Preference Selection Index Research Articles

Grey preferences selection index with trimmed group preference for evaluating university dormitory renovation design.

Renovating old buildings is a strategic approach to resource optimization and sustainability. However, dormitory design decisions are often made by a limited panel of experts, which risks excluding student preferences-key to the design process. Expert assumptions about student needs can lead to biased outcomes, and attempts to gather student input may suffer from respondent bias and inconsistent engagement, introducing data noise. This paper presents the Grey Trimmed Preferences Selection Index (GTPSI) Multi-Criteria Decision Making (MCDM) method, an improved preference selection index MCDM method that integrates expert weights and refines student feedback by filtering outliers, using interval grey numbers to address uncertainty. Applied to dormitory renovation proposals, GTPSI provides a stable ranking framework, as confirmed by whitening sensitivity analysis, ensuring consistency across grey bounds. To further validate the selected alternative, results are cross-verified with TOPSIS and ELECTRE-III, reinforcing GTPSI's robustness in aligning renovation designs with actual student needs. The GTPSI method thus offers a comprehensive solution for integrating user-centered preferences into expert evaluations, promoting designs that better meet stakeholder expectations.

A Novel Group Decision Making Model to Compare Online Shopping Platforms

Over the years, E-commerce industry has been witnessing a phenomenal growth, thanks to rapid technological advancement in Industry 4.0. There has been a standout surge in the use of various online shopping platforms (OSP) for daily use. The recent pandemic has accelerated the growth trajectory and made a transformational change in the digital commerce landscape. As a result, there has been a proliferation of OSPs in the competitive domain. It is therefore pertinent to address the questions: How do the customers select their favorite OSP? To what extent the OSPs differ based on consumers’ preferences? The present work addresses these questions by proposing a novel group decision making framework. The ongoing study provides several innovative extensions of multi criteria decision making models like Borda count, criteria importance assessment (CIMAS), modified preference selection index (MPSI), and root assessment method (RAM). In this paper, the researchers provide a novel use of the Borda count method, integrated with CIMAS for determining criteria weights utilizing ranking of the criteria. Further, a novel extension of MPSI and RAM has been made with multiple normalizations. In this paper, the authors demonstrate a rare combination of vector and non-linear normalization using the Heron mean. The present paper derives the final criteria weights by combining Borda count, CIMAS and multi-normalization based MPSI (MNMPSI) using Bayesian logic. The criteria are selected based on Uses and Gratification theory (UGT). The findings reveal that interactive app interface and features (C16), user-friendly interface and search (C13), convenience in shopping (C14), product availability and variety (C12) and discounts and offers (C8) exert significant influence in selecting the OSP. Further, it is observed that Flipkart (A2) and Amazon (A1) are the top performers in the eyes of the users. The stability and reliability of the proposed methodology are examined by conducting a sensitivity analysis and comparing with several other models. The robustness of the proposed methodology and practical relevance of the findings of the present work shall provide notable impetus to the analysts and strategic decision-makers.

The Application of Integrated Neutrosophic CRITIC and Preference Selection Index Methods for Multi Criteria Decision Making Over the Implementation of An Integrated Fish Farming System

This paper presents the application of integrated Neutrosophic CRITIC (The Criteria Importance Through Inter-criteria Correlation) and PSI (Preference Selection Index) methods using by the linguistic variables are converted to Neutrosophic set, which is an effective decision making model in a real life environment. This integrated methods is used to rank the 5 alternatives (Labeo rohita , Cirrhina mrigala , Catfishes, Catla catla, Murrels or Snakeheads) and 6 main criteria( Azolla with Duck Cum-Fish Culture, Paddy -Cum -Fish Culture, Duck- Cum -Fish farming, Fish - Cum -Poultry farming, Fish- cum Pig Culture, Cattle -Fish integrated farming system) for the growth of different varieties of fish by using Neutrosophic Environment.

PSI-SAW and PSI-MARCOS Hybrid MCDM Methods

This paper presents a study on the hybridization of Multi-Criteria Decision-Making (MCDM) methods: Preference Selection Index (PSI), Simple Additive Weighting (SAW), and Measurement Alternatives and Ranking according to COmpromise Solution (MARCOS). The hybridization was conducted between the PSI and the other two methods, resulting in new methods, namely PSI-SAW and PSI-MARCOS. For each specific problem, applying these two hybrid methods to rank alternatives among the available options produces three different sets of rankings: one created by PSI, one by the hybrid PSI-SAW, and one by the hybrid PSI-MARCOS. The accuracy of the proposed models was tested in three different cases. The test results show that both proposed models exhibit high accuracy. This study provides users with highly accurate and useful methods for MCDM.

Implementing Preference Selection Index for Optimal Employee Ranking in Organizational Decision-Making

The rapid development of information technology has affected various aspects of life, including in the world of work. This research aims to apply the Preference Selection Index (PSI) method in determining the best employees at Bina Karya Utama Company. The assessment is based on four main criteria: Attendance, Tardiness, Overtime, and Length of Service. Data is obtained through observation and interviews, then processed using the PSI method which involves the normalization process and the calculation of preference values. The results showed that employees with alternative code A8 obtained the highest score, followed by A5 and A9. The PSI method proved to be effective in helping companies make objective and fair decisions, as well as motivating employees to improve their performance. This research concludes that a PSI-based decision support system can improve transparency and fairness in employee evaluation at Bina Karya Utama Company.

Blockchain technology-driven supply chain financing for Ghana’s SMEs: An evaluation of the dilemmas to adoption using a picture fuzzy preference selection index technique

Small and medium-sized enterprises (SMEs) have been urged to embrace blockchain technology-driven supply chain financing (BcTSCF) by the Ghana Enterprises Agency (GEA) and the Association of Ghana Industries (AGI); however, a substantial number have not responded favourably. With the utilization of purposive non-probability sampling approach, data was collected using questionnaires from 39 respondents (owners/managers of SMEs with BcTSCF experience as well as academics with BcTSCF expertise). Adopting the Technology-Organization-Environment framework, a list of drawbacks has been prioritized using a novel multi-criteria decision-making method called the Picture Fuzzy Preference Selection Index. This method involved the computation of total preference values, creation of picture fuzzy decision matrix, ranking and the selection of the drawback with the highest value, thus the prioritization of a set of determinants serving as drawbacks to adopting BcTSCF. The top five drawbacks to BcTSCF adoption are the availability of blockchain infrastructure, inadequate regulatory framework, lack of technical expertise, compatibility concerns, and insufficient government support. This study provides valuable input to industry practitioners, which might help the government and other stakeholders make corresponding policy arrangements. The original contribution of this study is the utilization of a novel picture fuzzy preference selection and the TOE framework to prioritize BcTSCF drawbacks.

Sustainability evaluation and life cycle assessment of concretes including pozzolanic by-products and alkali-activated binders

The construction industry's top priorities for achieving a sustainable future include minimizing the use of non-renewable resources, managing waste generation, and reducing related gas emissions. This fact emphasizes the need for using natural materials such as pozzolan as a substitute or supplemental component in the production of Portland cement concrete. This study presents a review of evaluating the sustainability performance and life cycle assessment of pozzolan-based concrete compared with ordinary Portland cement concrete using the preference selection index approach and evaluating CO2 gas emissions during material life cycle stages. Furthermore, the durability and environmental effects of using pozzolan in concrete were comprehensively investigated. The advantages of using Pozzolans-based concrete in the development of sustainability aspects have been precisely predicted through the application of the PSI method, which depends on several criteria based on the properties of different cement types. It was discovered that substituting pozzolanic material for a portion of Portland cement when producing concrete significantly reduces the environmental effects (i.e., energy use and carbon dioxide emissions).

Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

Application of Preference Selection Index and TOPSIS in Product Aspect Extraction and Ranking

Selection of optimal strategy for managing decentralized solar PV systems considering uncertain weather conditions

Solar power is a promising source of energy that is environmentally friendly, sustainable, and renewable. Solar photovoltaic (PV) panels are the most common and mature technology used to harness solar energy. Unfortunately, these panels are prone to dust accumulation, which can have a significant impact on their efficiency. To maintain their effectiveness, solar photovoltaics s must be cleaned regularly. Eight main techniques are used to clean solar panels: natural, manual, mechanical, robotic, drone, coating, electrical, and acoustic. This study aims to identify the best cleaning method using multiple criteria decision-making (MCDM) techniques. Using the Analytical Hierarchy Process (AHP), Quality Function Deployment (QFD), Fuzzy Technique for Order of Preference by Similarities to Ideal Solution (FTOPSIS), and Preference Selection Index (PSI), this research evaluates all eight cleaning methods based on several criteria that are categorized under cost, performance, resource requirement, and safety in Abu Dhabi. The data are collected from surveys completed by experts in solar and sustainable energy. The AHP, QFD, and PSI results identified natural, manual, and surface coating as the best and most effective cleaning methods. Natural cleaning involves using rainwater primarily to remove dirt and dust; manual cleaning requires cleaning agents and wiping clothes; and surface coatings involve applying a layer of hydrophobic material to the panels to repel dust. Identifying the most effective cleaning method for dust removal from solar panels can ensure optimal efficiency recovery at minimal costs and resources.

Impact of smart sustainable value stream mapping – Fuzzy PSI decision-making framework

Value Stream Mapping (VSM) is a Lean method for waste reduction, crucial across industries. In the era of the fourth industrial revolution, Small and Medium-sized Enterprises (SMEs) are adopting smart, sustainable practices. The focus has shifted from traditional VSM to Smart Sustainable VSM (SS-VSM). This study introduces an SS-VSM framework, integrating lean, sustainable, and smart techniques. A fuzzy-based Preference Selection Index (FPSI) method selects the optimal SS-VSM strategy, considering environmental, economic, and social factors. Sensitivity analysis tests the approach's robustness. The results offer a conceptual framework for implementing and managing VSM, aiding SMEs in enhancing performance through SS-VSM practices.

Comparison of MCDM methods effectiveness in the selection of plastic injection molding machines

In each specific problem of finding the best solution among many available options, where each option has multiple criteria, multi criteria decision making methods are considered equally effective when they converge to the same optimal solution. Proximity Indexed Value, Preference Selection Index, Faire Un Choix Adéquat (in French), and Collaborative Unbiased Rank List Integration are four Multi Criteria Decision Making methods with very different characteristics. All these four methods have been used a lot in recent times. The effectiveness of these four methods have been confirmed to be comparable to other multi criteria decision making methods in many applications. However, the comparison of these four methods with each other has never been performed in any studies. This article is performed to fill that gap. These four methods have been used to find to the best option among five types of plastic injection molding machine. Ten criteria have been chosen to describe each alternative. Two different methods that have been used to calculate the weights for the criteria are the MEAN weight method and the CRiteria Importance Through Intercriteria Correlation weight method. Different scenarios have been created to compare the effectiveness of these four methods. The results have shown that the four multi criteria decision making methods mentioned above are equally effective in the selection of plastic injection molding machines. Among the five types of plastic injection molding machines, namely JSW J350EII-SPA ANBE-002-02, Meiki M-200B-SJ, Meiki M-350C-DF-SJ, JSW J350E II, and JSW J550E-C5, the JSW J550E-C5 is the best type.

A novel fuzzy multi-criteria decision-making for enhancing the management of medical waste generated during the coronavirus pandemic

The coronavirus pandemic significantly increased the use of essential medical supplies, resulting in a surge in medical waste generation. This surge has spurred extensive research into sustainable disposal methods for safe and environmentally responsible medical equipment management. Addressing this multifaceted issue falls within the domain of multi-criteria decision-making. This study presents a comprehensive framework for selecting optimal medical waste treatment methods, considering economic, technological, environmental, and social factors. This is the first study to address the problem of selecting a medical waste disposal technology using the Fuzzy Dombi Bonferroni. The mean operator to combine expert opinions, the fuzzy preference selection index method to evaluate the criteria and the fuzzy compromise ranking of alternatives from distance to ideal solution method to rank the alternatives. According to the weightings, the social dimension holds the highest significance at 0.3217. Disinfection efficiency ranks as the most critical criterion, weighing in at 0.0823. The autoclave is rated as the top disposal technique, with a utility function value of 5.4579. Sensitivity analyses ensured the stability and reliability of the models. The adaptability of the applied model to sustainable practices such as energy conversion, material recycling, and resource recovery represents an essential aspect of policymaking in waste management. This assessment can guide policy formulation or improvement processes for waste disposal.

Innovation in Financial Health Assessment: Applying MCDM Techniques to Banks in Vietnam

Abstract Assessing the financial health of banks is crucial to ensure the stability of the financial system. This is not only a way to safeguard the interests of customers and shareholders but also to prevent inherent risks and ensure reliability in banking operations. Thus, evaluating financial health plays a crucial role in maintaining stability and sustainable development in the banking sector. This study employed three methods: RAM (Root Assessment Method), PSI (Preference Selection Index), and SRP (Simple Ranking Process), to assess the financial health of twenty-eight banks in Vietnam. Capital adequacy rating, asset quality rating, management rating, earnings rating, liquidity rating, and sensitivity to market risk rating are the six criteria used to characterize each bank. The evaluation results using these three methods were compared with each other and with the evaluation using the CAMELS rating system. The study identified banks with good financial health and those with weak financial health. The notable point is that the results of ranking financial health of banks using three methods: RAM, PSI, and SRP, and according to the CAMELS system are quite similar. These findings are vital in providing valuable information for managers and investors, aiding them in making informed decisions regarding investment, risk management, and development strategies based on empirical data.

Implementation of the Preference Selection Index (PSI) Method in Determining the Best Coffee Shop

The decision support system can be claimed to be a personal computer capable of running data into information so that when taking a semi-structured or problem-specific decision, A coffee shop is a place that prioritizes the sale of coffee with a variety of brewing methods, ranging from cold brew, percolator, Turkish coffee, automatic drip, moka pot, tubruk, Arabica, and many more. In this study, the authors used the PSI (preference selection index) method so that the selection of the best coffee shop was carried out by making a decision matrix, normalizing the decision matrix, calculating the mean value of normalized data, determining the variation of preferences, determining storage in preference values, determining the weight of the criteria, and calculating the PSI value so as to find the best alternative. The criteria used in the selection of coffee shops are five: food, drink, service, entertainment, and parking. Then the final result of the best alternative value is A6 as the best coffee shop in Tanjung Morawa, with a result of 3.702 using the PSI method

An ADAM-based approach to unveiling entrepreneurial ecosystems in selected European countries

PurposeThis study aims to integrate the resource-based view (RBV) with other theories that consider external factors necessary to respond successfully to dynamic and uncertain entrepreneurial business conditions.Design/methodology/approachThe paper introduces an multi-criteria decision-making (MCDM) approach, utilizing the axial-distance-based aggregated measurement (ADAM) method with weights determined by the preference selection index (PSI) method, to rank eight European countries based on the Global Entrepreneurship Monitor (GEM) data. Additionally, the paper extends the existing entrepreneurial ecosystem taxonomy (EET), offering an additional classification.FindingsThe performed analysis emphasizes the importance and necessity of involving different dimensions of EE in assessing the countries' entrepreneurship performance, which facilitates creating adequate policy measures.Research limitations/implicationsThe crucial limitations are assessments based only on the GEM data from a particular period, possibly leading to a certain bias. Future research should involve data from various resources to increase the results' reliability.Originality/valueThe ranking results and country classification obtained using the ADAM-based approach and two distinct taxonomies served as the basis for formulating tailored policy recommendations, aiming to formulate tailored policy implications for increasing the number of new entrepreneurs and improving innovativeness, sustainability and internationalization of existing entrepreneurs for each group of countries.

Analyzing the Performances and Efficiencies of European Electric Vehicles in the European Union Market Using Decision Analysis Methods

Concerns about the environment have grown worldwide as carbon emissions, one of the primary drivers of global warming, continue to rise. As the transportation industry is one of the most greenhouse-gas-emitting sectors in the European Union (EU), the need for environmentally friendly solutions has been increasing progressively. In this regard, electric vehicles (EVs) play an important part in mitigating some of the adverse effects of road transportation on human health, the environment, and climate. Therefore, the popularity of electric vehicles in the EU has recently increased significantly because of climate change mitigation efforts. In this paper, we aim to analyze and compare the performance of European EVs with multi-criteria decision-making (MCDM) methods, namely preference selection index (PSI) and axial-distance-based aggregated measurement (ADAM) techniques, which do not need the criteria weights for measurement and a novel three-dimensional evaluation technique. The results of both MCDM methods reveal that the EVs of the Tesla Inc. and Geely Group models are found to be the best-performing vehicles. We also investigate the efficiency of the European EV market by the super-efficiency data envelopment analysis (DEA) technique, which enables us to evaluate among efficient units. The efficiency scores indicate that five EVs from three different manufacturer groups obtain efficiency levels. To the best of our knowledge, European EV market evaluation by integrated and comparative analysis has not been encountered for the EU on a group basis. The suggested MCDM and DEA techniques can benefit stakeholders while assessing the European EV market.

Modelling and optimization of hybrid Kevlar/glass fabric reinforced polymer composites for low-velocity impact resistant applications

Kevlar and glass-based fabric-reinforced polymer composites possess excellent impact resistance, corrosion and high specific strength properties which makes them suitable candidature for the fabrication of industrial helmets, riot shields, automobile parts etc. The polymer composite components are prone to impact failure, which restricts their wide commercial usage. In this work, to enhance the impact damage resistance of polymer composite laminates, a hybridized material model consisting of glass and Kevlar fabric was employed. The hybridization of Kevlar ‘K’ with glass ‘G’ fabric [K/K/G/G/K] results in a cost reduction of 32 % without any significant variation in performance when compared to non-hybrid Kevlar fabric-based composite laminate [K/K/K/K/K]. In comparison to plain glass fabric laminates [G/G/G/G/G], the [K/K/G/G/K] configuration exhibits a significant 16.68 % improvement in energy absorption. This improvement is rooted in a parametric study and optimization process employing the Preference Selection Index (PSI) technique to determine the optimum stacking sequence of fabrics within the composite laminate. In addition, it evaluates the projectile limit velocity necessary to prevent failure and assesses the total deformation for an optimized stacked [K/K/G/G/K] composite, taking into account various projectile shapes conforming to low-velocity impact applications. The results of this study provide valuable insights into the properties and capabilities of the hybrid composite laminate and may pave the way for the development of more effective materials for impact protection in various applications.

FDM process parameter selection by hybrid MCDM approach for flexural and compression strength maximization

Fused deposition modelling (FDM) is one of the mostly used additive technologies, due to its ability to produce complex parts with good mechanical properties. The selection of FDM process parameters is crucial to achieve good mechanical properties of the manufactured parts. Therefore, in this paper, a hybrid multi-criteria decision-making (MCDM) approach based on Preference Selection Index (PSI) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is proposed for the selection of optimal process parameters in FDM printing of polylactic acid (PLA) parts. Printing temperature, layer thickness and raster angle were considered as input process parameters. In order to prove the effectiveness of the proposed hybrid PSI – TOPSIS method, the obtained results were compared with the results obtained with different MCDM methods. The obtained best option of process parameters was confirmed by other MCDM methods. The optimal combination of process parameters to achieve the maximal flexural strength, maximal flexural modulus and maximal compressive strength is selected using the hybrid PSI-TOPSIS method. The results show that the hybrid PSI-TOPSIS approach could be used for optimisation process parameters for any machining process.

Optimum selection of nano-Al2O3, nano-SiO2, and nano-ZrO2 particulate fillers on physical, mechanical, and wear assessment of Al6061 nanocomposite using preference selection index

The present study investigated the role of three important nano-sized fillers such as nano-SiO2, nano-ZrO2, and nano-Al2O3 on mechanical and wear assessment of Al6061 alloy matrix composite and suggested the optimum compositions for the best performance for Al6067 alloy matrix composites. Preference selection index as optimization tool was used and attributes taken for optimizations were void content, hardness, tensile strength, flexural strength, fracture toughness, impact strength, specific wear rate at normal load of 25 and 55 N load, sliding velocities of 0.5 and 2 m/s, sliding distance of 500 and 2000 m, respectively. In general, all three nanofillers enhanced the mechanical and wear properties of Al6061 alloy matrix composite. In the steady state wear study, on increasing normal load from 25 to 55 N, the specific wear rate of Al6061, Al6061-3Al2O3, Al6061-3SiO2, and Al6061-3ZrO2 was increased by 58.7%, 33.2%, 15.4%, and 26.14%, respectively. On increasing sliding speed from 0.5 to 2 m/sec, the specific wear rate of Al6061, Al6061-3Al2O3, Al6061-3SiO2, and Al6061-3ZrO2 was increased by 27%, 13%, 6%, and 12%, respectively. On increasing sliding distance from 500 to 2000 m, the specific wear rate of Al6061, Al6061-3Al2O3, Al6061-3SiO2, and Al6061-3ZrO2 was increased by 41%, 36%, 9%, and 21%, respectively. Al6061-3SiO2 indicated the best composition in terms of mechanical and wear performance followed by Al6061-3Al2O3 and Al6061-3ZrO2. Hence, nano-SiO2 can be suggested as the best filler to enhance the mechanical and wear performance of Al6061-based nanocomposite. Therefore, nano-SiO2 reinforced Al6061 composite is indicated as the best suitable composite material for a large number of components in structural, automobile, marine, and aerospace industries.

Fermatean fuzzy framework based on preference selection index and combined compromise solution methods for green supplier selection in textile industry

This work aims to enhance the efficiency and dependability of the green supplier selection process by developing a decision support tool. Thereby, a novel fuzzy group multi-criteria decision-making approach based on the Preference Selection Index (PSI) and Combined Compromise Solution (CoCoSo) methods under the Fermatean Fuzzy (FF) environment for green supplier selection is proposed. The primary novelties and contributions are: (i) for the first time, the FF-PSI model is developed to extract the priority weight values of criteria, (ii) an improved FF-CoCoSo methodology is proposed, and (ii) the FF-PSI-CoCoSo framework is offered for resolving challenging real-life problems. To present the framework’s practicality, a real-case study evaluating six suppliers concerning nine drives under economics, environmental, and technological aspects in a textile company in Turkey is conducted. The results indicate that “green warehousing” is the most essential driver for green suppliers, followed by “recycling” and “damaged product ratio.” A detailed sensitivity and comparison check is further conducted to display the solidity and effectiveness of the proposed framework. The suggested framework offers a reliable solution for green supplier selection and a precise sustainable rating of suppliers. Moreover, it can be addressed to solve various challenging real-world problems.