Multi-objective optimisation of ionic liquid parameters for hydrogen compressors by Taguchi and grey relational analysis methods

In recent years, cultural and creative industry park is becoming a kind of hot industry to promote industrial restructuring and to improve the quality of urban space. For this reason, cultural and creative industrial parks are planning to build across the country. Currently, cultural and creative industrial parks that develop better than others thank to the government’s overall planning, construction, marketing, and management. At the same time, the new cultural and creative industry parks face enormous challenges, such as how to have together the cultural and creative industries and related groups or how to have area cultural creativity. And it is frequently regarded as a multiattribute group decision-making (MAGDM) process. Thus, a novel MAGDM process is needed to tackle it. Depending on the conventional grey relational analysis (GRA) method and intuitionistic fuzzy sets (IFSs), this paper designs a novel intuitive distance-based IF-GRA method for development potentiality evaluation of cultural and creative garden. First of all, some necessary theories related to IFSs are briefly reviewed. In addition, since subjective randomness frequently exists in determining criteria weights, the weights of criteria are decided objectively by utilizing the CRITIC method. Afterwards, relying on novel distance measures between IFSs, the GRA method is extended to the IFSs to calculate assessment score of each alternative. Eventually, an application about development potentiality evaluation of cultural and creative garden and some comparative analysis is given. The results think that the designed method is useful for development potentiality evaluation of cultural and creative garden.

The software development life cycle (SDLC) encompasses diverse aspects, including design, requirements, methodologies, programming languages, tools, frameworks, and user interface design. Effective interoperability and integration among these components are crucial for streamlining workflows, mitigating compatibility issues, and delivering robust software solutions. This study employs the Grey Relational Analysis (GRA) method to evaluate the interoperability and relative importance of these SDLC elements in software development endeavors. The analysis reveals that fostering seamless collaboration among design, requirements, methodologies, languages, tools, and frameworks is essential for optimizing development processes and enhancing overall efficiency and effectiveness. The normalized data highlight the varying degrees of influence each component exerts across different SDLC phases, underscoring the need to recognize and leverage these interdependencies. The deviation sequence, obtained through the GRA method, unveils the relative importance or effectiveness of various elements, providing insights for informed decision-making and identifying areas for potential improvement or optimization. Furthermore, the Grey Relation Coefficient quantifies the correlations between different facets of software development, enabling stakeholders to prioritize areas for improvement and focus attention on elements with the most substantial influence. Notably, the final Grey Relational Grade (GRG) Rank reveals that testing frameworks secured the top rank, suggesting their significant impact on overall development quality, followed by programming languages and user interface design. These rankings offer valuable insights for prioritizing efforts and allocating resources, ultimately enhancing development processes and achieving better project outcomes. The study underscores the importance of a holistic and integrated approach to software development, recognizing the interdependencies among various components and leveraging the insights derived from the GRA method. By embracing this approach, software teams can make informed decisions, streamline workflows, and deliver high-quality, user-centric solutions that meet industry standards and drive success in an increasingly competitive digital landscape.

In this paper, the concept of multiple-attribute group decision-making (MAGDM) problems with interval-valued Pythagorean fuzzy information is developed, in which the attribute values are interval-valued Pythagorean fuzzy numbers and the information about the attribute weight is incomplete. Since the concept of interval-valued Pythagorean fuzzy sets is the generalization of interval-valued intuitionistic fuzzy set. Thus, due the this motivation in this paper, the concept of interval-valued Pythagorean fuzzy Choquet integral average (IVPFCIA) operator is introduced by generalizing the concept of interval-valued intuitionistic fuzzy Choquet integral average operator. To illustrate the developed operator, a numerical example is also investigated. Extended the concept of traditional GRA method, a new extension of GRA method based on interval-valued Pythagorean fuzzy information is introduced. First, utilize IVPFCIA operator to aggregate all the interval-valued Pythagorean fuzzy decision matrices. Then, an optimization model based on the basic ideal of traditional grey relational analysis (GRA) method is established, to get the weight vector of the attributes. Based on the traditional GRA method, calculation steps for solving interval-valued Pythagorean fuzzy MAGDM problems with incompletely known weight information are given. The degree of grey relation between every alternative and positive-ideal solution and negative-ideal solution is calculated. To determine the ranking order of all alternatives, a relative relational degree is defined by calculating the degree of grey relation to both the positive-ideal solution and negative ideal solution simultaneously. Finally, to illustrate the developed approach a numerical example is to demonstrate its practicality and effectiveness.

This paper describes the important significance of cutting-edge technology in the machining of polycrystalline diamond (PCD) cutting inserts by comparing the evaluation criteria. The LASER technology of cutting-edge machining is compared with grinding and electrical discharge machining (EDM) technologies. To evaluate the data from the experiments, the Grey Relational Analysis (GRA) method was used to optimize the input factors of turning to achieve the required output parameters, namely the deviation of roundness and chip cross-section. The input factors of cutting speed, feed rate, depth of cut and corner radius were applied in the experiment for three different levels (minimum, medium and maximum). The optimal input factors for turning of aluminum alloy (AW 5083) were determined for the factorial plan according to Grey Relational Grade based on the GRA method for the multi-criteria of the output parameters. The results were confirmed by a verification test according to the GRA method and optimal values of input factors were recommended for the machining of Al-alloy (AW 5083) products. This material is currently being developed by engineers for forming selected components for the automotive and railway industries, mainly to reduce weight and energy costs. The best values of the output parameters were obtained at a cutting speed of 870 m/min, feed rate of 0.1 mm/min, depth of cut of 0.5 mm and a corner radius of 1.2 mm.

In this paper, a decision support methodology for multiple attributes group decision making problem is developed. The proposed methodologies are based on Data Envelopment Analysis (DEA), Grey Relation analysis (GRA) and hybrid GRA-DEA methods. These methods are implemented for evaluation and ranking of organizations in sustainable perspective. In the proposed methods, the payoff values of alternatives are expressed in linguistic terms, given by the decision makers. Then, these linguistic terms are described by triangular fuzzy numbers. Charnes model of DEA is formulated in fuzzy environment to determine efficiencies of DMUs. Following the GRA method’s algorithm, a relative closeness coefficient is defined to determine the ranking order of all alternatives by calculating the distances to the fuzzy positive-ideal solution (FPIS), as well as to the fuzzy negative-ideal solution (FNIS). Also, performance evaluation of these companies is made through hybrid GRA-DEA in fuzzy environment. The weights of the criteria are determined objectively during evaluation of these companies in the proposed GRA and DEA-GRA methods. Comparison of proposed approaches is made and final ranking of the organizations is sustainable perspective is arrived. The proposed methodologies are illustrated with a case study of five steel manufacturing companies in India.

Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts’ opinions.

The selection of the most appropriated external technological acquisition mode for a specific technological innovation is one of the important strategic decision-makings. To solve the complicated decision making problem, this study aims (i) to identify the multi-attribute criteria which influenced the selection of the external technological innovation acquisition mode and (ii) to illustrate the comprehensive proposed model by applying Multi-Criteria Decision Making (MCDM) approach, which was combined of Analytic hierarchy process (AHP) and Grey Relational Analysis (GRA) methods. The result can provide the prioritization of the relative important weight of criteria and the final ranking of alternative. This study is expected to effectively assist the managements or the decision-makers to choose the best appropriated alternatives which matched with their business objectives. A study case for the Thai technology-based firm was also applied in this article. KeywordsAnalytic hierarchy process, External technological innovation acquisition mode, Grey relational analysis, Multi-criteria decision-making method.

Hot water storage in the insulated tank is an effective method of energy storage and also fills the permanent and intermittent domestic need for hot water. In this study, an insulated water tank with 29 litres capacity is developed, and its energy storage performance is analysed through numerical simulation method. Silica gel and Polyurethane are the insulation materials attached to the concrete tank. This water tank is optimised for minimum cost and temperature loss using grey relation analysis (GRA) and response surface method (RSM). The thickness of concrete, polyurethane and silica gel layer are the considered control parameters. The best value obtained from the two optimisation methods are compared, and from GRA method, thickness of concrete, polyurethane and silica gel are identified as 7.5cm, 0.25cm and 0.25cm, respectively. Finally, from the confirmation test, the temperature loss in 24 hours is predicted as 6.29°C with a minimum material cost for the tank as 5914 INR.

The aluminum 6061 alloy is commonly employed in the automotive industry in the manufacture of rims, panels and even the chasses of vehicles and has excellent machinability. In this study, the surface of the cryogenically processed aluminum 6061-T651 alloy was milled using both untreated and cryogenically treated TiN-TiCN-Al2O3-coated cutting inserts. The Taguchi L18 orthogonal array was chosen as the experimental design. As the cutting parameters in the experiments, two different cutting inserts (untreated and cryogenically treated, TiN-TiCN-Al2O3-coated), three different cutting speeds (250, 350 and 450 m min−1) and three different feed rates (0.15, 0.30 and 0.45 mm/rev) were used. After each experiment, the surface roughness and wear values of the cutting inserts were measured, the latter after repeating the experiment five times. Wear and roughness values were optimized using the Taguchi method. Additionally, Gray Relational Analysis (GRA) was used for the combined optimization of wear and roughness values. The optimized findings determined using Taguchi optimization for minimum surface roughness were the cryogenically treated cutting insert, 250 m min−1 cutting speed and 0.45 mm/rev feed rate. The optimized findings for wear were the cryogenically treated cutting insert, 350 m min−1 cutting speed and 0.30 mm/rev feed rate. In the optimization with GRA, the common optimum parameters for surface roughness and wear were the cryogenically treated cutting insert, 250 m min−1 cutting speed and 0.15 mm/rev feed rate. According to the Taguchi and GRA results, the cryogenically treated cutting inserts performed the best in terms of minimum wear and surface roughness. The Gray-based Taguchi methodology proposed in this study was found to be effective in solving the decision-making problem in multi-specific results as wear and surface roughness.

PurposeIt is seen that little amount of work on optimization of mechanical properties taking into consideration the combined effect of design variables such as stacking angle, stacking sequence, different resins and thickness of composite laminates has been carried out. The focus of this research work is on the optimization of the design variables like stacking angle, stacking sequence, different resins and thickness of composite laminates which affect the mechanical properties of hybrid composites. For this purpose, the Taguchi technique and the method of gray relational analysis (GRA) are used to identify the optimum combination of design variables. In this case, the effect of the abovementioned design variables, particularly of the newly developed resin (NDR) on mechanical properties of hybrid composites has been investigated.Design/methodology/approachThe Taguchi method is used for design of experiments and with gray relational grade (GRG) approach, the optimization is done.FindingsFrom the experimental analysis and optimization study, it was seen that the NDR gives excellent bonding strength of fibers resulting in enhanced mechanical properties of hybrid composite laminates. With the GRA method, the initial setting (A3B2C4D2) was having GRG 0.866. It was increased by using a new optimum combination (A2B2C4D1) to 0.878. It means that there is an increment in the grade by 1.366%. Therefore, using the GRA approach of analysis, design variables have been successfully optimized to achieve enhanced mechanical properties of hybrid composite laminates.Originality/valueThis is an original research work.

Radial distribution system is an important link connecting power supply and users, and its power supply reliability is directly related to users. Radial distribution network reconfiguration can transform the network structure by changing the switching state of the distribution network lines, and achieve the goals of reducing network operational losses, improving power quality, and power supply reliability while meeting various constraints such as radial operation, power supply and demand balance, capacity, and voltage. Radial distribution systems have the characteristics of multiple components and complex structures. How to quickly and accurately evaluate the health performance of radial distribution systems and find an optimal solution for network reconfiguration are important issues in distribution network analysis. The network health performance evaluation of radial distribution system is classical multiple attributes group decision making (MAGDM). The probabilistic hesitancy fuzzy sets (PHFSs) are used as a tool for characterizing uncertain information during the network health performance evaluation of radial distribution system. In this paper, we extend the classical grey relational analysis (GRA) method to the probabilistic hesitancy fuzzy MAGDM with unknown weight information. Firstly, the basic concept, comparative formula and Hamming distance of PHFSs are briefly introduced. Then, the definition of the score values is employed to compute the attribute weights based on the information entropy method. Then, probabilistic hesitancy fuzzy GRA (PHF-GRA) method is built for MAGDM under PHFSs. Finally, a practical case study for network health performance evaluation of radial distribution system is designed to validate the proposed method and some comparative studies are also designed to verify the applicability.

Optimization of process parameters based on RSM and GRA method for machining of Inconel-600 by electric discharge machining

<div class="section abstract"><div class="htmlview paragraph">Fused Deposition Modeling (FDM) is a highly adaptable additive manufacturing method that is extensively employed for creating intricate structures using a range of materials. Thermoplastic Polyurethane (TPU) is a highly versatile material known for its flexibility and durability, making it well-suited for use in industries such as footwear, automotive, and consumer goods. Hoses, gaskets, seals, external trim, and interior components are just a few of the many uses for thermoplastic polyurethanes (TPU) in the automobile industry. The objective of this study is to enhance the performance of Fused Deposition Modeling (FDM) by optimizing the parameters specifically for Thermoplastic Polyurethane (TPU) material. This will be achieved by employing a Taguchi-based Grey Relational Analysis (GRA) method. The researchers conducted experimental trials to examine the impact of key FDM parameters, such as layer thickness, infill density, printing speed, and nozzle temperature, on critical responses like dimensional accuracy, surface finish, and mechanical properties. The Taguchi method enabled the systematic exploration of parameters through the design of experiments (DOE). The experimental data was analyzed using Grey Relational Analysis (GRA) to determine the optimal parameter settings. The GRA methodology offers a comprehensive approach to assess and prioritize various performance criteria, taking into account the inherent uncertainties in the manufacturing process. The results demonstrated the efficacy of the Taguchi-based GRA method in pinpointing the optimal parameter combinations for improving the printing quality and efficiency of TPU components. This study enhances the comprehension of Fused Deposition Modeling (FDM) for Thermoplastic Polyurethane (TPU) material and provides a useful framework for optimizing the manufacturing process. Manufacturers can enhance printing productivity, quality, and reliability by utilizing Taguchi-based GRA. This, in turn, promotes the wider use of FDM technology in various industrial applications that demand flexible and long-lasting components.</div></div>

this paper proposed a comprehensive loss reduction evaluation method for regional distribution grids based on G1 method and grey relational analysis (GRA). The method is good at avoiding the prominent disadvantages of analytic hierarchy process (AHP) by determining the weights of evaluation indices. Otherwise, the method of grey relational analysis was used for dimensionless processing. Therefore, the authors put forward an implementation scheme about the preferred area for loss reducing and the optimal index, which is proved to be practical, significant, and applicable. Keywords-distribution network; g1 method; grey relational analysis; combination evaluation

The Head of Warehouse has the main responsibility to manage and supervise the daily operations of the warehouse, including receiving, storing, and dispensing goods. In addition, the warehouse head is responsible for leading the warehouse staff team, providing the necessary direction, training, and supervision to ensure efficiency and safety in warehouse operations. This study aims to select warehouse heads by applying a combination of MOORA and PIPRECIA methods, as well as GRA and PIPRECIA. The combination of MOORA and PIPRECIA as well as GRA and PIPRECIA will be compared based on the final ranking of the two methods used. Based on the ranking results on the MOORA Method, the highest value with a value of 0.40017 was obtained by FHY with rank 1, a value of 0.36637 was obtained by AGL with rank 2, and a value of 0.35721 was obtained by YLS with rank 3 Based on the rating results on the GRA Method, the highest value with a value of 0.12164 was obtained by AGL with rank 1, a value of 0.12017 was obtained by FHY with rank 2, and a value of 0.1054 obtained by TAN with rank 3. Ranking differences between Multi-Method Objective Optimization by Ratio Analysis (MOORA) and Gray Relational Analysis (GRA) can arise due to differences in approaches in evaluating and comparing alternatives. The results of the conformity test can be concluded in the selection of warehouse heads who recommend the GRA method compared to the MOORA method, because the results of the conformity level of the GRA method get a value of 99.99919% higher than the MOORA method.