Multi-echelon supply chain network design with warehouse location and transportation mode optimization for cost and emission reduction: a case study in Northeastern Region of Thailand

This study develops a Multi-Echelon supply chain network model integrating warehouse location and transportation mode optimization to minimize both costs and Greenhouse Gas Emissions. Focusing on a real-world case in Northeastern Thailand's cement distribution network, the research applies a Multi-Objective Linear Programming (MOLP) framework, using the Augmented ε-Constraint Method and preemptive goal programming to balance cost-efficiency with environmental sustainability. Further, to account for uncertainties in operational costs and emissions, a Fuzzy Multi-Objective Linear Programming (FMOLP) model is constructed, utilizing triangular fuzzy numbers and the Max–Min Satisfaction Method. Numerical experiments demonstrate that strategic warehouse placement and transport mode selection significantly influence both operational costs and environmental outcomes. The fuzzy model extension enhances decision-making under uncertainty, providing robust, resilient strategies for supply chain planners. This research offers practical insights for industries seeking to optimize logistics networks under sustainability goals while facing real-world variability.