Optimal Industrial Waste Management Strategy for Pakistan Steel Mill Corporation Using Complex Spherical Fuzzy Einstein Aggregation Information

One of the most significant and complete approaches to accommodate greater uncertainty than current fuzzy structures is the T-Spherical Fuzzy Set (TSPFS). The primary benefit of TSPFS is that current fuzzy structures are special cases of it. Firstly, some novel TSPF power Heronian mean (TSPFPHM) operators are initiated based on Aczel–Alsina operational laws. These aggregation operators (AOs) have the capacity to eliminate the impact of uncomfortable data and can simultaneously consider the relationships between any two input arguments. Secondly, some elementary properties and core cases with respect to parameters are investigated and found that some of the existing AOs are special cases of the newly initiated aggregation operators. Thirdly, based on these AOs and Aczel–Alsina operational laws a newly advanced technique for order of preference by similarity to ideal solution (TOPSIS)-based method for dealing with multi-attribute group decision-making (MAGDM) problems in a T-Spherical fuzzy framework is established, where the weights of both the decision makers (DMs) and the criteria are completely unknowable. Finally, an illustrative example is provided to evaluate and choose the pharmaceutical firms with the capacity for high-quality, sustainable development in the TSPF environment to demonstrate the usefulness and efficacy. After that, the comparison analysis with other techniques is utilized to demonstrate the coherence and superiority of the recommended approach.

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a commonly used decision model in multi-attribute group decision making (MAGDM), and a probabilistic linguistic term set (PLTS) is the linguistic variable that can effectively express the fuzziness of decision makers’ (DMs’) preference. However, in actual decision use, PLTS type decision preference needs to be processed before use, which can distort the decision results. The randomness of DM’s preference which also affects the final decision making is often ignored. Therefore, in order to better serve the MAGDM problem, this paper proposes an asymmetric probabilistic linguistic cloud TOPSIS (ASPLC-TOPSIS) method. First, the basic theories of linguistic variables and cloud model (CM) are introduced. Second, the conversation model between linguistic variables and CM is defined along with the operation formula of ASPLC. Third, considering the importance of the DMs’ subjective weights, a DM trust network is established to calculate the DMs’ weights. Finally, the decision process of ASPLC-TOPSIS is proposed and the superiority of this method is proved through experimental studies.

In recent years several previous scholars made attempts to develop, extend, propose and apply Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for solving problems in decision making issues. Indeed, there are questions, how TOPSIS can help for solving these problems? Or does TOPSIS solved decision making problems in the real world? Therefore, this study shows the recent developments of TOPSIS approach which are presented by previous scholars. To achieve this objective, there are 105 reviewed papers which developed, extended, proposed and presented TOPSIS approach for solving DM problems. The results of the study indicated that 49 scholars have extended or developed TOPSIS technique and 56 scholars have proposed or presented new modifications for problems solution related to TOPSIS technique from 2000 to 2015. In addition, results of this study indicated that, previous studies have modifications related to this technique in 2011 more than other years.

This study applies multi-criteria decision-making (MCDM) techniques to identify and evaluate the risk factors associated with Type 2 diabetes mellitus (T2DM). By leveraging advanced MCDM approaches, the study aims to enhance the decision-making process for T2DM diagnosis and management. The primary objective of this study is to develop a comprehensive framework for ranking and prioritizing various risk factors contributing to the onset and progression of T2DM. The study also aims to compare different MCDM techniques and aggregation operators to provide a deeper understanding of the influence of key factors such as genetics, lifestyle, and environmental triggers. The study integrates four advanced MCDM approaches: Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE-II), Graph Theory and Matrix Approach (GTMA), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and VIˇsekriterijumsko KOmpromisno Rangiranje (VIKOR). A diverse range of aggregation operators is utilized within the MCDM framework to enhance the evaluation process, including the Einstein aggregation operator, Hamy mean operator, Dombi operator, and arithmetic-geometric aggregation operators. These operators facilitate a nuanced assessment of risk factors, allowing for a more comprehensive and structured evaluation of their impact on Type 2 diabetes mellitus (T2DM). The study provides a detailed comparison between PROMETHEE-II, GTMA, TOPSIS, and VIKOR rankings. Integrating various aggregation operators highlights the relative significance of different risk factors. The results demonstrate how different methodologies influence the prioritization of risk factors, contributing to a more refined approach to decision-making in T2DM management. This study enhances decision-making processes for T2DM diagnosis and management by offering a robust, operator-driven evaluation system for healthcare professionals. The comparative analysis of MCDM techniques and aggregation operators provides valuable insights into the key risk factors affecting T2DM, ultimately aiding in better healthcare strategies and preventive measures.

This study addresses the challenge of effectively modeling uncertainty and hesitation in complex decision-making environments, where traditional fuzzy and vague set models often fall short. To overcome these limitations, we propose the Fermatean neutrosophic vague soft set (FNVSS), an advanced extension that integrates the concepts of neutrosophic sets with Fermatean membership functions into the framework of vague sets. The FNVSS model enhances the representation of truth, indeterminacy, and falsity degrees, providing greater flexibility and resilience in capturing ambiguous and imprecise information. We systematically develop new operations for the FNVSS, including union, intersection, complementation, the Fermatean neutrosophic vague normalized weighted average (FNVNWA) operator, the generalized Fermatean neutrosophic vague normalized weighted average (GFNVNWA) operator, and an adapted Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. To demonstrate the practicality of the proposed methodology, we apply it to a solar panel selection problem, where managing uncertainty is crucial. Comparative results indicate that the FNVSS significantly outperforms traditional fuzzy and vague set approaches, leading to more reliable and accurate decision outcomes. This work contributes to the advancement of predictive decision-making systems, particularly in fields requiring high precision, adaptability, and robust uncertainty modeling.

In recent times, supplier selection has become one of the most important and crucial activities for companies. In this study, using the extended fuzzy cognitive maps (E-FCM) and technique for order of preference by similarity to ideal solution (TOPSIS), a decision-making support system is realised to assist managers in this activity. E-FCM expresses a causal relationship among criteria, computing linguistic variables to describe a complex situation. The proposed system allows managers to conduct an a priori evaluation regarding supplier suitability, according to both company and market requirements. A panel of experts was formed, according to their expertise areas, to cover the entire problem domain and model it. The problem was investigated in terms of the factors identified by the experts, such as costs, delivery quality, organisational capability, supplier flexibility, service quality and supplied product quality. These factors were analysed using the TOPSIS approach to rank the suppliers, and the use of TOPSIS allows for discrimination of the E-FCM. This decision-making support system was applied to a real case scenario to test its functionality; in particular, an Italian shoes and accessories company. The TOPSIS ideal solutions were defined from two different points of view: based on the standard TOPSIS procedure and on specifics fixed by the company managers. The two approaches resulted in considerably different outcomes, highlighting the need to consider concepts related to company expectations in the E-FCM.

A bibliometric-based survey on AHP and TOPSIS techniques

A new correlation-based measure on Fermatean fuzzy applied on multi-criteria decision making for electric vehicle selection

Multiple attribute group decision making based on quasirung orthopair fuzzy sets: Application to electric vehicle charging station site selection problem

A key issue faced within the manufacturing industry is determining how to measure quality characteristics and prioritise improvements to be made to all substandard quality characteristics of a product with respect to resource requirements and performance improvement potential. This study proposes a QCAC–Entropy–TOPSIS approach in order to address this issue. It combines the Quality Characteristic Analysis Chart (QCAC), entropy method and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The proposed method is not only helpful to measure and determine whether the quality characteristics meet 6σ, 5σ, 4σ or 3σ but also to rank improvements in all substandard quality characteristics of a product in light of resource requirements and potential for performance improvements simultaneously, making it suitable to all manufacturing industries. Moreover, it also can be a powerful tool for analysing the problems that lead to substandard quality characteristics due to poor accuracy and/or precision. Firstly, using the QCAC, the substandard quality characteristics of the product can be determined and the corresponding values of the Discrimination Distance (DD) can then be computed. Subsequently, all the substandard quality characteristics can be regarded as alternatives when conducting entropy and TOPSIS analyses. Secondly, the weights of the evaluation criteria can be calculated by using the entropy method. Lastly, the weights of the evaluation criteria and the values of DD can be substituted into the TOPSIS method. The manufacturer can then categorically prioritise improvement options for all substandard quality characteristics with respect to resource requirements, and consider potential for performance improvements simultaneously. An example is provided for a bicycle quick release manufacturer to illustrate in detail the calculation process of the developed approach. Finally, the advantages of the proposed method are also given through comparisons with Process Capability Analysis Chart and TOPSIS methods.

Selecting a method is a multi-criteria decision-making issue which includes both qualitative and quantitative aspects. In order to choose the best method or a solution,, it is necessary to make a transition between both visible and invisible aspects. The focus of this work is to expand a methodology to evaluate the best method based on Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). In this manuscript, by ‘method’ is meant an algorithm. The relevant criteria which affect the process of algorithmic selection are the image, the sensors, and the resources, and the available alternatives are discrete wavelet transform (DWT)-based image fusion, principal component analysis (PCA)-based image fusion, intensity hue saturation (IHS)-based image fusion, and Laplacian-based image fusion. The assessments of each criterion are calculated using pairwise comparisons based on analytic hierarchy process (AHP) and inserted to the TOPSIS method to rank the alternatives. The process of selecting the alternative and the drawback of an algorithm (AHP, TOPSIS) is demonstrated with the help of numerical example. This manuscript comprises the following section headings: Introduction, Concept of TOPSIS and AHP, Hybrid Method, A Numerical Example, Computation of TOPSIS, Occurrence of Rank Reversal, Conclusion, and References.

This paper presents a modified Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for evaluating system concepts using minimum and maximum customer requirements to determine a best value alternative. The purpose of this decision analysis technique is to identify system alternatives with an optimal combination of performance, reliability, and cost based on ideal customer requirements. Currently, most conventional TOPSIS models use minimum and maximum values under a specific criterion to evaluate each alternative. Using these models, an alternative being evaluated could receive significantly higher scores when reported capabilities are greater than ideal customer requirements. This problem is pronounced whenever weights are applied to criteria where excessive capabilities are recorded. If an organizational goal is to select the best value alternative, use of conventional TOPSIS models may not lead to the best value decision. The modified TOPSIS method presented in this paper restricts scoring for alternatives that provide excess capabilities beyond ideal customer requirements. Criteria weights are assigned according to a customer's prioritized requirements. This Objective Criteria Saturation TOPSIS (OCS-TOPSIS) method was created to provide Decision Makers (DM) with a tool to evaluate system alternatives against performance criteria, reliability and life cycle costs. To demonstrate the effectiveness of OCS-TOPSIS, a basic example is provided that displays the cost savings outcome over traditional TOPSIS.

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

Decision-making is a highly researched topic and various methods have been developed to facilitate a decision-maker (DM) in choosing the best alternative. Saaty's analytic hierarchy process (AHP) has been very popular since 1977 and has been adapted all over the world. However, AHP is a highly-debated topic. Technique for order of preference by similarity to ideal solution (TOPSIS) is another multi-criteria decision-making (MCDM) method developed by Hwang and Yoon in 1981 as a ranking method. This research is focused on identifying which is the MCDM method between AHP and TOPSIS. Since TOPSIS is a ranking method, the authors propose to combine AHP and TOPSIS methods and determine which method's ranking (AHP, AHP-TOPSIS combination, and TOPSIS with equal weights) aligns more closely with the DM's initial preference. Moreover, this research states the efficiency of the method by comparing the time it takes to make a decision and its reliability.

This research was conducted to conduct an analysis that was used to assist in providing recommendations in making decisions regarding the allocation of village funds regarding Karangturi village infrastructure development. The analysis carried out aims to improve the welfare of the community and fulfill the needs of the facilities and infrastructure needed to meet the daily needs of the surrounding village communities, such as landfills, green parks, asphalt roads and so forth. The data that will be used for data analysis in this study include infrastructure data, Criteria, Weight Weights, Calculations and Final Report on what infrastructure is feasible for development. Where this analysis is carried out with the help of a decision-making method namely TOPSIS (Technique For Order Of Preference By Similarity To Ideal Solution) which means that by using TOPSIS (Technique For Order Of Preference By Similarity To Ideal Solution) in addition to getting accurate results, it is expected also get the value of the criteria used to determine the priority of village infrastructure development and get the value of an ideal solution that can be used as a recommendation in making decisions for the allocation of village funds in the field of village infrastructure. The results of the research analysis using the TOPSIS (Technique For Order Of Preference By Similarity To Ideal Solution) method are expected to provide recommendation data along with ranking results in determining the infrastructure that is needed by the community around Karangturi village, Sumbang District.