzy numbers in which the uncertainty in data is dealt with amicably. This approach combines the advantages of transformation equation and nonarithmetic mean of the defuzzification method that transforms triangular fuzzy numbers into crisp numbers. The results indicate that “supply and demand” is the most influential factor in determining oil prices. Comparative analysis and future research directions are also discussed at the end of this paper.

Selection of Electric Vehicles for the Needs of Sustainable Transport under Conditions of Uncertainty—A Comparative Study on Fuzzy MCDA Methods

All over the world, including Poland, authorities are taking steps to increase consumer interest in electric vehicles and sustainable transport as a way to reduce environmental pollution. For this reason, the electric vehicle market is dynamically and constantly developing, more and more modern vehicles are introduced to it, and purchases are often subsidized by the government. The aim of the article is to analyse the A–C segments of the Polish electric vehicle market and to recommend the most attractive vehicle from the perspective of sustainable transport. The aim of the research was achieved with the use of three multi-criteria decision aid (MCDA) methods, which deal well with the uncertainty and imprecision of data that occur in the case of many different parameters of electric vehicles. In particular, the following methods were used: the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), the fuzzy simple additive weighting (SAW) method, and the new easy approach to fuzzy preference ranking organization method for enrichment evaluation II (NEAT F-PROMETHEE II). Electric vehicle rankings obtained using each method were compared and verified by stochastic analysis. The conducted analyses and comparisons allowed us to identify the most interesting electric vehicles, which currently appear to be the Volkswagen ID.3 Pro S and Nissan LEAF e+.

Concept selection with hesitant fuzzy ANP-PROMETHEE II

ABSTRACT An integrated approach is given for the assessment and ranking of concepts as part of the new product development (NPD) process. Selection of the best concept in NPD involves multiple criteria decision-making (MCDM) since there are numerous possibly competing criteria to consider. Here, “hesitant fuzzy Analytic Network Process” (H-F-ANP) and “hesitant fuzzy Preference Ranking Organization Method for Enriching Evaluations II” (H-F-PROMETHEE II) are integrated to assess the concepts and determine the best. In the hesitant fuzzy ANP-PROMETHEE II (H-F-ANP-PROMETHEE II), H-F-ANP is applied to calculate criteria weights, taking into consideration inner and outer dependence and feedback interactions between criteria and H-F-PROMETHEE II is implemented to rank concept alternatives, employing the calculated criteria weights. A demonstrative example is given where five concepts are evaluated by three decision makers (DMs) to exhibit the applicability. For comparison, H-F-ANP-VIKOR is utilized.

A comparison study of fuzzy-based multiple-criteria decision-making methods to evaluating green concept alternatives in a new product development environment

PurposeIn this paper, the four popular multiple-criteria decision-making (MCDM) methods in fuzzy environment are utilized to reflect the vagueness and uncertainty on the judgments of decision-makers (DMs), because the crisp pairwise comparison in these conventional MCDM methods seems to be insufficient and imprecise to capture the right judgments of DMs. Of these methods, as Fuzzy analytic hierarchy process (F-AHP) is used to calculate criteria weights, the other methods; Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (F-TOPSIS), Fuzzy Grey relational analysis (F-GRA) and Fuzzy Preference Ranking Organization METhod for Enrichment of Evaluations (F- PROMETHEE II) are used to rank alternatives in the three different ways for a comparative study.Design/methodology/approachThe demand for green products has dramatically increased because the importance and public awareness of the preservation of natural environment was taken into consideration much more in the last two decades. As a result of this, especially manufacturing companies have been forced to design more green products, resulting in a problem of how they incorporate environmental issues into their design and evaluate concept options. The need for the practical decision-making tools to address this problem is rapidly evolving since the problem turns into an MCDM problem in the presence of a set of green concept alternatives and criteria.FindingsThe incorporation of fuzzy set theory into these methods is discussed on a real-life case study, and a comparative analysis is done by using its numerical results in which the three fuzzy-based methods reveal the same outcomes (or rankings), while F-GRA requires less computational steps. Moreover, more detailed analyses on the numerical results of the case study are completed on the normalization methods, distance metrics, aggregation functions, defuzzification methods and other issues.Research limitations/implicationsThe designing and manufacturing environmental-friendly products in a product design process has been a vital issue for many companies which take care of reflecting environmental issues into their product design and meeting standards of recent green guidelines. These companies have utilized these guidelines by following special procedures at the design phase. Along the design process consisting of various steps, the environmental issues have been considered an important factor in the end-of-life of products since it can reduce the impact on the nature. In the stage of developing a new product with the aim of environmental-friendly design, the green thinking should be incorporated as early as possible in the process.Practical implicationsThe case study was inspired from the previous work of the author, which was realized in a hot runner systems manufacturer, used in injection molding systems in a Canada. In a new product development process, the back- and front-ends of development efforts mainly determine the following criteria: cost, risk, quality and green used in this paper. The case study showed that the three fuzzy MCDM methods come to the same ranking outcomes. F-GRA has a better time complexity compared to the other two methods and uses a smaller number of computational steps. Moreover, a comparative analysis of the three F-MCDM methods; F-PROMETHEE II, F-TOPSIS and F-GRA used in ranking for green concept alternatives using the numerical results of the case study. For the case study; as seen in table 20, the three F-MCDM methods produced the numerical results on the rankings of the green concept alternatives as follows; {Concept A-Concept C–Concept B–Concept D}.Social implicationsInclusion of environmental-related criteria into concept selection problem has been gaining increasing importance in the last decade. Therefore, to facilitate necessary calculations in applying each method especially with its fuzzy extension, it can be developed a knowledge-based (KB) or an expert system (ES) to help the DMs make the required calculations of each method, and interpret its results with detailed analysis.Originality/valueThe objective of the research was to propose a F-AHP based F-MCDM approach to green concept selection problem through F-PROMETHEE II, F-TOPSIS and F-GRA methods. As the F-AHP is used to weight evaluation criteria, the other methods are respectively used for ranking the concept alternatives and determine the best concept alternative.