The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

https://www.mdpi.com/2073-8994/13/4/525/pdf

Determination of Objective Weights Using a New Method Based on the Removal Effects of Criteria (MEREC)

AHP integrated TOPSIS and VIKOR methods with Pythagorean fuzzy sets to prioritize risks in self-driving vehicles

Kano-based mapping of innovation strategies for renewable energy alternatives using hybrid interval type-2 fuzzy decision-making approach

A Choquet integral based interval Type-2 trapezoidal fuzzy multiple attribute group decision making for Sustainable Supplier Selection

This study presents a comprehensive and comparative analysis of weighting and multiple attribute decision-making (MADM) methods in the context of sustainable energy. As the selection problems of energy involve various conflicting attributes, MADM methods have been widely applied in addressing these issues. In this study, six weighting and seven MADM methods that constitute a total of 42 models are implemented to evaluate different weighting and multicriteria decision-making methods and determine the most efficient and sustainable energy option. To determine the weights of economic, environmental, socioeconomic, and technical attributes, two subjective methods—the analytic hierarchy process and best–worst method—and four objective methods—the criteria importance through intercriteria correlation, Shannon's entropy, standard deviation, and mean weight—are used. Thus, both expert evaluations and data-based assessments are considered. Using each attribute weight provided by the six methods, the ranking of electricity generation options for Turkey is obtained through seven MADM methods: the elimination and choice expressing the reality method, the weighted sum method, the weighted product method, the organization, rangement et synthese de donnes relationnelles (ORESTE) method, the technique for order performance by similarity to the ideal solution, the preference ranking organization method for the enrichment of evaluations, and the multiple criteria optimization compromise solution. Rankings obtained from all models are integrated through the Borda, Copeland, and grade average methods. The results indicate that hydro is the optimal electricity generation option, followed by onshore wind, solar PV, geothermal, natural gas, and coal.

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A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy

A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR

Fault diagnosis of rolling bearing using symmetrized dot pattern and density-based clustering

The development of industry 4.0 has spurred the transformation of traditional manufacturing into modern industrial Internet-of-Things. The most notable feature during this transition is the improvement of digitization and intelligence based on the massive data drives. In such a data-driven environment, the processing, storage, and utilization of the industry data get more and more important. Usually, the traditional data processing architecture runs as a one-way streamline, which cannot adapt to the different requirements of the multi-scenario application. This paper proposed a new industrial big data processing architecture called Phi architecture, which can realize many functions such as batch data processing and stream data processing, distributed storage and access, and real-time control. Compared with other data processing architecture, the Phi architecture combined with edge computing and feedback control has the ability to deal with the different demands in aviation manufacturing. Next, the new architecture is designed for microservices pattern, which improves the flexibility and stability of the architecture, and makes it independent operated in multi-scenarios, such as state monitoring of workshop, adaptive data acquisition, feedback control, and user-oriented information classification. As a proof of concept, the architecture has been tested in a simulation digital manufacturing workshop. The results verify the improved effectiveness of the Phi architecture on the data feedback control and real-time processing. And, the development of microservices architecture greatly improves the efficiency, adaptability, and extensibility of the manufacturing process.

A New Data Processing Architecture for Multi-Scenario Applications in Aviation Manufacturing

Networks of five-axis machine tools produce huge amounts of process data. These data directly reflect the running condition of the machine tool but are seldom used to examine the machine performanc...

Data acquisition and data mining in the manufacturing process of computer numerical control machine tools

This paper demonstrates the modelling and simulation comparison of the static characteristics of a porous, orifice, and multiple type aerostatic thrust bearings on the basis of load-carrying capacity(LCC) and stiffness. The equations Navier-Stokes (N-S) are used to solve the internal distribution of pressure in computational fluid dynamics(CFD) simulation environment. An axisymmetric model, which minimizes the computational time and increases efficiency, is used to evaluate the static characteristics of a porous, orifice, and multiple restrictors of aerostatic bearings. Our numerical analysis and empirical results show the agreement with the significant effect of material and geometrical parameters on the LCC and stiffness. The thickness of the air film is less than 10μm, the multiple orifice restrictors have more LCC than porous and orifice restrictor. The porous restrictor's stiffness is larger than orifice and multiple restrictors. The LCC of porous and orifice is notably smaller than multiple orifice restrictors. Additionally, it is analyzed that LCC of porous, orifice, and multiple orifice restrictors can be improved with an increase in the supply of air pressure.

/pdf/modelling-and-simulation-of-aerostatic-thrust-bearings-80ods6kt73.pdf

Hai Li

Papers

A novel hybrid MCDM model for machine tool selection using fuzzy DEMATEL, entropy weighting and later defuzzification VIKOR

Fault diagnosis of rolling bearing using symmetrized dot pattern and density-based clustering

A New Data Processing Architecture for Multi-Scenario Applications in Aviation Manufacturing

Data acquisition and data mining in the manufacturing process of computer numerical control machine tools

Modelling and Simulation of Aerostatic Thrust Bearings