The transportation route planning and vehicle scheduling in cold chain logistics not only involve complex space and time factors, but also need to consider the challenges such as the shelf life of goods, transportation costs, customer demand and so on. Unreasonable route planning and vehicle scheduling may lead to rising transportation costs, declining commodity quality and even losses, which will seriously affect the efficiency of cold chain logistics and customer satisfaction. The purpose of this study is to discuss the optimization of transportation route planning and vehicle scheduling in cold chain logistics. This study compares the traditional genetic algorithm (GA), simulated annealing algorithm (SA), and the hybrid genetic algorithm (HGA) proposed here to assess the effectiveness of GA in optimizing cold chain logistics. Experimental results demonstrate that the HGA significantly outperforms the others in terms of iterative convergence speed, solution quality, and runtime. The HGA rapidly reaches the optimal solution within a few iterations, thereby enhancing the efficiency of cold chain logistics and reducing overall costs. Our research offers not only a new algorithmic approach for optimizing transportation in cold chain logistics but also serves as a valuable reference for transportation management in related sectors.
Research on Transportation Route Planning and Vehicle Scheduling Based on Optimization Algorithm in Cold Chain Logistics
29.05.2024
512290 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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