Multiple Attribute Decision Making Methods Research Articles

Fuzzy-AHP based optimal RSU deployment (Fuzzy-AHP-ORD) approach using road and traffic analysis in VANET

Vehicular Ad Hoc Network (VANET) is a proven technology in Intelligent Transportation Systems (ITS) that provides various safety and non-safety applications. The Roadside Unit (RSU) is one of the essential components of the VANET, which allows vehicles to disseminate and exchange safety and infotainment information in a broader range. RSUs are also valuable for offloading the internet data traffic of vehicular users from cellular networks. However, due to high deployment and maintenance costs and security overhead, it is essential to optimally deploy these RSUs and place them in prominent places to increase the overall network coverage and efficiency. The existing deployment strategies either analyzed the road network on one or a few topological parameters or vehicular traffic parameters and considered equal or random weights for each parameter. None of them deal with the uncertainty of parameter weightage. Thus, this paper proposes a Fuzzy-Analytic Hierarchy Process-based optimal RSU deployment (Fuzzy-AHP-ORD) approach to finding the highly influential intersection nodes for RSU deployment. The Fuzzy-AHP-ORD considers different static and dynamic parameters concerning road topology and the vehicle’s traffic behavior. The weights of the parameter are calculated through Fuzzy-AHP, then the various intersection nodes of the road networks are ranked using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) based MADM (Multiple Attribute Decision Making) method. The simulation results reveal the effectiveness of the proposed method in comparison to other benchmark methods in terms of coverage ratio, connection time, packet delivery ratio, and delay.

GHF-COPRAS Multiple Attribute Decision-Making Method Based on Cumulative Prospect Theory and Its Application to Enterprise Digital Asset Valuation

With the rapid development of the economy, data have become a new production factor and strategic asset, enhancing efficiency and energy for technological innovation and industrial upgrading in enterprises. The evaluation of enterprise digital asset value (EDAV) is a typical multi-attribute decision-making (MADM) problem. Generalized hesitant fuzzy numbers (GHFNs) can better express the uncertainty and fuzziness of evaluation indexes, thus finding wide applications in MADM problems. In this paper, we first propose the Kullback–Leibler (K-L) divergence distance of GHFNs and prove its mathematical properties. Second, recognizing that decision-makers often have finite rationality in practical problems, we combine the cumulative prospect theory (CPT) with the Complex Proportional Assessment (COPRAS) method to propose the GHF-CPT-COPRAS model for solving MADM problems. Simultaneously, we extend the distance correlation-based Criteria Importance Through Intercriteria Correlation (D-CRITIC) method to the GHF environment to rationally calculate the weights of attributes in the EDAV evaluation problem. Finally, we apply the proposed GHF-CPT-COPRAS model to the EDAV evaluation problem and compare it with existing GHF decision-making methods to verify its effectiveness and feasibility. This study provides an important reference for addressing the EDAV assessment problem within an uncertain fuzzy environment and extends its application methods in the decision-making field.

A Review on Picture Fuzzy Aggregation Operators

Multiple Attribute Decision Making (MADM) is a fundamental concept in the scope of the decision sciences, serving as a structured method for evaluating and selecting the most appropriate alternative option from a pool of available alternatives. MADM methods have gained prominence over the years and are frequently applied in real-world scenarios. Nevertheless, decision-making in practical situations often involves information that is imprecise and uncertain, especially conflicting criteria or attributes. Therefore, Picture Fuzzy Sets (PFSs) and Aggregation Operators (AOs) have proven invaluable in effectively addressing decision challenges characterized by impression and uncertainty. During the past few years, various Picture Fuzzy Aggregation Operators (PFAOs) have been suggested and established but have not been thoroughly reviewed. The primary highlight of this research is to analyse as well as review the development and proposals surrounding PFAOs and their diverse applications within the decision-making paradigm. Regarding this, a review of 140 published articles from 2017 to 2022 appeared in 48 high-ranking journals cited from the "Scopus" and "Web of Science" databases. Other than that, all these articles have been classified by the nationalities of authors, publication year, published journal, research area, operators and methods. The findings of this study discovered that PFAOs have been increasingly applied for supporting decisions due to their frequent implications and applications in different managerial domains, either profit or non-profit organizations. This literature survey's significant contribution provides a platform for researchers to identify future dimensions of works as improvements for decision-making in picture fuzzy environments while also promoting future application of the approaches.

Decision Science-Driven Assessment of Ti Alloys for Aircraft Landing Gear Beams

Titanium alloys, with their low density, exceptional mechanical properties, and outstanding corrosion resistance, play a vital role in various aerospace applications. Our decision science-driven assessment focused on metastable β, near-β, α + β, and near-α Ti alloys for landing gear applications, integrating multiple-attribute decision-making (MADM) methods, principal component analysis (PCA), and hierarchical clustering (HC) is based on current literature. The ranks of the alloys evaluated by diverse MADM methods were consistent. The methodology identifies five top-ranked Ti alloys assists and verifies the guidelines for alloy design. The top-ranked alloy, Ti1300-BM-nano-α (alloy chemistry: Ti-5Al-4V-4Mo-3Zr-4Cr, solution treatment: 800 °C for 1 h followed by air cooling—solution treated below β transus, and aging: 500 °C for 4 h followed by air cooling), stands out with a percentage elongation (%EL) ~3.3 times greater than the benchmark or goal (density, d = ~4.6 g/cm3; yield strength YS = ~1250 MPa; %El = ~5), while maintaining similar density and yield strength. The analyses underline that metastable β Ti alloys comprising globular primary α + trans β matrix coupled with α precipitates in trans β are the base optimal microstructure to fine-tune using thermomechanical processing for aircraft landing gear applications.

Many-objective optimization of operation strategy for district cooling system based on the per-unit value form

The operational strategy of the district cooling system (DCS) with an ice storage system must be carefully optimized for many objectives to achieve optimal operation. However, owing to the fact that the variables of DCS are in different orders of magnitude and there is a vast gap between the variables, it is not easy to perform arithmetic. In this paper, the DCS model based on the per-unit value form is formulated to develop a many-objectives operational strategy that is solved by the linear weighting method. And a multiple-attribute decision-making method is taken to get the optimal operating strategy. The simulation results are discussed for an existing cooling system with 619-node and 614-branch to illustrate that the model based on the per-unit value form can obtain satisfactory optimization results and a significant reduction in time consumption.

An intelligent decision methodology for triangular Pythagorean fuzzy MADM and applications to college English teaching quality evaluation

Abstract The development of contemporary college English teaching methods and practice should respect the subjective will of college students; fully tap their wisdom and potential; constantly innovate college English teaching methods, teaching practice activities, classroom teaching, and teaching evaluation models; create a good teaching atmosphere; and establish the concept of developmental evaluation. Through the innovation of teaching methods, college students’ actual language communication ability is cultivated, the teaching objectives and teaching methods of college English reading are innovated, and interactive, body language, role play, situational teaching, communicative teaching, and other teaching methods are used. The College English teaching quality evaluation is a classical multiple attribute decision making (MADM). In this article, the triangular Pythagorean fuzzy sets (TPFSs) are introduced, and the MADM problem is investigated under TPFSs. Based on the traditional dual generalized weighted Bonferroni mean operator and power average operator, the triangular Pythagorean fuzzy dual generalized power Bonferroni mean (TPFDGPBM) operator is proposed. Accordingly, the TPFDGPBM operator is employed to construct the triangular Pythagorean fuzzy MADM method. Finally, a numerical example for College English teaching quality evaluation is constructed to verify the TPFDGPBM technique.

Research on innovation and entrepreneurship education environment evaluation in universities under the background of “Double Innovation” with interval-valued intuitionistic fuzzy information

As China’s economic and social development enters a new stage, the role of innovation and entrepreneurship is becoming increasingly prominent, and its importance is also being emphasized. As the main force for future employment and national economic construction, college students naturally become the new force for innovation and entrepreneurship. Therefore, it is imperative for universities to carry out in-depth innovation and entrepreneurship education (IEE) for college students. Then, with the continuous development of social development needs and the professional growth needs of college students, the “innovation and entrepreneurship” education for college students should also be adjusted in a timely manner in terms of educational concepts, models, and methods. The IEE environment evaluation in universities under the background of “ Double Innovation” is looked as multiple attribute decision-making (MADM). In this paper, the information entropy model is employed to calculate the objective weight of the evaluation attribute. Then, interval-valued intuitionistic fuzzy Combined Compromise Solution (IVIF-CoCoSo) is built based on the Hamming distance and Euclid distance to cope with MADM under interval-valued intuitionistic fuzzy sets (IVIFSs). The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation”. Finally, the IVIF-CoCoSo approach is compared with existing methods to verify the effectiveness of IVIF-CoCoSo algorithm. The main contributions of this constructed paper are: (1) the IVIF-CoCoSo method is built based on the Hamming distance and Euclid distance. (2) the information entropy model is employed to calculate the objective weight of the evaluation attribute. (3) The new MADM method is proposed for IEE environment evaluation in universities under the background of “ Double Innovation” based on IVIF-CoCoSo. (4) The IVIF-CoCoSo model is compared with existing methods to verify the effectiveness of the IVIF-CoCoSo algorithm.

Magnet selection for permanent magnet synchronous machines by decision science

This work investigates magnet selection for permanent magnet synchronous machines (PMSMs) by decision science. 20 multiple-attribute decision-making (MADM) methods ranked a list of 45 industrial-grade permanent magnets based on eight property requirements―high energy density (BHMAX), high coercivity (HC), high magnetic remanence (BR), high electrical resistivity (ρ), high Curie temperature (TC), high maximum service temperature (TS), low linear thermal expansion coefficient (α), and low density (d)―targeted to selecting magnets for PMSMs. The Shannon's entropy method evaluated the objective attribute weights of ρ, BHMAX, and HC, as ∼46%, ∼24%, and ∼23%, respectively, and the rest—BR, TC, TS, d, and α―combined as ∼7%; equal weights (∼12.5% each) to all the attributes were the subjective weights. Principal component analysis (PCA) consolidated ranks from various MADM methods, and hierarchical clustering (HC) grouped magnets based on their similarities. For PMSMs in applications where ρ, BHMAX, and HC, are more critical compared to the rest of the properties, the eight top-ranked magnets are N35 (Neodymium® 35), N35H (Neodymium® 35H), N30 (Neodymium® 30), N30H (Neodymium® 30H), N27H (Neodymium® 27H), N27 (Neodymium® 27), F5–S (Ferrite 5, Sintered Iron Strontium Oxide), and F3–S (Ferrite 3, Sintered Iron Barium Oxide) in that order. On the other hand, for PMSMs in applications where all the attributes are equally important, the eight top-ranked magnets are N35 (Neodymium® 35), N35H (Neodymium® 35H), N30H (Neodymium® 30H), N30 (Neodymium® 30), N27H (Neodymium® 27H), N27 (Neodymium® 27), CS4 (Samarium Cobalt 4), and F5–S (Ferrite 5, Sintered Iron Strontium Oxide) in that order. The consolidated ranks of the magnets obtained by assigning objective and subjective attribute weights showed a strong correlation of ∼0.94. This investigation targeted magnet selection for PMSMs from a comprehensive dataset of industrial-grade permanent magnets, utilizing several property requirements, applying both objective and subjective attribute weights identified Neodymium® 35 as the top-ranked magnet.

Research on the economic collaborative development evaluation in the Beijing-Tianjin-Hebei region based on hesitant triangular fuzzy information

Beijing, Tianjin and Hebei are located in the Bohai Rim region of Northeast Asia, China. It is the region with the largest economic scale and strongest economic vitality in northern China. Due to historical development and administrative division, the economic strength of Beijing and Tianjin is strong, while the economic strength of Hebei Province is weak. The economic development of the Beijing-Tianjin-Hebei region is severely uneven. The “Beijing-Tianjin-Hebei Coordinated Development Strategy” is proposed and elevated to a national strategy in this context, aiming to explore the path of coordinated economic development in the Beijing-Tianjin-Hebei region, promote economic cooperation, balance economic differences, and enhance the overall economic strength of the Beijing Tianjin Hebei region through national leadership. The economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is a classical multiple attribute decision making (MADM) problems. Recently, the TODIM and Evaluation based on Distance from Average Solution (EDAS) method has been used to cope with MADM issues. The hesitant triangular fuzzy sets (HTFSs) are used as a tool for characterizing uncertain information during the economic collaborative development evaluation in the Beijing-Tianjin-Hebei region. In this paper, the hesitant triangular fuzzy TODIM-EDAS (HTF-TODIM-EDAS) method is built to solve the MADM under HTFSs. In the end, a numerical case study for economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is given to validate the proposed method. The main contributions of this paper are summarized: (1) the HTF-TODIM-EDAS method is proposed under HTFSs. (2) The MADM method is designed based on the information entropy and HTF-TODIM-EDAS method under HTFSs. (3) A numerical case study for economic collaborative development evaluation in the Beijing-Tianjin-Hebei region is given to validate the proposed method. (4) A comparison between proposed method and existing methods is carried out to check its effectiveness.

Lagrange-enhanced GRA method for probabilistic simplified neutrosophic MADM: Yaozhou porcelain decoration design quality evaluation

Yaozhou ware is located in Huangbao Town, Tongchuan City. It began in the Song Dynasty and flourished in the Tang Dynasty. Its warm and jade like glaze color, exquisite decorative techniques, and rich and varied utensil shapes all give it a very important position in history. The traditional Yaozhou porcelain contains the ancient people’s concept of creation and their attitude towards humans and nature. Each artifact of Yaozhou porcelain contains the designer’s understanding and emotions about the world, establishing a dialogue between people and objects to form a desired lifestyle. The production method of Yaozhou porcelain that has been preserved for thousands of years shares many similarities with modern design ideas. The quality evaluation of Yaozhou porcelain decoration design is viewed as multiple-attribute decision-making (MADM). The grey relational analysis (GRA) is a useful tool to cope with the MADM issue. The probabilistic simplified Neutrosophic set (PSNSs) is easy to characterize uncertain information during the quality evaluation of Yaozhou porcelain decoration design. In this paper, in order to obtain the weight information, an optimization model is constructed to obtain a simple and exact formula which can be employed to derive the attribute weights values based on the Lagrange function and the probabilistic simplified neutrosophic (PSN) grey relational analysis (PSN-GRA) method is originated for MADM to rank the decision alternatives. Finally, a numerical example for quality evaluation of Yaozhou porcelain decoration design is used to verify the practicability of the PSN-GRA method and compares it with other methods.

A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation

. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.

Retracted: Multiple-Attribute Decision-Making Method Based on Normalized Geometric Aggregation Operators of Single-Valued Neutrosophic Hesitant Fuzzy Information

Retracted: Multiple-Attribute Decision-Making Method Based on Normalized Geometric Aggregation Operators of Single-Valued Neutrosophic Hesitant Fuzzy Information

Research on the Correlation between Industry 4.0 and Sustainable Development in the Machine Tool industry – Application of QFD and Fuzzy MADM methods

<p>Starting in 2025, Taiwan will introduce a requirement that listed companies with a paid-in capital of less than 2 billion NTD need to prepare a sustainability report. Most of the actual income scale of Taiwan’s machine tool industry falls within this range. In response to the requirement, the machine tool industry must reduce its impact on the environment and society and strike a balance while increasing economic returns. This is undoubtedly a major challenge for the machine tool industry, which is one of the most internationally competitive industries in Taiwan. If sustainable development (SD) can be successfully implemented, it will set a benchmark for Taiwan’s traditional industries.</p> <p>The machine tool industry has continued to improve its industrial competitiveness in recent years, especially after Germany proposed the concept of Industry 4.0 in 2013, which accelerated the pace of industrial upgrading and transformation. Studies have shown that the adoption of Industry 4.0 technology can effectively enhance the sustainable performance of enterprises. Additionally, in the literature on SD, it has been confirmed that there is a significant positive relationship between corporate social responsibility and profitability.</p> <p>This study will propose a reasonable and feasible model framework to demonstrate the relationship between SD factors and Industry 4.0 technical indicators, aiming to identify the key Industry 4.0 technologies to improve SD. This study uses the Quality Function Deployment (QFD) method as the basic structure and adopts the Fuzzy Delphi Method (FDM) of the Multiple Attribute Decision Making (MADM), the improved Fuzzy Extended Analytic Hierarchy Process (FEAHP), and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for prioritizing performance index factors.</p>

Multi-objective optimization of inconel 718 turning parameters using PCA-MARCOS and MARCOS-Taguchi

Abstract In recent years, multiple-attribute decision-making (MADM) methods have gathered significant interest from researchers across various scientific fields. In the case of mechanical part machining when faced with conflicting objectives, these methods have become essential in the selection of necessary cutting parameters. The integration of these techniques with multi-objective optimization procedures has proven effective in leading to an efficient solution to a multi-attribute decision-making problem. The present study falls within this context. Its primary objective being the determination of the optimal cutting parameters that reduces tool vibration, minimizes surface roughness along withmaximizing material removal rate during the turning of Inconel 718 using a coated carbide cutting tool grade (GC1105). These various objectives have been achieved through combining the principal component analysis (PCA) method to the measurement of alternatives and ranking according to the compromise solution (MARCOS), and MARCOS method coupled with the Taguchi SN ratio. The results obtained demonstrated that both methods generated the same combination of optimal cutting parameters that achieve the aforementioned objectives and represented by a nose radius r = 0.8 mm, a cutting speed Vc = 70 mm min−1, a feed rate f = 0.08 mm rev−1, and a depth of cut ap = 0.3 mm.

An integrated decision-making framework for blended teaching quality evaluation in college English courses based on the double-valued neutrosophic sets

Blended teaching, which combines the advantages of face-to-face teaching and online learning, has become an important breakthrough in the current higher education teaching reform and innovation, and the construction of a blended teaching quality evaluation in college English courses system is of great significance and value to ensure the sustainable development of blended teaching activities. The blended teaching quality evaluation in college English courses is regarded as the defined multiple attribute decision making (MADM). In this paper, the Combined Compromise Solution (CoCoSo) method is constructed for MADM under double-valued neutrosophic sets (DVNSs). Then, the double-valued neutrosophic numbers CoCoSo (DVNN-CoCoSo) method is built for MADM. Finally, a practical numerical example for blended teaching quality evaluation in college English courses is supplied to show the DVNN-CoCoSo method. The main contributions of this constructed paper are: (1) This paper builds the novel MADM method based on CoCoSo decision methods under DVNSs, which extends the classification CoCoSo method. (2) The new MADM method for blended teaching quality evaluation in college English courses based on DVNN-CoCoSo is proposed.

Q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on Schweizer-Skla operations

In order to be able to make a good decision, we need to evaluate the uncertainty information of multiple attributes of different alternatives that obey the normal distribution, and the interrelationship among multiple attributes should be considered in the process of evaluating. This paper aims to propose a new multiple attribute decision-making (MADM) method, which uses a new aggregation operator to evaluate the uncertainty information that obey normal distribution comprehensively. We firstly extended Schweizer-Sklar (SS) t-norm (TN) and t-conorm (TCN) to q-rung orthopair normal fuzzy number (q-RONFN) and defined the Schweizer-Skla operational laws of q-rung orthopair normal fuzzy set (q-RONFs). Secondly, we developed q-rung orthopair normal fuzzy Maclaurin symmetric mean aggregation operators based on SS operations considering that the Maclaurin symmetric mean operator can reflect the interrelationship among multiple input variables. Furthermore, we discussed some desirable properties of the above operators, such as monotonicity, commutativity, and idempotency. Lastly, we proposed a novel MADM method based on developed aggregation operators. A numerical example on enterprise partner selection is given to testify the effectiveness of the developed method. The results of analysis indicated that our proposed aggregation operators have stronger information aggregation ability and are more general and flexible for MADM problems.

A hesitant fuzzy multiple-attribute decision-making method considering rank relation

A decision matrix is typically used to express hesitant information when solving multi-attribute decision problems in an uncertain environment. To further investigate the decision problem, this paper takes the property of matrix rank as the starting point, introduces it into the hesitant fuzzy theory, presents the concept of the rank of the hesitant fuzzy decision matrix and discusses the related properties, and then studies the hesitant fuzzy linear relation, obtaining the attribute reduction method based on the hesitant fuzzy linear relation and applying it to the multi-attribute decision making. It adds to the theoretical understanding of the hesitant fuzzy decision matrix. The aggregation operator first transforms the hesitant fuzzy information into a comprehensive decision matrix, and the row echelon transformation determines the rank of the matrix. Second, the hesitant fuzzy linear relationship is obtained using the rank property. A new hesitant fuzzy matrix is obtained after attribute reduction based on the hesitant fuzzy linear relationship, and the alternatives are sorted using the TOPSIS method. Finally, the effectiveness and superiority of the proposed method are demonstrated through a comparison of actual case analysis and existing methods, and the expected research purpose is met.

Multiple attribute decision making based on novel nonlinear programming model, the distance between score values, and novel score function of interval-valued intuitionistic fuzzy values

In this paper, we propose a novel multiple attribute decision making (MADM) method based on the proposed nonlinear programming (NLP) model, the distance between the score values appeared in the constructed score matrix (SCMX), and the proposed score function (SF) of interval-valued intuitionistic fuzzy values (IVIFVs), where the NLP model is used to get the optimal weights (OWs) of the attributes. Firstly, we propose a novel SF to conquer the shortcomings of the existing SFs of IVIFVs. Then, we use the proposed SF to construct the SCMX from the decision matrix (DM) given by the decision maker (DK). Then, we propose a NLP model to obtain the OWs of the attributes based on the distance between the score values appeared in the constructed SCMX, the interval-valued intuitionistic fuzzy weights (IVIFWs) of the attributes provided by the DK, the concept of deviation variables, and the largest range of the IVIFW of each attribute. Then, we calculate the weighted score (WS) of each alternative based on the obtained SCMX and the obtained OWs of the attributes. Finally, we rank the alternatives according to the WSs of the alternatives. The proposed MADM method can conquer the shortcomings of the existing MADM methods.

Methodology for evaluating the ideological and political education quality of chinese universities based on fuzzy number intuitionistic fuzzy multiple-attribute decision-making

The quality evaluation of Chinese universities ideological and political (IAP) education has gone through the stages of defining tasks, proposing standards and exploring and carrying out, and has completed the stage tasks and accumulated practical experience. To construct the quality evaluation system of IAP education of Chinese universities in the new era, it is necessary to find the quality positioning in the fundamental task of establishing moral education and pay attention to the synergy between the internal and external parts of the quality of IAP education of Chinese universities. The IAP education quality evaluation of Chinese universities are the multiple-attribute decision-making (MADM) issue. In this paper, we extend the geometric Heronian mean (GHM) operator to fuzzy number intuitionistic fuzzy numbers (FNIFNs) to propose the fuzzy number intuitionistic fuzzy weighted geometric HM (FNIFWGHM) operator. Then, the MADM method are built on FNIFWGHM operator. Finally, a numerical example for IAP education quality evaluation of Chinese universities and some comparative studies are used to prove the built methods’ credibility and reliability.

What is the Ordinal Priority Approach?

Humans, in their most important role as decision-makers, make choices every day. Simple choices don't even require a calculator, but more complicated ones require some calculation. Complicated choices, such as the ones where one has to choose one (or multiple) alternatives from a list of alternatives, whereas each alternative may be better than the other on different conflicting attributes, are not easy to make. Such problems are usually referred to as multiple attribute decision-making (MADM), and one of the easiest and most effective ways to solve such problems is the Ordinal Priority Approach (OPA). In this article, readers will learn: (1) what is the OPA (and some discussion on its background), (2) what distinguishes it from other MADM methods, (3) its scale, and (4) how to apply it. Readers, including those with the most elementary knowledge of mathematics and operations research, would find this article particularly useful as every effort has been made to make content easy to comprehend.