Projection Method for Material Selection Problem with Triangular Fuzzy Numbers

The aim of this paper is to put forward a new material selection method based on COPRAS method. The method combines the COPRAS method and coefficient of variation method. The new method is simple and easy to use, and coefficient of variation method can objectively determine the attributes weights. Thus it can be easily accepted by decision makers. Finally, a practical example is used to demonstrate the feasibility and effectiveness of the proposed method.

An intelligent method for selecting optimal materials and its application

In this study, the process of efficiency measurement is tackled using multi-attribute decision-making (MADM) processes where a sequential algorithm is proposed. The framework of the study is based on two main stages; first, the data envelopment analysis (DEA), separately formulating each pair of units, formulates the department evaluation problem. DEA is a nonparametric multiple criteria method; no production, cost, or profit function is estimated from the data. In the second stage, the pair-wise evaluation matrix generated in the first stage is utilized to fully rank-scale the units via the fuzzy analytical hierarchical process (FAHP). The FAHP method adopted here uses triangular fuzzy numbers (TFN). Inability of AHP to deal with the impression and subjectiveness in the pair-wise comparison process has been improved in Fuzzy AHP. Instead of a crisp value, Fuzzy AHP a range of value to incorporate the decision maker’s uncertainly. Key words: Data envelopment analysis (DEA), fuzzy analytical hierarchy process (FAHP), triangular fuzzy numbers (TFN), multi criteria decision-making (MCDM), multi-attribute decision-making (MADM), fully-rank.

The urge to remanufacture and address environmental concerns in various industrial processes has drawn the attention of academics as well as practitioners towards Closed-loop Supply Chain Networks (CLSC). Although everchanging and complex external factors including social and economic ones, adversely impact the sustainable development of closed-loop supply chain networks. The basic aim of the research is to optimize the functioning of CLSC networks. For the above-said, two objective functions are made. The first objective is to minimize the cost of production and assembly expenses of the forward and reverse logistics. Secondly, an endeavour has been made to reduce the fixed costs associated with plants and retailers. For the sake of achieving two objective functions, two methods are employed: triangular fuzzy numbers and triangular intuitionistic fuzzy numbers. Among the two methods, triangular intuitionistic fuzzy numbers achieved the said objectives with greater optimization substantiated by statistics. This method can deal with uncertain external factors without undermining the optimization of the CLSC networks.

Provides a systematic methodology for integrating materials selection with the design process, including simultaneous technical and economic evaluation Save hours of frustrating research time Get fast answers about the best material for a particular application For the practicing design and maintenance engineer concerned with corrosion, an excellent reference for graduates and students DonAt get bogged down with voluminous data; get useful information on a vast subject that was previously covered by many different books. If youAre involved in the design or maintenance of products or equipment where corrosion is a concern, you know that materials selection is an age-old problem. Not only do you have to consider the materialAs ability to test corrosion, you must achieve an optimum cost-effective design with minimal total life-cycle cost. In the past, researching the endless sources on corrosion and materials in their countless application was next to impossible. ThatAs why this book was written: to help simplify your materials selection problems. ItAs an exhaustive source on the different corrosion-resistant materials, types of corrosion, factors affecting corrosion, passivation, corrosion monitoring, corrosion control measures, methodology of materials selection, and more. This straight-to-the-point book is divided into six sections: Corrosion Materials: Ferrous Materials Materials: Non-ferrous Metals and Alloys Non-Metallic Materials Corrosion Control Measures Selection of Materials, and is comprised of a total of 30 chapters. It covers a wide spectrum of applications in the aerospace, automotive, chemical, materials, mechanical, power engineering, marine, and biomedical industries, providing a systematic methodology for materials selection. Integral to each materials selection discussion are design considerations manufacturability, and economic concerns, providing you with a solid understanding of the factors influencing corrosion of materials and how these materials can work for you. As a one-stop reference source, this book not only contains thorough explanations, it is also enhanced with 187 figures and 184 tables, enabling you to quickly access easy-to-understand information.

As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework's ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.

Group-oriented service composition is still challenging for big group and personalized QoS requirements. Therefore, this paper presents a Hybrid QoS-aware Web Service Composition Strategies for Group Pareto Optimal Plan (HOWSCS_GGOP) based on multi-attribute group decision making (MAGDM) theory. It includes five main steps: calculating group preference, clustering weighted cluster weights, constructing cluster weighted normalized decision-making matrix, determining the ideal solution, and evaluating alternatives for group synthetically. In addition, a hybrid Web service QoS model is introduced to describe QoS values with real numbers, interval numbers, triangular fuzzy numbers and intuitionistic fuzzy numbers. Experimental results show that the proposed algorithm can well solve the problem of Web service composition for big group and personalized QoS requirements.

Multi-attribute utility analysis (MAUA) has emerged as a powerful tool for materials selection and evaluation. An operations research technique, MAUA has been used in a wide range of engineering areas, of which materials science and engineering is one of the more recent. Utility analysis affords a rational method of materials selection which avoids many of the fundamental logical difficulties of many widely used alternative approaches. However, MAUA has traditionally been used in materials selection problems only, in which there is certainty regarding the attribute levels of the alternatives. For many new technologies this is not the case. Another operations research technique, subjective probability assessment (SPA), can be used to address this issue. SPA makes it possible to measure a probabilistic distribution describing the confidence of the decision maker in the levels of attributes for which there is a high degree of uncertainty. These probability distributions can be used in conjunction with MAUA to provide a consistent framework for making materials selection decisions. Furthermore, the use of these techniques extends beyond the problem of materials selection into the more speculative areas of materials competitiveness and market demand in cases involving new, unproven technologies.

The entropy-weighting method (EWM) and variation coefficient method (VCM) are two typical diversity-based weighting methods, which are widely used in risk assessment and decision-making for natural hazards. However, for the attributes with a specific range of values (RV), the weights calculated by EWM and VCM (abbreviated as WE and WV) may be irrational. To solve this problem, a new indicator representing the dipartite degree is proposed, which is called the coefficient of dipartite degree (CDD), and the corresponding weighting method is called the dipartite coefficient method (DCM). Firstly, based on a large amount of statistical data, a comparison between the EWM and VCM is carried out. It is found that there is a strong correlation between the weights calculated by the EWM and VCM (abbreviated as WE and WV); however, in some cases the difference between WE and WV is big. Especially when the diversity of attributes is high, WE may be much larger than WV. Then, a comparison of the DCM, EWM and VCM is carried out based on two case studies. The results indicate that DCM is preferred for determining the weights of the attributes with a specific RV, and if the values of attributes are large enough, the EWM and VCM are both available. The EWM is more suitable for distinguishing the alternatives, but prudence is required when the diversity of an attribute is high. Finally, the applications of the diversity-based weighting method in natural hazards are discussed.

Multi-criteria decision making method is used in such cases having multiple targets. Multi-objective linear programming problems, which deal with uncertain measurements for both objectives and constraints, are solved by fuzzy multi-objective linear programming methods. Analytic hierarchy process (AHP), one of the multi-criteria decision-making methods based on weighting of objectives, is designed to solve complex problems. In this study, multiple criteria decision making problems and decision makers’ opinions on these problems as well as their solution processes are discussed. Firstly, a fuzzy multi objective linear programming problem is solved using the approaches of Zimmerman and Hybrid. In the literature, these methods are applied using triangular fuzzy numbers based on decision makers’ opinions only in cases where the objective function is minimization. In this study, as an alternative to the triangular fuzzy numbers, trapezoidal fuzzy numbers are defined, and both minimization and maximization of objective functions are examined. Finally, a solution algorithm for a fuzzy multi-objective linear programming problem is proposed under some given conditions. The algorithm is applied to a sample problem that involves modeling of a congress organizing which aims to place attendees to accommodations under specific objectives and constraints. In the evaluation phase of the algorithm, targets are weighted by using triangular and trapezoidal fuzzy numbers. At the end, the results of the proposed algorithm are compared with the results of other methods in the literature.

The selection of suitable material plays fundamental and vital role in product development as each material possesses individual characteristics that contribute many aspects to suit the particular application. A wrong selection of materials favours huge cost contribution as well as product failure. Hence among the various available materials the selection of a particular becomes difficult. A better methodology is more and more needed for the selection of material. Multi Attribute Decision Making (MADM) methods based on mathematical models provide a ranking to the different alternatives, there by the decision making become easier. This paper presents the selection of Magnesium alloy material to use in automotive wheel applications using MADM methods. The influence of weighting factors has also been discussed.

An RBF neural network model with fuzzy triangular numbers as inputs is set up to solve fuzzy multi-attribute decision making (MADM) problems. The model can determine the weights of attributes automatically so that weights are more objectively and accurately distributed. In this model, decision maker's specific preferences are considered in the determination of weights. It is simple, and can give objective results while taking into decision maker's subjective intensions. A numerical example is given to illustrate the method.

The aim of this paper is to propose the methods to select reasonable normalization method in TOPSIS and decide best optimal material combined with individual results from TOPSIS with some popular normalization methods. In this paper, to evaluate performance of normalization method, entropy-based and variation coefficient-based performance scores are introduced. To decide final result of materials selection combined with individual results from TOPSIS with different normalization methods, final rank index of alternative material is proposed. To verify the effectiveness of the proposed methods, TOPSIS with some popular normalization methods is applied to select optimal tribological coating material. As a result, it is desirable to select the normalization method with highest entropy-based and variation coefficient-based performance scores. In order to select best optimal material using TOPSIS with some popular normalization methods, the method to decide final result of materials selection is proposed by using final indices of alternative materials. The proposed methods may be widely used to solve the materials selection problems in engineering practice.

This article is designed to establish the basis of the green ceramic, mainly for traditional ceramic raw materials selected on the lack of ideas and methods, and in-depth study of modern ceramic enterprise in material selection problems. According to the basic concepts of ceramic raw materials, basic adaptation and impact factor analysis, given the general principles and selection process, and this tensor product wavelet neural network modeling and artificial intelligence methods, building material selection of ceramic green system framework. Through the ceramic material composition of the sample input to the network for training and testing, fast convergence, not only analysis of existing materials do not exist misdiagnosis, but also the emerging material composition can all properly classified and presented to environment as the main factor in the decision-making.

Diseases such as diabete and cancer must be detected and treated precociouse. They are generally detected using recognition and classification systems. The latter must be choosen according their accuracies and other measures. In addition, some difficulties and ambiguities can exist related with features representing data sets such as their variant types (eg., integer, nominal, etc.) or overlapping between them. Also, data can be imprecise or incomplete and can influence negatively classification results. Fuzzy sets and their generalizations, resolve problems of data uncertainty by modeling them with linguistic variables. The latter are presented with membership functions having different shapes. Triangular fuzzy numbers which represent tringular functions are very rarely used in classification systems. The cause can be that triangular shape does not preserve in case of some calculations. This research proposes to generalize the classification system "‘Amended Fused TOPSIS-VIKOR for classification"’ (ATOVIC) with triangular fuzzy numbers and to apply it to some UCI date sets. The objective is to unveil the impact of triangular fuzzy numbers in classification. The results of generalized fuzzy ATOVIC are compared to those of its crisp version and those of some existing classification systems using fuzzy sets. Results of ATOVIC generalized with tringular fuzzy numbers are promising. Especially its results on breast cancer Wisconsin and IRIS data sets are higher than Mamdani and Segano fuzzy inference systems using triangular fuzzy numbers from literature.