Logistics Distribution Route Optimization Based on Genetic Algorithm

Abstract

Aiming at the problem of logistic division based on genetic algorithm, it is planned to study the improvement of logistic distribution methods. We first meet the requirements of the genetic algorithm of logistic development, use the division method to divide the delivery area of the gene, and formulate a functional delivery plan, which generally includes weight measurement, measurement time, customer value measurement, instrument measurement time, and the whole process index. We set weight goals and find the best way to improve genetic algorithm delivery. The experimental comparison results show that the optimal method takes less than 2 minutes to find the optimal method, while the normal process takes 4 minutes to find the optimal method, and the longest can reach 5 minutes. The comparison shows that the traditional algorithm takes longer to find the correct way than the algorithm developed this time. Finally, the simple logistic distribution optimization method model and the soft time-limited logistic distribution processing optimization model are calculated and simulated by the genetic testing algorithm and genetic algorithm development. The effectiveness of the improved genetic algorithm in local research and the effectiveness of the logistic transportation allocation solution are determined.

Enhancing Logistic Efficiency in Product Distribution through Genetic Algorithms (GAs) for Route Optimization

“…This current study on the application of Genetic Algorithms (GA) in logistics optimization both aligns with and diverges from previous research in several key aspects. Like the studies by Xin et al (2022) and Yang and Wu (2021), it employs GA for enhancing the efficiency and cost-effectiveness of distribution processes [11] [13]. Echoing the environmental concerns addressed in the works of Li et al (2020) and Zhang (2022), this research also emphasizes the importance of sustainable logistics, demonstrating how optimized routes can reduce carbon emissions [15] [16].…”

“…Recent advances in the application of Genetic Algorithms (GA) for logistics and distribution optimization are underscored by a series of studies, which highlight the efficacy and adaptability of algorithms in various logistics contexts. Xin et al (2022) focused on optimizing logistics distribution routes using GA, which showed significant time efficiency in finding optimal routes compared with traditional methods [11]. Similarly, Cui et al (2023) explored route optimization in urban logistics by introducing adaptive GA, emphasizing customer satisfaction and cost optimization, thereby validating the effectiveness of adaptive GA compared with traditional GA [12].…”

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

“…For the usage of a genetic algorithm, only parts of the entire supply chain can be studied at a time [55]. To overcome this problem, Xin et al [56] proposed an improved genetic algorithm to reduce the time required for local search. Meanwhile, the limitation of a genetic algorithm is that a genetic algorithm may decrease the quality and rate of convergence of the population and cause a loss of diversity in the population, which may result in premature convergence [5,57,58].…”

A fuzzy model for generalized transportation problems in China

“…12 GAs significantly enhance vehicle routing in transportation networks, notably reducing travel distance, fuel consumption, and CO 2 emissions. 13 Previous research has shown the effectiveness of employing GA and fuzzy logic in optimizing multimodal transportation networks. Hybrid approaches have proven successful in lowering transportation costs and enhancing service levels.…”