Navigating Cross-Border E-Commerce: Prioritizing Logistics Partners with Hybrid MCGDM.

Area of Science:

• Operations Research
• Supply Chain Management
• Decision Science

Background:

• Global e-commerce growth necessitates efficient cross-border logistics.
• Evaluating logistics service providers (LSPs) is complex due to diverse data and uncertainty.
• Existing multi-criteria decision-making models struggle with heterogeneous and uncertain data.

Purpose of the Study:

• To propose HD-CBDTOPSIS, a hybrid multi-criteria group decision-making model for evaluating cross-border LSPs.
• To handle heterogeneous data types and uncertainty in logistics service provider evaluation.
• To provide a robust framework for selecting optimal LSPs in dynamic e-commerce environments.

Main Methods:

• Developed HD-CBDTOPSIS, integrating normal cloud models (NCMs) for uncertainty representation.
• Introduced an improved multi-step backward cloud transformation with sampling replacement (IMBCT-SR) for dataset conversion.
• Utilized a novel Cloud Bhattacharyya Distance (CBD) for similarity measurement and objective criteria weighting via coefficient of variation (CV).
• Applied a cloud-based Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking LSPs.

Main Results:

• The proposed HD-CBDTOPSIS model effectively handles heterogeneous data (exact, interval, datasets, linguistic terms).
• The Cloud Bhattacharyya Distance (CBD) demonstrates superior discrimination compared to traditional distance measures.
• Objective weighting using CV based on CBD ensures reliable criteria importance.
• The model successfully ranks alternative LSPs under uncertain and multi-dimensional conditions.

Conclusions:

• HD-CBDTOPSIS offers an effective, flexible, and robust solution for evaluating cross-border LSPs.
• The integration of NCMs and CBD addresses key challenges in complex decision-making scenarios.
• This model supports informed decision-making in the expanding global e-commerce logistics sector.