E-commerce Logistics Distribution Research Articles

Retraction Note: Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path

Retraction Note: Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path

Research on E-commerce Logistics Distribution Path Optimization Based on Ant Colony Algorithm

With the rapid development of e-commerce industry, the efficiency of logistics and distribution has become a key constraint to its further development. In this paper, we explore the optimization of e-commerce logistics and distribution paths through the ant colony algorithm, aiming to improve the distribution efficiency and reduce the cost. The study firstly analyzes the core elements and challenges of e-commerce logistics and distribution paths, and then describes in details about the ant colony algorithm and its application to path optimization. By constructing a model adapted to the characteristics of e-commerce distribution, we use the ant colony algorithm to optimize a variety of different sizes and complexities of distribution paths. By constructing a model adapted to the characteristics of e-commerce distribution, we use the ant colony algorithm to optimize a variety of distribution paths with different sizes and complexities, and the experimental results show that the algorithm can effectively reduce the total distribution distance and cost. In the test cases, we observe that the distribution paths are shortened by 15\% on average, and the distribution cost is reduced by about 20\%. These results prove the effectiveness and feasibility of the ant colony algorithm in the optimization of e-commerce logistics distribution paths. This study provides a new idea for e-commerce logistics distribution path optimization, which is of great significance for improving logistics efficiency and reducing operation cost.

Cross-border E-commerce Logistics Distribution Optimization Based on Iot Artificial Intelligence Algorithm

Cross-border E-commerce Logistics Distribution Optimization Based on Iot Artificial Intelligence Algorithm

Cross-border e-commerce logistics distribution optimisation based on IoT artificial intelligence algorithm

Cross-border e-commerce logistics distribution optimisation based on IoT artificial intelligence algorithm

Effective Application of Smart Logistics Technology in Rural E-commerce Logistics Distribution

Abstract With the continuous development of smart logistics technology, the status of rural e-commerce logistics and distribution has also increased. This paper combines the intelligent logistics technology framework based on rural e-commerce logistics distribution situation, the selection of rural e-commerce distribution node sites, and distribution path optimization. Furthermore, construct a game model for the rural e-commerce logistics and distribution industry chain with the goal of generating synergy. We examine the traditional rural e-commerce logistics and distribution scheme, distribution node setting, and benefits game based on this basis. The results show that the total cost of the smart logistics distribution program is reduced by 200.7 compared with the traditional distribution program, and the total cost of the smart logistics distribution program saves 5% compared with the traditional distribution program. The average transportation time of smart logistics distribution is 11.22min, which is significantly reduced compared with traditional logistics distribution. The rural smart logistics distribution system for e-commerce that uses benefit sharing and cost sharing profit 1242 is the best program when comparing benefits. To sum up, smart logistics technology plays a significant role in rural e-commerce logistics distribution, which can effectively save logistics distribution costs and enhance distribution efficiency.

A Dynamic Path Optimization Model of IOT Delivery Vehicles for E-commerce Logistics Distribution

Logistics and distribution is a vital link to guarantee the stable supply of the e-commerce market and the healthy development of the industry. With the constant growth of the e-commerce, the efficiency and service quality of logistics and distribution have been paid more and more attention to. Therefore, the study firstly Considering distribution fixed cost, transportation penalty cost and carbon emission cost, the vehicle routing optimization model is transformed into the lowest transportation cost model, then uses an improved traditional artificial fish swarm algorithm to find the optimum way for this model, and finally verifies its performance and applicability through experiments. The performance test results show that the algorithm finds the optimal solution 3589 and 3590 in 63 and 78 iterations in the Oxford Robot Car dataset and Apollo Scape dataset, respectively; the average running time of the algorithm is 11.864s and 11.967s in the 10 operation time tests; in the operation function test, the algorithm. The algorithm was able to overcome the local optimal solution problem. The applicability simulation shows that this algorithm stabilizes after 53 iterations, the minimum cost of the optimal solution of the model is $41,224, and the total distance of distribution is 9035 km. The research algorithm is fast in finding the optimal value, which is close to it, indicating that the algorithm is highly efficient and reliable, and can greatly optimize the path of e-commerce logistics delivery vehicles, and give a theoretical foundation for the optimization of logistics delivery paths in other industries.

RETRACTED ARTICLE: Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path

The study of logistics distribution network under e-commerce environment is conducive to the establishment of efficient logistics distribution system, but also to promote the further development of e-commerce and improve social benefits of great significance. This study considers multiple fuzzy factors and introduces a customer fuzzy time window with variable coefficients, establishes a multi-objective set allocation integrated multi-level location path planning model, and proposes an archive type multi-objective simulated annealing improvement algorithm based on master–slave parallel framework embedded taboo search to solve the model. Tabu search and large-scale neighborhood algorithm are used to solve the initial solutions of the first level network and the second level network respectively, and archival reception criterion is introduced to deal with the multi-objective problem. The results of the proposed algorithm for the two-level site-routing problem are less than 6% different from the internationally known optimal solution. The master–slave parallel computing framework improves the efficiency of the algorithm by about 6.38%. The experimental results prove the effectiveness and necessity of the improved optimization. In addition, this study simulates the site-routing problem model constructed by the study by extending the data of standard examples. The experimental results prove the correctness and reference significance of the multilevel site-routing problem model with multiple fuzzy factors.

Retracted: Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

Retracted: Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

Research on Optimization of Cross-Border e-Commerce Logistics Distribution Network in the Context of Artificial Intelligence

The continuous innovation of artificial intelligence technology has led to industrial upgrading and industry transformation in various industries, and e-commerce logistics has borne the brunt. Artificial intelligence relies on the intelligence of a large number of operations and decisions in the process of logistics operations, as well as the integration of transport, storage, distribution, packaging, loading and unloading, and other aspects of the production process and the hierarchy of the system, which has become an important engine to promote the upgrading of logistics equipment and technology, the innovation of production links and processes, the change in the structure of supply and demand of traditional logistics jobs, and the renewal of the traditional form of logistics. The application of artificial intelligence and the development of a new generation of information technology, including big data, has opened the era of intelligent logistics. And the continuous deepening of China’s foreign trade makes the demand for cross-border e-commerce logistics surge, and the importance of cross-border e-commerce logistics has been rapidly highlighted. Hence, it becomes urgent to construct a cross-line Internet business coordinated operations improvement model by consolidating man-made consciousness innovation. In view of investigating the activity techniques of cross-line online business coordinated factors, this paper advances the improvement methodology of cross-line web-based business operations advancement way with regard to man-made reasoning. By concentrating on the ongoing circumstance and issues of cross-line web-based business coordinated factor circulation module and consolidating the idea of wise strategies, the appropriation stage, the way transportation stage, and freight conveyance phase of Internet business coordinated factor dissemination activity are advanced, in order to accomplish the motivation behind lessening the expense of end dispersion, working on the effectiveness of dissemination, and expanding consumer loyalty.

Design and Optimization of E-Commerce Logistics Distribution System Based on Multiobjective Function

To reduce the complexity and multiple constraints of logistics distribution routing problems, the author proposes an improved genetic algorithm, adaptive immune genetic algorithm (AIGA). The algorithm utilizes a new immune vaccine selection strategy and immune operation method, the optimization process is adaptively changed with the evolutionary algebra, combined with the parallel selection method to optimize the multiobjective logistics distribution path, and the specific steps to solve the multiobjective logistics distribution path problem are given. The experimental results show that the optimal path is calculated as 0—2—8—5—3—1—0; 0—4—7—6—0. AIGA not only efficiently converges to the optimal solution but also stabilizes to a fitness value of 66.5, reflecting better precision than IGA. The computational efficiency and convergence of the algorithm are significantly improved, which verifies the practicability and effectiveness of the algorithm. The adaptive immune genetic algorithm realizes the path optimization of complex logistics distribution, which can better change the global search performance of the original immune genetic algorithm, greatly improve the algorithm convergence speed, and achieve good results in practical applications.

User Experience Evaluation of B2C E-Commerce Websites Based on Fuzzy Information

With the popularization of personal computers and the development of the Internet, the number of netizens is increasing. The emerging B2C e-commerce platform shows the fierce competition in the e-commerce market. B2C e-commerce distribution is faced with the problems of high distribution cost, long time, and poor quality, which leads to the poor user experience of B2C online shopping and the lack of trust in e-commerce enterprises. This research mainly discusses the user experience evaluation of B2C e-commerce websites based on fuzzy information. First, the AHP analytic hierarchy process is used to construct a hierarchical evaluation system, and then, the two-by-two judgment matrix is compared to the index factors at all levels, and experts are invited to score the method to determine the weight of each index to test whether it meets the consistency requirements. Finally, the fuzzy comprehensive evaluation method is used to perform fuzzy conversion of the original weight, and the membership degree set of the user experience evaluation factors is given, and the fuzzy comprehensive calculation result of the B2C website performance level is calculated. By combining in-depth interviews with website users and questionnaire surveys to analyze the behavioral characteristics of user information navigation, summarize the demand list for product search, product selection, product comparison, and product detail page browsing, and provide a reference for the design and development of information navigation. Calculating the decision model, mainly using fuzzy calculation, calculate the foreground value under each attribute and get the comprehensive foreground value. By comparing with the decision-making behavior model constructed by expected utility theory, it is found that the behavioral decision-making model constructed in this paper based on prospect theory can be closer to the actual situation. In this study, the satisfaction with the function provided reached 68 points, the emotional response 66 points, the aesthetic response 70 points, and the information construction 64 points. Through empirical research, the key factors affecting user satisfaction of B2C e-commerce logistics distribution are summarized, and a B2C e-commerce logistics distribution evaluation system based on user experience is established. This research will provide methods and ideas for the research on user experience design of e-commerce websites and the research and development of related network products. The article helps to draw out the countermeasures and suggestions for the development of the current B2C e-commerce logistics distribution.

The Optimization Model of E-Commerce Logistics Distribution Path Based on GIS Technology

The optimization of the e-commerce logistics distribution path has always been an important research object in intelligent control. Based on the geographic information system (GIS) platform, this paper proposes an improved ant colony algorithm for the logistics distribution path optimization model based on the GIS platform. Firstly, ArcGIS software is used to solve the problem of complex networks and realize the selection and output of optional routes. Secondly, the basic ant colony algorithm is improved, and a dynamic path planning method combining global planning information and local planning is proposed. It mainly includes the improvement of the heuristic function and the improvement of the update method of pheromone. Finally, through simulation experiments and case validation experiments, it is concluded that the improved algorithm outperforms the traditional ant colony algorithm. In the case validation part, a local area of Beijing is selected to simulate a natural distribution environment, and the path optimization experiments are validated by building an actual road network model. The results show that the model can plan the optimal path for logistics distribution according to the road congestion. The data analysis shows that the route optimization model proposed in this paper can effectively reduce the distribution cost of enterprises, increase vehicle loading, and increase the profit and industry competitiveness of logistics enterprises.

Analysis of distribution path optimization algorithm based on big data technology

The traditional e-commerce logistics distribution path optimization algorithm has the problem of a long time to find the optimal path. To solve this problem, this paper designs an e-commerce logistics distribution path optimization algorithm in the context of big data, introduces NSGA-II evolutionary algorithm for solving multi-objective optimization problems, combines improved genetic algorithm to solve multi-objective terminal distribution path optimization model in new retail mode to obtain the Pareto optimal solution set of the research problem in this paper, and then establishes a multi-objective function for logistics distribution path optimization through five aspects: weight index, time efficiency index, customer importance index. In this paper, in the process of using the ant colony algorithm to solve the optimization of the end-delivery path of e-commerce logistics, global pheromone update rules such as the ant-perimeter model will be used for the pheromone update in the ant pathfinding process. Then, the multi-objective function of logistics distribution path optimization is established by five aspects: weight index, timeliness index, customer importance index, time window index, total path index, and finally, the distribution target weights are set to find the better distribution path in the objective function according to the different demands of e-commerce logistics, to complete the e-commerce logistics distribution path optimization. We were able to save 6.6% on the route optimized by the ant colony algorithm over the empirical route under the condition of path optimization with the ant colony algorithm. The experimental comparison results show that the designed e-commerce logistics distribution path optimization algorithm in the context of big data is shorter than the traditional algorithm to find the optimal path, which can reduce the e-commerce logistics distribution time and has certain practical application significance.

Research on E-commerce Logistics Distribution Path Planning Based on Improved Genetic Algorithm

With the rapid development of the Internet, e-commerce business has gradually emerged. However, its logistics distribution route planning method has problems such as redundancy of logistics data, which cannot achieve centralized planning of distribution paths, resulting in low e-commerce logistics distribution efficiency and long distribution distances, higher cost. Therefore, in order to improve the ability of logistics distribution path planning, this paper designs an e-commerce logistics distribution path planning method based on improved genetic algorithm. Optimize the analysis of e-commerce logistics distribution nodes, establish a modern logistics distribution system, and optimize the total transportation time and transportation cost under the location model of the logistics distribution center. Using hybrid search algorithm and improved genetic algorithm parameters, an improved genetic algorithm distribution path planning model is established to select the optimal path of logistics distribution, and realize e-commerce logistics distribution path with high accuracy, low error and good convergence. planning. According to the experimental results, the method in this paper can effectively shorten the distance of e-commerce logistics distribution path, reduce the number of distribution vehicles, reduce distribution costs, improve distribution efficiency, and effectively achieve centralized planning of logistics distribution. Therefore, the e-commerce logistics distribution route planning method based on improved genetic algorithm has high practical application value.

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%.

Intelligent Prediction Algorithm of Cross-Border E-Commerce Logistics Cost Based on Cloud Computing

Cross-border e-commerce logistics cost prediction algorithm does not consider logistics distribution scheduling, and logistics information interchange is not enough, which leads to confusion of logistics cost parameters and large deviation. Therefore, an intelligent prediction algorithm of cross-border e-commerce logistics cost based on cloud computing is designed. Introduce cloud computing platforms, optimize the scheduling of cross-border e-commerce logistics distribution tasks, and select the targets for the scheduling of cross-border e-commerce logistics distribution tasks from the aspects such as the shortest waiting time required by customers, the degree of resource load balance, and the costs consumed in completing cross-border e-commerce logistics distribution tasks, and design logistics scheduling process. On this basis, the logistics distribution data are classified, the association rules between the data are mined, and the monitoring of abnormal values in the cost forecasting process is completed. In order to eliminate the interference caused by the difference of different cost management interval, the function value is calculated by weighted Euclidean distance. Design feedback forecast mechanism to realize intelligent forecast algorithm of cross-border e-commerce logistics cost. Experimental results show that the proposed algorithm has better accuracy of cross-border e-commerce logistics cost prediction and higher completion rate of logistics tasks.

Multirobot Task Allocation in e-Commerce Robotic Mobile Fulfillment Systems

Robotic Mobile Fulfillment System (RMFS) is a new type of parts-to-picker order picking system and has become the development trend of e-commerce logistics distribution centers. There are usually a large number of tasks need to be allocated to many robots and the picking time for e-commerce orders is usually very tight, which puts forward higher requirements for the efficiency of multirobot task allocation (MRTA) in e-commerce RMFS. Current researches on MRTA in RMFS seldom consider task correlation and the balance among picking stations. In this paper, a task time cost model considering task correlation is built according to the characteristics of the picking process. Then, a multirobot task allocation model minimizing the overall picking time is established considering both the picking time balance of picking stations and the load balance of robots. Finally, a four-stage balanced heuristic auction algorithm is designed to solve the task allocation model and the tasks with execution sequence for each robot are obtained. By comparing with the traditional task time cost model and the algorithm without considering the balance among picking stations, it is found that the proposed model and algorithm can significantly shorten the overall picking time.

Research on Logistics Distribution in E-commerce Environment Based on Particle Swarm Optimization Algorithm

In order to improve the efficiency of logistics distribution under electronic commerce environment and reduce the logistics cost of distribution path length and time delay, to solve the problem of logistics distribution method at present larger and better convergence, a logistics distribution method in e-commerce environment is proposed based on particle swarm optimization algorithm, the characteristics of construction behavior the characteristics of logistics distribution under the e-commerce environment, the logistics distribution problem into a multi-objective optimization problem of particle swarm optimization. Based on the IOT technology, to establish a stable and efficient logistics supply chain structure, according to the interval distribution characteristics of e-commerce products to the distribution of the time interval of extraction group behavior of particles, the adaptive path characterized the amount of logistics distribution optimization and implementation under the environment of e-commerce logistics distribution path optimization, improve the coverage level of logistics and distribution of the simulation nodes. The results show that the method has stronger spatial search ability, it has higher convergence accuracy and faster search speed, and reduces the time cost and path overhead of logistics delivery.

Optimization model design of cross-border e-commerce transportation path under the background of prevention and control of COVID-19 pneumonia.

In order to better accelerate the transition from traditional trade to cross-border e-commerce, a cross-border e-commerce transportation route optimization model was designed in the context of the prevention and control of new crown pneumonia. Against the background of the new coronavirus pneumonia, through the analysis and research of the current situation of domestic and foreign e-commerce logistics, optimize the cross-border e-commerce logistics distribution model, establish an environmental model, and use efficient search algorithms to search for walking paths that meet environmental requirements. Based on the Dijkstra algorithm model of demand, and based on the linear relationship between demand and delivery distance, an optimal route selection model is established to select the optimal route with the shortest total travel distance. The simulation results show that the cross-border e-commerce transportation time of this model is within 13 h, which is shorter than that of the traditional model. The search efficiency of the optimal route for cross-border e-commerce transportation is higher, and the time for cross-border e-commerce transportation is shorter.

Optimization of E-Commerce Logistics Distribution Path Algorithm under the Background of Big Data

In recent years, online shopping has become a major development trend of shopping. The traditional e-commerce logistics distribution mode has played e-commerce by taking advantage of the advantages of the distribution path algorithm. However, with the increase of the order quantity, the previous path optimization algorithm is no longer efficient. This paper makes an in-depth investigation on the main problems existing in China’s e-commerce logistics. Through the analysis of the survey results, we can see that there are mainly problems such as low degree of data application and imperfect function of logistics information platform. Aiming at these problems, this paper optimizes the logistics distribution routing algorithm according to the actual demand. Through the establishment of three-dimensional constraint model, the problem of low efficiency in the traditional model is effectively improved. In contrast with the genetic algorithm, the distribution mileage of the optimization algorithm is reduced by more than 25% compared with the genetic algorithm, which improves the comprehensive performance of the model. Constraint model established in this paper makes up for the shortcomings of the current e-commerce logistics distribution path algorithm to a certain extent, and makes a contribution to the development of China’s e-commerce logistics distribution field.