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This study presents a review of literature on the usage of artificial neural networks (ANNs) architecture contribution method and structural equation modeling (SEM), and proposes a new selection process in the context of algorithm -based SEM-ANNs schemes. This study enriches academic literature by providing a review of all the main aspects of customization in ANNs and contribution methods in combination with SEM. Academic databases are examined for exhibition findings, yielding 253 papers published between 2016 and 2022. The retrieved papers are categorized according to inclusion criteria, and the final set of 73 articles are discussed based on two directions, namely, 'Sector-based' and 'Algorithm-based' as a new representation of taxonomy research. A state-of-the-art bibliographic analysis is presented. This review also identifies modern challenges and open issues in terms of multiple evaluation criteria, importance criteria, and data variations related to the selection of customizations in ANNs and contribution methods combined with SEM in different industrial cases. Several issues fall under multicriteria decision making for handling complexity problems in different ANNs and contribution methods. Thus, this study also presents a research proposal and recommends a solution based on a three-phase methodology for handling the selection and overcoming the identified issues, subsequently completing a strategic guideline solution. [ABSTRACT FROM AUTHOR]

• Formulated an extension of FWZIC method under fuzzy environment q-ROFS were called q-ROFWZIC. • Formulated an extension of FDOSM under fuzzy environment q-ROFS were called q-ROFDOSM. • Developed an integration between q-ROFWZIC method and q-ROFDOSM. • Performed the proposed Integration Methods on a Case Study of COVID-19 Vaccine Distribution Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase 'development' presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of 'recipients list' and 'COVID-19 distribution criteria' was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria. [ABSTRACT FROM AUTHOR]