Understanding complex energy-environment system investments with queuing enhanced large expert set-based decoded decision analytics

Abstract

Complex energy environment system investments involve high levels of uncertainty, interdependent performance criteria, and subjective expert judgments, which make effective decision making a challenging task. The fundamental problem in this context is the lack of robust and integrated evaluation frameworks that can simultaneously handle expert bias, dynamic system behavior, and multidimensional performance assessment. Key aspects that should be analyzed include operational efficiency, system responsiveness, congestion effects, and the reliability of expert driven evaluations. Although the literature offers numerous multicriteria decision making approaches, it largely overlooks the decoding of latent uncertainty in expert assessments and the use of dynamic, system-oriented performance indicators. This study aims to address these gaps by proposing a novel and comprehensive decision-making model for prioritizing strategies that enhance the performance of complex energy environment system investments. The proposed model integrates Manhattan distance-based centrality for consensus expert selection, dynamical influence propagation with entropy optimization for criterion weighting, orthogonal metric robust aggregation for alternative ranking, and newly developed Cipher fuzzy sets to decode uncertainty and latent truth in expert evaluations. The main contribution of this study lies in the introduction of Cipher fuzzy sets and their integration into an advanced decision-making framework, offering improved robustness, interpretability, and stability compared to conventional approaches. The results indicate that queuing theory-based criteria, particularly utilization factor and queue length, play a critical role, while shared green infrastructure funding and urban waste energy efficiency partnerships emerge as priority strategies. These findings suggest that integrated, efficiency oriented, and resilience focused strategies should be emphasized in future energy environment system investments. Methodologically, the proposed framework advances existing multicriteria decision making approaches by integrating Cipher fuzzy sets with consensus based expert selection, entropy driven influence propagation, and orthogonal metric robust aggregation within a unified decision architecture. Nevertheless, the proposed framework is primarily based on expert judgments and a generalized investment setting, suggesting that future studies should validate its applicability using sector specific empirical data.

The authors' abstract, as published at the source. Array, 2026 · DOI ↗