Research On Low-Carbon Trends In Cold Chain Logistics By Applying Meta-Heuristic

Abstract

Sustainable development has emerged as a critical focus for businesses, particularly in terms of energy consumption and carbon emissions. This is especially relevant for cold chain logistics, where the impact of carbon emissions must be thoroughly assessed within operational costs. This study aims to minimize total costs, including fixed costs, transportation costs, damage costs, refrigeration costs, carbon emissions costs, and penalty costs within the context of Vehicle Routing Problems (VRP) with time windows for fresh frozen meat products. The research involves selecting a specified number of customers within the company's distribution network and solving the problem using mathematical models such as Mixed Integer Linear Programming (MILP), complemented by metaheuristic methods like Genetic Algorithms, to manage larger-scale operations effectively. The results will provide optimized distribution routes aimed at minimizing operational costs and reducing carbon emissions. This study will offer valuable insights into low-carbon transformation in the cold logistics industry and contribute to the broader discourse on sustainable development and environmental protection in business operations