Logistics project assessment for network freight platform: an interval cross-directional distance function approach

The rapid evolution of China’s logistics industry has driven the growth of network freight platforms, offering substantial opportunities to optimize freight matching and resource allocation. However, platform effectiveness is hindered by inherent uncertainty in logistics processes, including information authenticity, safety concerns and settlement risks, which can destabilize platform operations and reduce efficiency and trustworthiness. To monitor and manage these risks while providing network freight platform companies with an effective evaluation tool, this study introduces an innovative data envelopment analysis (DEA) methodology, the interval cross-directional distance function (DDF) model. By incorporating interval data, the approach enables robust assessment of logistics project performance under multi-dimensional uncertainty. Notably, it considers operational anomalies as risk indicators and introduces payment amount as an output to capture financial sustainability, offering comprehensive evaluation balancing risk control and business viability. The method enhances discriminatory power among decision-making units (DMUs) and accommodates varying risk preferences through the Hurwicz criterion. A numerical example illustrates the step-by-step procedure and the method is applied to 27 logistics projects from a network freight platform in Anhui Province. Comparative analysis with existing approaches highlights the proposed method’s advantages in handling data uncertainty, improving project differentiation and supporting more informed managerial decisions.