A Green Supply Chain Model Integrating Carbon Emission Costs in Transportation and Storage Using Heterogeneous Vehicles

Amid growing environmental concerns and stringent regulatory requirements, Green Supply Chain Management (GSCM) has emerged as an essential strategic approach for organizations. This study introduces an advanced optimization model that incorporates carbon emission costs in both transportation and product storage, utilizing heterogeneous vehicle fleets. The proposed model integrates inventory and transportation decisions while considering both environmental and economic impacts, with the objective of optimizing energy consumption and resource allocation across the supply chain. This research formulates and evaluates a multi-product, multi-period optimization framework within the context of GSCM, specifically aimed at minimizing carbon emissions. Initially developed using Mixed-Integer Non-Linear Programming (MINLP), the model is subsequently linearized into a Mixed-Integer Linear Programming (MILP) formulation. It includes various cost factors, such as carbon emission costs in transportation and product storage. The findings demonstrate that increasing the carbon tax rate significantly reduces carbon emissions but concurrently elevates overall supply chain costs. While extending the planning horizon increases overall and transportation costs, inventory holding costs and carbon emissions from logistics and storage remain relatively stable. By presenting a comprehensive optimization framework, this study provides supply chain managers with a robust decision-making tool to mitigate carbon emissions and promote both environmental and economic sustainability.