A Hybrid Integrated Multi-Objective Optimization Framework for Sustainable International Road Logistics Networks: Integrating Transportation Models and Pythagorean Aggregation Decision Methods

Abstract

The Greater Mekong Subregion (GMS) has emerged as a strategic logistics hub due to rapid economic integration and the expansion of cross-border road transportation. However, international road logistics networks in the region must address several competing objectives, including minimizing transportation costs, reducing delivery times, and balancing transport distances among trading partners. To overcome these challenges, this study proposes an innovative hybrid computational framework that integrates the classical Transportation Problem with the Pythagorean methodology (TPPM). The proposed approach consolidates multiple transportation objectives into a unified performance metric based on the Pythagorean concept, thereby enabling simultaneous optimization under practical constraints. In addition, geographic inputs derived from Google Maps and Google Earth via web platforms, which are reliable open-source GIS tools, are incorporated into the transportation model to improve spatial accuracy. A simulated dataset comprising 35 suppliers and 42 customers, representing major logistics nodes in the GMS, is developed to evaluate the proposed method. The computational results indicate that the TPPM approach outperforms the conventional single-objective Classical Transportation Problem (CTP) by producing higher solution quality and more balanced performance. Overall, the findings demonstrate that the proposed hybrid method is a robust decision-support tool for sustainably enhancing the resilience of international logistics planning in emerging economic regions.