Waste minimization strategies for environmental sustainability analysis of MABAC based on schweizer-sklar prioritized approach for circular bipolar fuzzy systems

Circular bipolar complex fuzzy sets are more efficient to cope with awkward and vague data, because it is a modified technique of various existing tools, for instance, fuzzy sets and their extensions, bipolar fuzzy sets and their extensions, and circular fuzzy sets with their extensions. The main theme of this manuscript is to develop the Schweizer-Sklar operational laws for circular bipolar complex fuzzy information because these laws will be used in the construction of the prioritized aggregation operators for circular bipolar complex fuzzy sets. Therefore, after the analysis of the Schweizer-Sklar operational, we aim to derive the model of the Schweizer-Sklar prioritized averaging operator, Schweizer-Sklar prioritized weighted averaging operator, Schweizer-Sklar prioritized geometric operator, and Schweizer-Sklar prioritized weighted geometric operator for circular bipolar complex fuzzy sets. Thus, we use these operators for the construction of the multi-attributive border approximation area comparison network and the evaluation of the multi-attribute decision-making models. Further, the application of waste minimization strategies for environmental sustainability is very effective and is used to reduce the environmental impact of waste generated during disposal, production, and consumption. The key goal of this technique is to monitor the design systems, behavior, and procedures that reduce pollution, promote sustainable practice, and conserve resources in numerous communities and organizations. The key theme of this article is to analyze the problem of waste minimization strategies for environmental sustainability based on invented models. Finally, we demonstrate numerical examples based on decision-making techniques for addressing the comparison between proposed data with various existing models to describe the variety and worth of the designed approaches.