Hospital hazards and human errors pose a significant and complex problem, with rising incidents and irreversible consequences. Managing laboratory errors and risks is vital due to the presence of chemicals, electrical equipment, and the involvement of students, professors, and staff. The high value of laboratory equipment further underscores the need for robust risk management strategies. To address these challenges, researchers have explored the Failure Mode and Effects Analysis (FMEA) method for risk identification and assessment in healthcare settings. However, recognizing its limitations, this study aims to prioritize and evaluate laboratory errors using an integrated approach that combines the Best-Worst Method (BWM) and Complex Proportional Assessment with a Fuzzy Spherical Environment (CoCoSo-FSE). By applying the BWM, criteria such as severity, detectability, and occurrence probability are weighted to account for the nature of laboratory errors. The CoCoSo-FSE is then employed to evaluate and prioritize 18 identified laboratory errors, reducing uncertainty and enhancing decision-making. The fuzzy spherical set is used to address uncertainties by providing a flexible framework for decision-makers to define membership functions in specific spherical regions, enhancing the representation of knowledge and decision-making information. The proposed approach is compared with other decision-making methods, namely MOORA and COPRAS, demonstrating reliable ranking results. Sensitivity analysis confirms the stability of the approach's ranking when adjusting the flexibility parameter. This integrated approach offers a reliable and robust decision-making technique for managing laboratory errors, providing valuable insights to enhance laboratory safety and risk management for stakeholders, managers, and policymakers.

The decision to purchase the best available electric power wheelchair (EPWC) for a person with a disability in a low-resource context is very stressful, whether it is based on financial circumstances or the availability of medical solutions. The study's objective is to assess the EPWC options available on the market, focused on a set of conflicting criteria. In this research, three multi-criteria decision-making (MCDM) approaches are used to make decisions. ENTROPY method for weightage calculation of various parameters, COPRAS and EDAS methods for evaluating and ranking alternatives are applied. Both COPRAS and EDAS are applied separately for ranking of selected wheelchair models, and to check the robustness of the applied method, sensitivity analysis on cost criterion is carried out. The result shows that for both methods, EPWC-1 is the top priority model to buy, whereas EPWC-7 is the worst model for COPRAS, and EPWC-10 is the worst model for EDAS among the ten alternatives.

An increase in investment is required to support the growth and expansion of the industrial sector in a given country. But the planning and ranking of investments must consider financial resource constraints, high investment risk, the frequency of needs and goals, as well as an unfavorable pattern of investments in the production sectors and industries. To ascertain ranking and economic viability for the investment sectors, this study used optimization techniques called TOPSIS, VIKOR, and COPRAS. From 2020 to 2022, this study was conducted for the Debre Berhan City Administration. The study's findings include investment criteria and outline the importance of certain investment areas. The final findings of this study show that, according to TOPSIS, VIKOR, and COPRAS, the current industrial investment pattern is not ideal investment priorities need to be changed. As a result of the above three optimization techniques; spinning, weaving, and finishing of textile fabric’s sector have been ranked first.

The present paper emphasizes on the development of a hierarchical model using Fuzzy Multiple Attribute Decision Making (FMADM) method for the selection of E-learning websites. The working of the model developed in this research mainly consists of three steps: (i) Summarization and identification of selection indexes, (ii) Selection indexes weights calculations using Fuzzy Analytical Hierarchy Process (FAHP) and (iii) Ranking of alternatives by implementing three MADM analytical methods as Complex Proportional Assessment (COPRAS), Visekriterijumsko Kompromisno Rangiranje (VIKOR) and Weighted Distance Based Approximation (WDBA). In order to demonstrate the applicability and utility of the proposed methods, an empirical example related to the selection of E-learning websites that are widely used to learn the ‘C’ Programming Language for the software development is carried out. In addition, the results of these three methods are also compared to analyze the critical aspects of the selection indexes. It strongly shows that the developed FMADM model of this paper could be an efficient and effective assessment tool.

Agricultural tractor selection is vital for farms, farmers or other agricultural companies in terms
of success and competitiveness in the global market. This selection may be assumed as a
MCDM (Multi Criteria Decision Making) problem involving qualitative and quantitative
factors that must be simultaneously integrated into the selection process. At the same time there
are many agricultural tractor alternatives in the market when purchasing an agricultural tractor.
This paper deals with the agricultural tractor selection problem using TOPSIS method. This
problem is also solved with two other MCDM methods; COPRAS (COmplex PRoportional
ASsessment) and EVAMIX (EVAluation of MIXed Data) to rank the tractors alternatives.
Lastly Borda and Copeland methods are used to aggregate all three ranking results.

The main objective of this research article is to select the best mobile model among various alternatives available on the market. For this analysis 10 alternative models from different brands are selected from different online shopping website having different specifications and ranging from low budget to medium budget in terms of price. For this selection purposes two multiple criteria decision making tools (MCDM) has been adopted i.e. Complex Propor-tional Assessment (COPRAS) and Additive Ratio Assessment (ARAS). The selection process is done based on four important criteria i.e. price, internal storage, RAM and brand. The weightages of the criteria are calculated by using Analytic Hierarchy Process (AHP) and these weightages are further used in COPRAS and ARAS methods. Individual COPRAS and ARAS method is applied for the selection of the best mobile and the preference ranking order of the models are also proposed by each process. The proposed ranking order by both the methods are compared and it is found that the outcome results are more or less the same using both techniques but there is a slight change in ranking of the middle-order alternatives. Both processes give model 1 and model 4 as the best and the worst models respectively among 10 alternatives.

Portfolio selection is a business process which has helped organisations identify an area of com-petitive advantage and it is a major concern to industrial players in the banking sectors. In order to enhance bank portfolio selection, cost, profitability, time and location are important parameters that decision-makers often consider. This study implements a fuzzy-TOPSIS (Technique for Or-der Preference by Similarity to Ideal Solution) framework to evaluate three potential portfolios (automated teller machine gallery, quick service point and branch) for a bank using the infor-mation from three decision-makers. An illustrative example of real bank information is used to demonstrate the proposed framework applicability. The complex proportional assessment of al-ternatives (COPRAS) method is also used as an evaluation technique and the results are com-pared, which yields that the results from the ranking order of fuzzy-TOPSIS and COPRAS were different. However, there is a consistency between the aggregation of intuition-based, fuzzy-TOPSIS and COPRAS ranks and fuzzy-TOPSIS ranking results. The presented framework is an easy-to-apply tool that improves portfolio selection decision in the banking system.