Synergistic Optimization of Green Inventory-Routing with Heterogeneous Fleets and Demand Risk (Case Study: Iran's Automotive Industry)

Yasser Emamian ¹ ( Assistant Professor, Industrial Engineering, Department of Technology Development Studies, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran )
Negin Fallah ² ( Associate Professor, Agricultural and Entrepreneurship Education, Department of Technology Development Studies, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran )
Seyed Moslem Mousavi Dorcheh ³ ( Assistant Professor, Science and Technology Policy, Department of Technology Development Studies, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran )

Submited date : 2026-08-29 Accepted date : 2026-10-18

Keywords: Carbon Policies, Conditional Value at Risk, Green Inventory Routing, Heterogeneous Fleet, Iranian Automotive Industry, Synergistic Optimization,

Abstract :

The Iranian automotive industry faces significant challenges regarding environmental performance, energy consumption, and logistics efficiency. The transportation sector accounts for approximately 25% of the country's total greenhouse gas emissions. Integrated inventory management and transportation decisions are recognized as effective approaches to reduce costs and carbon emissions in supply chains. This study presents a novel multi-objective synergistic optimization framework for heterogeneous fleet management, dynamic emission factors, Conditional Value at Risk (CVaR) for demand uncertainty, and a comparative analysis of four carbon policies (carbon tax, cap, cap-and-trade, and hybrid) within a Mixed-Integer Linear Programming (MILP) model using real-world data from the Iranian automotive industry. The model simultaneously minimizes total cost, environmental impact (direct and indirect CO2 emissions), and energy consumption. It incorporates dynamic load-based emission factors and indirect emissions from cold storage electricity consumption. To solve the model, the augmented epsilon-constraint method (AUGMECON2), Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO) are utilized. Results demonstrate that the proposed synergistic approach reduces carbon emissions by 23% with only a 2.4% increase in cost. The cap-and-trade policy provides the best economic-environmental trade-off, particularly under high uncertainty and risk-averse conditions. Indirect emissions account for about 12% of total emissions. MOPSO shows better coverage (hypervolume of 0.90), while NSGA-II provides a more uniform distribution (spacing metric of 0.12). The proposed model enables simultaneous improvement of economic and environmental objectives in the Iranian automotive supply chain by creating synergy among inventory, routing, and fleet selection decisions

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