Enhancing operational cost-efficiency in Iran’s maritime industry: a hybrid fuzzy AHP and Bayesian approach

The maritime industry is a critical pillar of global trade, but it faces mounting pressure to improve operational cost-efficiency amid rising costs and stringent environmental regulations. This challenge is particularly acute in constrained environments, such as Iran, where sanctions and infrastructure limitations exacerbate inefficiencies. This study develops and applies a hybrid decision-making framework to identify and prioritize the key drivers of operational cost-efficiency in Iran’s maritime sector. The framework integrates the adaptive fuzzy analytic hierarchy process (FAHP) to derive expert-based criterion weights under uncertainty with Bayesian Analysis and Markov Chain Monte Carlo (MCMC) simulations for robust probabilistic inference. Data were collected from 108 industry stakeholders across major Iranian ports. The results demonstrate that the adoption of blockchain technology (weight = 0.28), clean fuel solutions (0.25), advanced logistics optimization (0.22), and risk management mechanisms (0.25) are significantly associated with substantial perceived reductions in operational costs (15–30%) and carbon emissions (15–20%). Statistical and Bayesian validations confirmed all hypotheses with high posterior probabilities. The study provides a context-sensitive, evidence-based roadmap for strategic investment, prioritizing digital infrastructure and clean energy transition to navigate economic constraints and advance sustainability goals. The hybrid FAHP-Bayesian methodology provides a framework for complex decision-making in industrial environments.