Customer demand-driven low-carbon vehicles combined strategy and route optimisation integrated decision

Area of Science:

• Logistics and Supply Chain Management
• Operations Research
• Environmental Science

Background:

• Chemical logistics companies face increasing pressure to reduce operating costs and carbon emissions.
• Balancing economic efficiency with environmental sustainability is a key challenge in modern logistics.

Purpose of the Study:

• To propose a low-carbon routing optimization strategy for chemical logistics companies.
• To integrate multi-energy type vehicle (MEV) combination strategies with route optimization based on customer demand.
• To achieve optimal operating costs and carbon emission targets.

Main Methods:

• Development of a combined decision-making model for MEV strategy and route optimization.
• Design and application of an improved genetic algorithm (GA) for solving the optimization problem.
• Cost analysis comparing environmental energy vehicles and traditional energy vehicles in various scenarios.

Main Results:

• The improved GA effectively optimizes low-carbon routing for chemical logistics.
• A rational MEV combination strategy significantly correlates with reduced operating costs and carbon emissions.
• Environmental vehicle purchasing costs and subsidies impact overall operating expenses.

Conclusions:

• Integrated MEV combination and route optimization is crucial for sustainable chemical logistics.
• The proposed model and algorithm provide a viable approach for minimizing costs and environmental impact.
• Policy interventions, such as subsidies, can further enhance the economic feasibility of green logistics solutions.