A contribution of this article is to introduce new q-rung Orthopair fuzzy (q-ROF) aggregation operators (AOs) as the consequence of Aczel–Alsina (AA) t-norm (TN) (AATN) and t-conorm (TCN) (AATCN) and their specific advantages in handling real-world problems. In the beginning, we introduce a few new q-ROF numbers (q-ROFNs) operations, including sum, product, scalar product, and power operations based on AATN and AATCN. At that point, we construct a few q-ROF AOs such as q-ROF Aczel–Alsina weighted averaging (q-ROFAAWA) and q-ROF Aczel–Alsina weighted geometric (q-ROFAAWG) operators. It is illustrated that suggested AOs have the features of monotonicity, boundedness, idempotency, and commutativity. Then, to address multi-attribute decision-making (MADM) challenges, we develop new strategies based on these operators. To demonstrate the compatibility and performance of our suggested approach, we offer an example of construction material selection. The outcome demonstrates the new technique’s applicability and viability. Finally, we comprehensively compare current procedures with the proposed approach.

Data management and finding precise outcomes from large amounts of information are among the biggest challenges for scientists. The technique of multi-attribute group decision-making (MAGDM) is a valuable tool for investigating fuzzy data precisely. The key objective of the paper is to redefine the q-rung orthopair (q-RO) fuzzy set (FS) (q-ROFS) in the term of interval-valued and proposed new aggregation operators (AOs) based on the Aczel-Alsina (AA) t-norm (TN) and t-conorm (TCN) operations. The AA operational laws are a generalized form of existing TNs and TCNs and give more reliable results because they can fluctuate in their parametric values. The concept of interval-valued enlarges the space of membership degree (MD) and non-membership degree (NMD) for decision-makers. By taking qth power, the interval-valued q-ROFS (IV-q-ROFS) structure. The IV-q-ROFS can handle the uncertainty and vagueness in data, then interval-valued intuitionistic FS (IV-IFS) and interval-valued Pythagorean FS (PyFS) (IV-PyFS) and provide accurate results. The thought of power AOs (PAOs) makes the relationship between weight vectors and reduces the chances of uncertainty in aggregated results. By taking advantage of PAOs, this article is devoted to introducing the interval-valued q-ROF Aczel-Alsina power-weighted averaging (IV-q-ROFAAPWA) and interval-valued q-ROF Aczel-Alsina power-weighted geometric (IV-q-ROFAAPWG) operators. The fundamental axioms of AOs, idempotency, boundedness, and monotonicity, are also discussed. To illustrate the importance of suggested AOs, the real-life problem of electric car selection was solved by applying the MAGDM method using the proposed IV-q-ROFAAPWA and IV-q-ROFAAPWG operators. The comparison of proposed AOs with currently present AOs is also part of the article. We finally constructed solid conclusions.

Advancement in mobile phone (MP) technology has revolutionized the lifestyle. In recent years, we observed that MP technology had been involved in almost all aspects of life, such as communication purposes, e‐commerce, mobile baking, and social media connectivity. So, it becomes a hot research topic to select the best MP that fulfills the desired feathers requirement. In this paper, the expert’s familiarity with the examined objects is factored into the initial judgments under the T‐spherical fuzzy sets (T‐SFSs) environment. The T‐SFS is the extension of the picture fuzzy (PF) set (PFS), which gives wider scope for finding the most precise options than existing fuzzy frameworks. The multiattribute decision‐making (MADM) is a common and valuable method for aggregating information. For MADM, various aggregation operators (AOs) have been created over the years. The article introduces the newly proposed approach T‐spherical fuzzy (T‐SF) confidence level weighted averaging T − SFWA_c and T‐SF confidence level weighted geometric T − SFWG_c. Also, some desired properties of AOs are discussed, and the T‐SF entropy measure is introduced for selecting the weight criteria. A MADM framework is introduced, on the behalf of proposed operators. The proposed MADM framework is applied to solve the real‐life example of consumers’ preferences to show effectiveness and practicality. Lastly, the developed framework is set side by side with other prevailing approaches to demonstrate the superiority and significance of other existing AOs.

Effective municipal solid waste management is currently facing a global problem. The increasing accumulation of these waste contents, resulting in improper waste management, has led to various environmental issues, including frequent greenhouse gas emissions and a lack of space for garbage disposal. Solid waste management is a serious problem in the current era. For this type of problematic issue, the concept of intuitionistic fuzzy sets (IFS) is a promising tool for assessing uncertain and fuzzy information. In this article, we propose the preference ranking organization method for enrichment evaluation (PROMETHEE) in combination with the analytical hierarchy process (AHP). The AHP is used for calculating weight vectors of attributes using IFS information. We present the multi-criteria decision-making (MCDM) algorithm based on AHP to investigate the weightage of attributes and the PROMETHEE approach for ranking the alternatives. We solve the real-life numerical example of finding the best solid waste management company for utilizing solid waste material. To investigate the applicability of the proposed theory, we comprehensively compare it with other fuzzy data aggregation models, such as intuitionistic fuzzy Hamacher weighted averaging and intuitionistic fuzzy Dombi weighted averaging operators. On the other hand, many MCDM approaches based on simple fuzzy sets fail to investigate IFS-based information. Hence, our proposed model offers more computational efficiency and is a suitable tool for investigating complicated MCDM problems.