Figure - available from: Advances in Materials Science and Engineering
This content is subject to copyright. Terms and conditions apply.
Source publication
The selection of manufacturing processes for a given application is a complex problem of multicriteria decision-making although there have been several different approaches that can be utilized to select a suitable alternative. However, identifying appropriate multicriteria decision-making approach from the list of available methods for a given app...
Citations
... From an initial screening of the literature of the last two decades from some of the best search engine for an effective literature research, like the Web of science, Scopus and Google Scholar, we found that MCDM methods have been used in many diverse contexts, like: waste water treatment for resources protection (Garcia -Garcia, 2022; Coban et al., 2018;Hadipour et al., 2015;Qin et al., 2017); Chandrakar and Limje, 2018), production and IT industries for the selection of materials and other organizational fields (Sandström, 1985;Brown & Wright,1998;Ghaleb et al., 2020;Zhu et al., 2021), Economics and logistics (Zavadskas & Turski, 2011;Yıldız & Aybar, 2019;Zopounidis et al., 2015;Yuksel et al., 2018;Kowalski et al., 2009), health sector (Frazão et al., 2018;Adunlin et al., 2015;Kahraman et al., 2020;Afshari & Khorsand, 2020) education (Malik et al., 2021;Ayyildiz et al., 2022;Bhattacharyya & Chakraborty, 2014;Alias, et al., 2008), environmental science (Zavadskas et.al., 2014;Geldermann, et al., 2000;Vaillancourt & Waaub, 2004;Huang et al., 2011;Bhanutej & Rao, 2023). We can argue that till today, several research projects were made for measuring the impact of multi-criteria decision-making methods in diverse fields by achieving a mapping of the number of articles and the most cited MCDM methods. ...
This research presents a short review of Multiple Criteria Decision-Making (MCDM) methods and research in various fields, including marketing and business management. The academic literature shows that MCDM methods in the area of marketing are using by academics to solve problems related to the positioning of products and services, market segmentation, brand management, promotion and advertising strategies, product development and market entry strategies, customer relationship marketing and channel distribution. With regard to business and management domain they are using to prioritize various decision-making aspects, like project assessments, resource allocation, strategic planning, risk management, performance evaluation, supplier and vendor selection, human resource management and strategic investment decisions. We can claim that in both domains, MCDM brings a systematic and transparent approach to decision-making, helping marketing managers to make more informed and objective choices. In summary, the continual refinement of these methods and the integration of cutting-edge technologies hold promise for further enhancing the effectiveness and efficiency of decision-making processes in the dynamic landscape of business and management. Further, the analysis highlights emerging trends and challenges in for the future of MCDM research.
... According to the literature reviewed, the most used multi-criteria decision methods are MODM and MADM: distance-based methods, value/utility theory, pairwise comparison process method, outranking methods, metaheuristics, and mathematical programming methods. According to Ghaleb et al. (2020), the selection of MCDM methods evaluation was done depending on the factors: number of alternative processes and criteria, addition or removal of criterion and agility through the process of decision-making, computational complexity, and adequacy in supporting a group decision. Another study by Silva et al. (2021) suggested the selection of a suitable MCDM method is influenced by different factors like time available to decide, the effort that a given strategy will involve, the importance of making an accurate decision, and whether or not the user has to justify their choice to others. ...
... Another example is the study by Saaty in 1977 [15], in which multi-criteria models were used to solve problems with conflicting goals. Several MCDM methods have been developed and applied to support decision making in different areas, such as manufacturing process selection [16], supply chain management contract selection [17], and material selection [18]. In this research area, a combination of VIKOR [19] with entropy weighting methods [20,21] has been chosen as an MCDM methodology to establish optimum die material selection. ...
The selection of the most suitable material is one of the key decisions to be made during the design stage of a manufacturing process. Traditional approaches, such as Ashby maps based on material properties, are widely used in industry. However, in the production of multi-material components, the criteria for the selection can include antagonistic approaches. The aim of this work is to implement a methodology based on the results of process simulations for several materials and to classify them by applying an advanced data analytics method based on machine learning (ML)-in this case, the support vector regression (SVR) or multi-criteria decision-making (MCDM) methodology. Specifically, the multi-criteria optimization and compromise solution (VIKOR) was combined with entropy weighting methods. To achieve this, a finite element model (FEM) was built to evaluate the extrusion force and the die wear during the multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR in combination with the entropy weighting methodology, a comparison was established based on material selection and the complexity of the methodology used. The results show that the material chosen in both methodologies is very similar, but the MCDM method is easier to implement because there is no need for evaluating the error of the prediction model, and the time required for data preprocessing is less than the time needed when applying SVR. This new methodology is proven to be effective as an alternative to traditional approaches and is aligned with the new trends in industry based on simulation and data analytics.
... This approach assists decision-makers in identifying the most suitable process for a particular manufacturing area, with the objective of minimizing production costs. Ghaleb et al. [7] compared the performances of different MCDM approaches for the selection of manufacturing processes. The criteria utilized in identifying the optimal manufacturing process were divided into categories such as productivity, accuracy, complexity, flexibility, material utilization, quality, and operational cost. ...
Robotic Process Automation (RPA) has emerged as a powerful technology for streamlining business operations by automating repetitive tasks. It is important for public universities as it helps streamline administrative processes, improve operational efficiency, and free up staff resources, allowing the institutions to focus more on delivering quality education and enhancing the overall student experience. However, selecting the right processes for RPA implementation poses a challenge due to the multitude of criteria involved. To address this issue, this paper proposes a multi-criteria decision-making (MCDM) approach for RPA process selection. The objective of this research is to develop a systematic methodology that enables decision-makers to evaluate and prioritize RPA processes based on multiple criteria, such as process complexity, ROI, and strategic importance. The proposed methodology incorporates two MCDM techniques, including the Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), to assist decision-makers in effectively assessing and ranking alternative RPA processes. AHP helps determine the relative weights of criteria, while TOPSIS ranks alternatives based on their similarity to an ideal solution. A case study was conducted to validate the effectiveness of the proposed methodology. Empirical results showed that “Campus Event Management” is the most suitable alternative for RPA implementation, followed by “Campus Facility Management” and “Library Management”. In the study, sensitivity analysis was also performed by changing the weight values given for three different experts. The findings of this research contribute to the field of RPA process selection by providing a structured framework that facilitates the evaluation and prioritization of RPA processes. The proposed methodology empowers organizations to maximize the benefits of RPA implementation by selecting processes that align with strategic goals, enhance operational efficiency, and optimize resource utilization.
... Ghaleb et al implemented process selection using Vlse Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR), AHP, and TOPSIS. The authors reported that the VIKOR and TOPSIS methods were better suited to choosing manufacturing processes due to their flexibility throughout the decision-making process, quantity of available processes and criteria, suitability in supporting a group option, and ability to add or remove a criterion (Ghaleb et al., 2020). Papakostas et al used the developing, deploying, and using an agent-based decision support platform. ...
The study was primarily concerned with the dimensional deviation for the part produced in the various alignments A, B, C, & D and selecting the orientation or alignments through the least dimensional deviation. In this work, the part is lying on the base (A), the long edge (B), and the short edge (C), and the part is inclined at 45 degrees (D) to the surface of the base plate. Created the components in a variety of orientations using a multi-jet printer. Further, using experimental data (change in length, width, height and diameter), the model has been developed with a regression-based imperial connection to predict the behavior of MultiJet-three-dimensional (MJP-3D) printed components in various orientations. Because the goal was to anticipate the optimum orientation, the Graph Theory and Matrix Approach Method (GTMA) were utilized towards discover the best orientation. In contrast to other orientations, orientation C is determined to be the optimum manufacturing orientation with the least dimensional variation.
... These methods are particularly useful in complex decision-making situations where there are multiple criteria, alternatives, and stakeholders involved [20]. In addition, some MCDM methods can help optimize decisions by identifying the best alternative or compromise solution that maximizes or minimizes a speci c criterion [21]. The method focuses on ranking and selecting from a set of alternatives and determines solutions for a problem, which can help the decision makers to reach the solution. ...
Identifying influential nodes in complex networks remains a significant challenge in network analysis. In this direction, one attractive challenge is to characterize the spreading capabilities of nodes, which could serve as potential regulators of the network. While node centrality methods have been widely used for identifying such nodes, they are often tailored to specific problems. In this research work, a new method InfVIKOR is proposed aimed at accurately identifying influential nodes and addressing bias inherent in single-measure evaluations. This method utilizes a Multi-Criteria Decision Making (MCDM) approach called VIKOR, which integrates multiple parameters to effectively identify influential nodes. The method uses the centrality measure as a criterion with proper optimization method to construct group utility function of the complex network, and then quick sort algorithm is applied to rank the nodes according to their influence score derived from the group utility measure. InfVIKOR prioritizes influential nodes to achieve a balanced combination of efficacy and efficiency. To evaluate the effectiveness of the method, the Susceptible-Infected (SI) model is employed to simulate communication propagation across six real-world networks. The experimental findings underscore the accuracy and efficacy of the proposed method. Further, this method can be used in any hierarchical scale free networks.
... Another example is Saaty in 1977 [15], who used multicriteria models to solve problems with conflicting goals. Several MCDM methods have been developed and applied to support decision-making in different areas such as, manufacturing process selection [16], supply chain managing contract selection [17] and material selection [18]. In this research a combination of VIKOR [19,20] together with Entropy weighting methods [21,22] has been chosen as MCDM methodology to establish the optimum die material selection. ...
... (www.preprints.org) | NOT PEER-REVIEWED | Posted: 5 February 2024 doi:10.20944/preprints202402.0224.v116 ...
Selection of the most suitable material is one of the key decisions to be taken at the design stage of a manufacturing process. Traditional approaches as Ashby maps based on material properties are widely used in the industry. However, in the production of multimaterial components, the criteria for the selection can include antagonistic approaches. The aim of this work is the implementation of a methodology based on the results of process simulations for several materials and classify them by applying an advanced data analytics method based on Machine Learning (ML), in this case the Support Vector Regression (SVR) and Multi-Criteria Decision Making (MCDM) methodolo-gies, in this case Multi-criteria Optimization and Compromise Solution (VIKOR) combined with Entropy weighting methods. In order to do this, a Finite Element Model (FEM) has been built to evaluate the extrusion force and the die wear in a multi-material co-extrusion process of bimetallic Ti6Al4V-AZ31B billets. After applying SVR and VIKOR combined with Entropy weighting methodologies, a comparison has been established based on the material selection and complexity of the methodology used, resulting that material chosen in both methodologies is very similar and MCDM method is easier to implement because there is no need of evaluate the error of the pre-diction model and the time for data preprocessing is less than the time needed in SVR.
... Since it determines the geometric precision of components with complex shapes and sizes, the overcut is the most crucial variable for numerous uses. Recent advances in material development necessitate improvements to the machining process and optimum parameters, because each machining method has drawbacks and performance requirements, choosing the right production process for any product is a difficult task (Ghaleb et al., 2020;Ozcalici and Bumin, 2020;Sadhana et al., 2020;Basak et al., 2021). ...
A highly advanced thermo-electric machining technique called wire electrical discharge machining (WEDM) can effectively produce parts with varying hardness or complicated designs that have sharp edges and are very difficult to machine using standard machining procedures. This useful technology for the WEDM operation depends on the typical EDM sparking phenomena and makes use of the commonly used non-contact material removal approach. Since its inception, WEDM has developed from a simple approach for creating tools and grown to an outstanding option for creating micro-scale components having the greatest degree of dimensional precision and surface finish characteristics. The WEDM method has endured over time as an efficient and affordable machining alternative that can meet the stringent operating specifications enforced by rapid manufacturing cycles and increasing expense demands. The possibility of wire damage and bent, nevertheless, has severely hindered the process' maximum potential and decreased the precision as well as effectiveness of the WEDM process. The article examines the wide range of investigations that have been done; from the WEDM through the EDM process' spin-offs. It describes WEDM investigation that required variables optimization and an assessment of the many influences on machining efficiency and accuracy. Additionally, the research emphasizes adaptive monitoring and control of the process while examining the viability of multiple approaches to control for achieving the ideal machining parameters. Numerous industrial WEDM applications are described with the advancement of hybrid machining
... In order to assess the complexity of various MCDM methods, the number of mathematical operations implicated in their computations is identified, which can be likened to measuring time complexity based on the number of calculations performed (Chang, 1996;Chatterjee & Chakraborty, 2022;Ghaleb et al., 2020;Lima Junior et al., 2014;Yazdani et al., 2020). Given that there are P alternatives for nozzle materials and C criteria for evaluation, the EDAS method requires PC operations to calculate the positive distance from the average solution, PC operations to estimate the negative distance from the average solution, P operations to calculate the sum of positive distances, P operations to calculate the sum of negative distances, 2P operations to normalize the sums of the distances, and P operations to determine the appraisal score. ...
Rapid advancements in 3D printing technology have compelled the manufacturers to search for better nozzle material in the extruder of 3D printers. Materials ranging from brass to tungsten carbide and ruby are primarily used as the nozzle material. In 3D printing technology, due to major constraints imposed by the filament material and other decisive factors, no single nozzle material satisfies all the desired characteristics for a real time application. Thus, it has become crucial to select the most appropriate nozzle material with the desired properties for enhanced 3D printing performance. In this paper, the performance of eight candidate nozzle materials is evaluated based on nine selection criteria. Entropy method is utilized to determine the criteria weights, whereas, evaluation based on distance from average solution (EDAS) method is employed to identify the best suited 3D printer nozzle material. Tungsten carbide emerges out as the best choice, followed by titanium alloy (TiAl6V4). This paper also proposes a sensitivity analysis to establish the robustness of the adopted methodology.
... The computational complexity of the proposed MCDM has been evaluated based on its time complexity (Ghaleb et al. 2020). The time complexity, represented by C, has been determined using the number of augmentations. ...
As an extension of interval-valued intuitionistic fuzzy sets, the concept of interval-valued q-rung orthopair fuzzy (IVq-ROF) sets (IVq-ROFSs) represents an efficient tool for handling uncertain information in a more expansive context, owing to its utilization of the adjustable parameter q ≥ 1. In the present article, we devise Aczel–Alsina (AA) operations to IVqROF numbers, employing the AA t-norm and t-conorm, and subsequently establish their inherent properties. Based on these operations, we originate a series of aggregation operators, including IVq-ROF AA weighted averaging (IVq-ROFAAWA) operator, IVq-ROF AA ordered weighted averaging (IVq-ROFAAOWA) operator, IVq-ROF AA hybrid averaging (IVq-ROFAAHA) operator, IVq-ROF AA weighted geometric (IVq-ROFAAWG) operator, IVq-ROF AA ordered weighted geometric (IVq-ROFAAOWG) operator, and IVq-ROF AA hybrid geometric (IVq-ROFAAHG) operator. Some required properties of the formulated operators are verified, and their interrelatedness is shown exhaustively. Meanwhile, we formulate the IVq-ROF weighted Bonferroni mean (IVq-ROFWBM) operator by leveraging AA
operations, considering that the Bonferroni mean operator can capture the interrelationships among the input arguments. Based on these operators, a decision-making approach is framed for ranking the alternatives in the IVq-ROF environment. Further, we present an illustrative example concerning the distortion of the 2022 monsoon flood to showcase its practical applicability and to examine how various parameters impact the outcomes. Finally, the merits and originality of the presented methodology are underscored through a comprehensive comparison with prevailing approaches.
