Optimization of Location-Routing Problem for Cold Chain Logistics Considering Carbon Footprint

Area of Science:

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

Background:

• Fresh food distribution faces challenges due to perishability and the need for cold chain integrity.
• Traditional logistics optimization often overlooks environmental impact and carbon emissions.
• Developing sustainable and low-carbon distribution networks is crucial for the cold chain industry.

Purpose of the Study:

• To develop a green and low-carbon location-routing problem (LRP) model for cold chain logistics of fresh food.
• To minimize total costs, including carbon emission costs, in the distribution network.
• To evaluate the effectiveness of carbon tax policies on reducing emissions and costs.

Main Methods:

• Formulation of a green and low-carbon LRP model for cold chain logistics.
• Design of a hybrid genetic algorithm incorporating heuristic rules for model solution.
• Application of a practical numerical example for validation and simulation.
• Analysis of carbon tax policy impacts on cost and emission reduction.

Main Results:

• The developed LRP model effectively integrates cost and carbon emission considerations.
• The hybrid genetic algorithm demonstrates efficiency in solving the complex optimization problem.
• Simulation results confirm the model's applicability and provide viable green distribution schemes.
• Carbon tax policies were found to be effective in reducing carbon dioxide emissions.

Conclusions:

• The study provides a valuable framework for optimizing cold chain logistics with a focus on environmental sustainability.
• The proposed model and algorithm offer practical solutions for cold chain enterprises seeking to reduce their carbon footprint.
• Carbon tax policies can serve as an effective tool to incentivize emission reductions in logistics operations.