Logistics Distribution Routing Optimization Research Articles

Logistics distribution route optimization of electric vehicles based on distributed intelligent system

Abstract The data management system of health cloud authentication plays an important role in the optimization of logistics vehicle routing. It can not only help logistics vehicles choose the best distribution path, but also save time and cost and improve economic efficiency. At present, logistics has not yet formed a complete service system. High distribution costs and low distribution efficiency limit the development of the entire logistics. The reduction of logistics costs and the improvement of distribution efficiency have become the top priorities of the society. The optimization of the distribution route is the key to cost saving and distribution logistics. It is particularly important to study and optimize the distribution route, because the distribution route affects the logistics transportation efficiency and the loss cost during transportation. Therefore, this paper adjusted the scheduling system of logistics vehicles through a distributed intelligent system, and optimized the path of logistics vehicles according to the improved genetic algorithm, thereby reducing the transportation cost of logistics and improving the efficiency of logistics distribution. This article first explains the definition, classification, and main components of the delivery vehicle routing problem. Then, using cloud authentication path optimization, a distributed intelligent system is constructed. Finally, an improved ant colony algorithm is used to analyze and study the distance constraints of vehicles. By improving the ant colony algorithm, it can be seen that the optimized path pheromone concentration and the optimized sub-function have gradually increased with time. The mean pheromone concentration was 40 %, and the seventh day was 15 % higher than the first. The mean value of the optimized subfunction was 0.34 %, and the seventh day was 20 % higher than the first. The distribution cost and distribution efficiency of the optimized logistics vehicle distribution path were much higher than those of the traditional logistics distribution path. Moreover, the distribution cost of the logistics distribution path was 9 % lower than the traditional one, and the distribution efficiency was 13 % higher. The average smoothness of the optimized logistics path is about 90 %, and the seventh day is 11 % higher than the first day. The average fitness of the optimized logistics path is 88 %, and the seventh day is 14 % higher than the first day. In a word, the data management system can uniformly schedule logistics vehicles and improve the efficiency of distribution.

Logistics Distribution Route Optimization With Time Windows Based on Multi-Agent Deep Reinforcement Learning

Multi-depot vehicle routing problem with time windows (MDVRPTW) is a valuable practical issue in urban logistics. However, heuristic methods may fail to generate high-quality solutions for massive problems instantly. Thus, this article presents a novel reinforcement learning algorithm integrated with a multi-head attention mechanism and a local search strategy to solve the problem efficiently. The routing optimization was regarded as a vehicle tour generation process and an encoder-decoder was used to generate routes for vehicles departing from different depots iteratively. A multi-head attention strategy was employed for mining complex spatiotemporal correlations within time windows in the encoder. Then, a decoder with multi-agent was designed to generate solutions by optimizing reward and observing transition state. Meanwhile, a local search strategy was employed to improve the quality of solutions. The experiments results demonstrate that the proposed method can significantly outperform traditional methods in effectiveness and robustness.

Logistics Distribution Route Optimization in Artificial Intelligence and Internet of Things Environment

With the increasing challenges facing the logistics industry, especially in meeting the growing demand for distribution efficiency and accuracy, the use of modern technology to optimize logistics distribution routes has become a key issue. This study explores the application of artificial intelligence (AI) and Internet of Things (IoT) technologies in the optimization of logistics distribution routes. The research first focused on collecting and processing logistics-related data, including historical delivery records, real-time traffic data, and cargo tracking information. Then, by building optimization models based on AI and IoT technologies, the study explores the potential of these technologies to improve logistics distribution efficiency and reduce costs. In addition, through cost-benefit analysis and discussion of challenge-coping strategies, this study not only verified the effectiveness of the proposed scheme in theory but also demonstrated its feasibility in practical application. Finally, the study presents implications for industry practice and recommendations for future research, emphasizing the importance of continuous technology evaluation and adaptation to market changes.

Retracted: Multiparty Coordinated Logistics Distribution Route Optimization Based on Data Analysis and Intelligent Algorithm

Retracted: Multiparty Coordinated Logistics Distribution Route Optimization Based on Data Analysis and Intelligent Algorithm

Retracted: Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm.

[This retracts the article DOI: 10.1155/2021/3338840.].

Logistics Distribution Route Optimization Based on Improved Particle Swarm Optimization

Logistics Distribution Route Optimization Based on Improved Particle Swarm Optimization

Research on logistics distribution route optimization based on deep learning model and block chain technology

The growing data age is reflected in all aspects of today's society. In the field of logistics, especially when the road conditions in urban areas are complex, how to select the optimal distribution path and reduce the distribution time is a problem worthy of attention. Aiming at the problems faced by traditional algorithms in solving the distribution of logistics vehicles in urban areas, however, the method based on regional chain technology can better solve the path optimization problem. A deep reinforcement learning algorithm based on attention mechanism and LSTM model is designed and applied to the distribution path planning of logistics vehicles. The distribution optimization path of logistics vehicles is obtained through sample training experiments, Thus, it provides a new idea for the optimization of logistics distribution path.

Design and optimization of logistics distribution route based on improved ant colony algorithm

To improve the effect of logistics distribution path optimization design, this paper combines the improved ant colony algorithm to study the logistics distribution path design and optimization. The key point of the fusion of genetic algorithm and improved ant colony algorithm is that the path optimal solution is transformed into the initial distribution of pheromone, and the transformed model rules affect the final algorithm effect. In addition, the mutation operator forms new individuals by changing the gene values of certain positions on the individual chromosomes to expand the search space to areas that may not be close to the current population, so that the genetic algorithm has local random search capabilities and accelerates the convergence to the optimal solution. Through the analysis, it can be known that the logistics distribution route optimization design and optimization method based on the improved ant colony algorithm proposed in this paper has good results.

Multiparty Coordinated Logistics Distribution Route Optimization Based on Data Analysis and Intelligent Algorithm

Aiming at the shortcomings of traditional genetic algorithms such as premature and insufficient local search ability, a hybrid genetic algorithm combining k -means algorithm and cluster analysis to improve genetic algorithm is proposed. Among them, the selection operation adopts the tournament selection strategy of the elite retention model, the crossover operation adopts the double cut-point crossover, and the variation operator introduces the k -exchange variation operation to ensure the evolution of individuals from generation to generation. Through the selection, crossover, and variation operations, the objective function is minimized, the vehicle travel distance is greatly reduced, and the distribution route is optimized.

A logistics distribution route optimization model based on hybrid intelligent algorithm and its application

A logistics distribution route optimization model based on hybrid intelligent algorithm and its application

Logistics Distribution Route Optimization Based on Genetic Algorithm

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.

Logistics Distribution Path Optimization Using Support Vector Machine Algorithm under Different Constraints

Accelerating product flow, improving service level, lowering logistics costs, reducing the possibility of product losses in circulation, and thus optimizing the logistics distribution system are the issues that enterprise managers should consider in logistics distribution. Traditional algorithms can only solve simple problems, while intelligent algorithms can solve the most complex combinatorial optimization problems. The optimization problem of logistics vehicle scheduling path with different constraints is studied in this paper using the SVM algorithm, and the improved algorithm is simulated to verify its effectiveness. The simulation results show that the logistics distribution path optimization method based on the SVM algorithm has good global searching ability, effectively avoids the algorithm falling into local optimum, and reduces total distribution cost, proving the algorithm’s effectiveness. This scheme can optimize vehicle routes, increase distribution efficiency, and reduce logistics costs, and it can be used in a wide range of logistics distribution route optimization applications.

Logistics Distribution Route Optimization Model Based on Recursive Fuzzy Neural Network Algorithm.

In recent years, more and more attention has been paid to the utilization of data and information in the logistics distribution path optimization system of e-commerce, but it is difficult to have scientific guarantee in the process of determining the optimal distribution path scheme of e-commerce. How to realize the optimization and adaptive setting of distribution path by using intelligent algorithm has become a hot spot. To battle these issues, this paper studies the logistics distribution path optimization model based on recursive fuzzy neural network algorithm. This paper analyses the research status of logistics distribution path determination scheme and applies the recursive fuzzy neural network algorithm in the selection of e-commerce logistics distribution path scheme. The experimental results show that the recursive fuzzy neural network algorithm can realize the optimization of e-commerce logistics distribution path, and the best distribution route can be made according to the characteristic difference of logistics distribution route, and its distribution accuracy can reach more than 97%.

The Applications of Nature-Inspired Algorithms in Logistic Domains: A Comprehensive and Systematic Review

The utilization of nature-inspired algorithms for logistic domains and its potential to manage uncertainty evolve solutions and conduct optimization leftovers to be an antecedent research domain. The present article has considered primary bio-inspired processes to solve the logistics problem systematically until Feb 2020. We have opted 36 articles to summarize and review. The significant algorithm has been ranked into five primary categories: ant colony optimization, particle swarm optimization, memetic algorithm, genetic algorithm, and artificial bee colony. It is evident from the outcomes that within the past 10 years, bio-inspired procedures have experienced fast progress. They have prosperously been applied for optimization and design of particularly intricate systems like logistics distribution systems. It can be deducted from the outcomes that the other algorithms in the logistics distribution’s optimization have to be enhanced, leading to the elevation of the effectiveness of logistics enterprises. Also, the policy assistance for the logistics organization has been empowered to boost the efficient and healthy progress of it. We can observe that the genetic algorithm and its hybrids have indicated the best efficiency until now. So, it is essential to investigate the logistics distribution route optimization by recent nature-inspired algorithms for optimizing the logistics and selecting a rational distribution scheme.

基于灰狼优化器改进蚁群算法的物流配送路径优化算法

基于灰狼优化器改进蚁群算法的物流配送路径优化算法

Cold Chain Logistics Distribution Routing Optimization Based on Realistic Delivery Time and Ant Colony

In recent years, cold chain logistics, especially for agricultural products has been greatly developed. It aims to keep agricultural product fresh before arrive to the designated locations. In this paper, the definition and current situation of cold chain logistics are introduced first. Then a novel approach for path distribution of cold chain is proposed according to time-consumption. After that, we introduce the Ant Colony Optimization (ACO) method and the way to get realistic deliver time. At last, we put forward a method for distribution path optimization based on delivery time and ACO. Experiments show that the proposed method can achieve the optimal solution effectively.

Cold-Chain Logistics Distribution Routing Optimization Based on PSO

Recently, cold chain which aims to keep agricultural product fresh before arrive to the designated customers has been greatly developed. The distribution routing optimization problem of cold chain has been an attractive research hotspot. In this paper, we introduce the basic PSO method and it’s advantaged about good robustness, global convergence, and intelligent search. Then, we put forward a method for distribution path optimization based on PSO. Taking an example, the evaluation results show that our proposed method can achieve the optimal solution effectively.

Optimization of Vehicle Routing Problem with Time Windows for Cold Chain Logistics Based on Carbon Tax

In order to reduce the cost pressure on cold-chain logistics brought by the carbon tax policy, this paper investigates optimization of Vehicle Routing Problem (VRP) with time windows for cold-chain logistics based on carbon tax in China. Then, a green and low-carbon cold chain logistics distribution route optimization model with minimum cost is constructed. Taking the lowest cost as the objective function, the total cost of distribution includes the following costs: the fixed costs which generate in distribution process of vehicle, transportation costs, damage costs, refrigeration costs, penalty costs, shortage costs and carbon emission costs. This paper further proposes a Cycle Evolutionary Genetic Algorithm (CEGA) to solve the model. Meanwhile, actual data are used with CEGA to carry out numerical experiments in order to discuss changes of distribution routes with different carbon emissions under different carbon taxes and their influence on the total distribution cost. The critical carbon tax value of carbon emissions and distribution cost is obtained through experimental analysis. The research results of this paper provide effective advice, which is not only for the government on carbon tax decision, but also for the logistics companies on controlling carbon emissions during distribution.

Study of logistics distribution route based on improved genetic algorithm and ant colony optimization algorithm

To solve the problem of vehicle routing problem under capacity limitation, this paper puts forward a novel method of logistics distribution route optimization based on genetic algorithm and ant colony optimization algorithm (GA-ACO). On the first stage, improved genetic algorithm with a good global optimization searching ability is used to find the feasible routes quickly. On the second stage, the result of the genetic algorithm is used as the initial solution of the ant colony algorithm to initialize the pheromone. And then improved ant colony optimization algorithm is used to find the optimal solution of logistics distribution route. Experimental results show that the optimal or nearly optimal solutions of the logistic distribution routing can be quickly obtained by this two stages method.

Study on Optimization of Logistics Distribution Routes Based on Opposition- based Learning Particle Swarm Optimization Algorithm

In view of shortcomings of the particle swarm optimization algorithm such as poor late optimization ability and proneness to local optimization etc, this paper proposes an opposition-based learning particle swarm optimization (OBLPSO) algorithm for the optimization of logistics distribution routes, firstly, establishes a logistics distribution route optimization mathematical model, and then solves through collaboration and information exchange among particles, in- troduces an opposition-based learning mechanism to improve particle swarm optimization ability and convergence rate, and finally conducts simulation test on the performance of OBLPSO algorithm on Matlab 2012 platform. The simulation results show that OBLPSO algorithm can be used to obtain logistics distribution solutions with short time and rational routes and thus has certain practical value, compared with other logistics distribution route optimization algorithms.