A decision-making framework based on complex fuzzy bipolar soft sets for investment strategy optimization

Decision-making under uncertainty, bipolarity (positive-negative assessments), and periodic/oscillatory behaviors remains challenging for existing fuzzy and soft set models, often resulting in information loss and unstable rankings. To address this gap, we propose the complex fuzzy bipolar soft set (CFBSS) framework, which integrates complex fuzzy sets (amplitude-phase representation for periodicity) with bipolar soft sets. We develop novel operational laws, score/accuracy functions, distance/entropy measures, and three aggregation operators: CFBSSWA, CFBSOWA, and CFBSHWA, along with their mathematical properties (idempotency, boundedness, monotonicity). A complete MCDM algorithm is introduced and applied to solar panel selection for investment strategy optimization. Comparative and sensitivity analyses indicate preliminary improvements in ranking stability, adaptability, and interpretability compared to existing approaches. While the findings are based on a single case study, they suggest promising potential for applications in finance, healthcare, engineering, and other domains involving dual and time-varying uncertainties.