Logistics Distribution Path Optimization Research Articles

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.

Simulation Research on Logistics Distribution Path Based on Colony Intelligence Optimisation

Abstract In light of the growing emphasis on “logistics integration,” this paper contends that the logistics network system requires comprehensive integration and optimization. We propose the use of the Bacterial Foraging Optimization (BFO) algorithm to enhance the Vehicle Routing Problem (VRP). The penalty function method is employed to direct the BFO algorithm in identifying the optimal vehicle path encoding and to improve the convergence operations for logistics distribution. This study analyzes the fundamental characteristic elements of the VRP, introduces a basic model for the VRP, and develops a logistics distribution path optimization model that minimizes the total cost by thoroughly considering the expenses associated with cold chain low-carbon logistics distribution. A simulation environment is established, and algorithm parameters are set to determine the optimal solutions for both the BFO and the Improved BFO (IBFO) algorithms in the test function. An example is selected for practical application, wherein the design of the distribution center and points is carried out, and the colony intelligence optimization algorithm is applied to simulate the logistics and distribution paths. Combined with the specific case of cold chain distribution of food, the optimization of the cold chain logistics distribution path is solved. In the case study, when the carbon penalty coefficient is ε = 15, the optimal logistics and distribution site selection program is Guangxi and Guizhou, and the total cost of the supply chain is 16,363,365.92 yuan. At this time, the supply chain cost is optimal. Compared with the carbon penalty coefficient ε = 20, the economic cost increased by 70152.24 yuan, but the carbon emission decreased by 1890.9 kg. The optimization of logistics and distribution processes encourages enterprises to develop strategies that promote low carbon and environmental protection and achieve better economic and ecological outcomes.

Optimization of perishable agricultural products logistics distribution path based on IACO-time window constraint

With the development of modern logistics industry, the cold chain logistics distribution of perishable agricultural products has become an important link to ensure food quality and safety. Aiming at the complicated situation in the process of perishable agricultural products transportation path planning, a cold chain logistics distribution path optimization model based on Ant Colony Optimization (ACO) was proposed. Based on the traditional soft and hard time window constraint model, an optimized time window constraint model was proposed, and the update function of pheromone increment was substituted to optimize the traditional ant colony algorithm. An Improved Ant Colony Optimization (IACO)-time-window constrained path optimization model was proposed. The test results showed that in the convergence training experiment in Matlab environment, the research method only needed 19 iterations to reach the lowest level. In the experiment of application effect, the length of the path generated by the research method for car a was 188.93 km. Car Group a could complete 60.38 % offload at the fourth distribution node. The experimental results showed that the proposed route optimization model had better iteration efficiency and could effectively generate a shorter distance distribution path, which provides a new technical support for the route optimization of cold chain distribution of perishable agricultural products.

Retracted: Logistics Distribution Path Optimization Algorithm Based on Intelligent Management System.

[This retracts the article DOI: 10.1155/2022/3699990.].

Dynamic Route Optimization for Chinese E-Commerce Logistics Based on Ant Colony Algorithm

Abstract The limited nature of logistics and distribution vehicles and the variability of customer acceptance service time limit the service efficiency and quality of logistics and distribution. To optimize a logistics distribution path under capacity and time window constraints, a mathematical model of the problem is first developed in this study, with the lowest cost as the model’s objective function. The logistics distribution issue with soft time windows is then addressed using an ant colony algorithm, and a logistics path optimization strategy based on the maximum minimal ant colony system is suggested. Then, the heuristic function is rebuilt to improve the ant colony algorithm’s solution speed, and the pheromone update approach is included. Finally, experimental approaches are used to test the model’s and optimization algorithm’s efficacy for customer sizes of 30, 50, and 100. The experimental results show that the optimized ant colony algorithm has the best value of 2 for α and 3 for β, which can converge earlier. The improved ant colony algorithm also finds the best solution faster than the conventional ant colony method in just 23 rounds. In the mathematical model of the logistics distribution path optimization issue, this study suggests that the optimized ant colony method has the optimization algorithm’s rationality, efficacy, and stability.

Multi-objective Logistics Distribution Path Optimization Based on Annealing Evolution Algorithm

Logistics distribution is a collection of interrelated organizations and facilities. There is a waste of cost and time in many links. Therefore, it is particularly important to use information technology to improve distribution efficiency. Under the constraints of delivery vehicle cost and time, this paper proposes an improved genetic simulated annealing algorithm (SAGA), which combines the global search ability of the genetic algorithm (GA) and the simulated annealing algorithm (SA) with strong local search ability to solve the vehicle routing problem with time windows (VRPTW). The perturbation factor is introduced to improve the local search, and the crossover method is optimized to obtain more efficient genetic operators by using population information. In this paper, combined with the actual application case, the simulation experiment is carried out in MATLAB. The experimental results show that, compared with the traditional genetic algorithm and simulated annealing algorithm, the total cost of the improved genetic simulated annealing algorithm is reduced by about 15%, which provides a more suitable vehicle route planning scheme.

Research on Cold Chain Logistics Distribution Path Optimization under the Application of Big Data

The perishable of fresh agricultural products and the improvement of people's requirements for food quality have put forward higher requirements for the distribution efficiency. This article for the cold chain of fresh agricultural products logistics distribution path optimization problem, in Chengdu shuangliu district A company agricultural products co., LTD., as A research object, using the particle swarm algorithm of the original raw agricultural products distribution path optimization design, calculate more reasonable distribution scheme, effectively improve the distribution efficiency, vehicle rate, and reduce the use of the vehicle, reasonable to reduce the logistics costs, ultimately improve the economic benefit of the enterprise and customer satisfaction, realize enterprise scientific logistics management.

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.

Pharmaceutical distribution path optimization based on K-nearest neighbor algorithm

With the changing situation of the prevention and control of the new crown epidemic, the demand for various types of pharmaceutical supplies is rising, which puts forward very high requirements on the timeliness of distribution. In view of this, this paper establishes a pharmaceutical logistics distribution path optimization model by comprehensively considering constraints such as vehicle loading and latest delivery time in pharmaceutical distribution. In order to solve this type of problem, a nearest neighbor algorithm is proposed, which introduces correlation coefficients to realize the improvement of the performance of the original algorithm. At the end of the paper, the effectiveness of the model and the algorithm is verified by applying a comparative analysis with the existing correlation algorithm with real data from a pharmaceutical company. The experimental results show that the method effectively reduces the delivery mileage and improves the pharmaceutical delivery timeliness.

Low-Carbon Cold-Chain Logistics Path Optimization Problem Considering the Influence of Road Impedance

An improved road impedance function in conformity with Chinese city road traffic is designed for calculating the actual transportation time based on real-time traffic information and the complex road environment in the city. The objective function minimizes the total distribution cost, including fixed cost, transportation cost, damage cost, energy cost, penalty cost, and carbon emission cost. A mathematical model is constructed for cold-chain logistics distribution path optimization considering the influence of road impedance. The model is solved using three particle swarm optimization algorithms with improved weights. The experimental results show that the self-adaptive weighted particle swarm optimization algorithm is more efficient in solving this model. Experimental results obtained from the cold-chain logistics path optimization model considering road impedance compared to those of the model without road impedance indicate that the former are closer to the actual situation. Based on the changes in carbon emission and total cost under different carbon taxes, we analyze the critical carbon tax value and the optimal carbon tax value range to improve the economic and environmental benefits in the distribution process. This study should prove to be of great practical significance and application value for logistics enterprises to conduct rational planning of path problems.

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.

Logistics Distribution Path Optimization Algorithm Based on Intelligent Management System.

There are three key points in logistics distribution: the distribution vehicle, the amount of goods, and the distribution path. Comprehensive calculation and optimization of these three parameters can plan a reasonable and efficient distribution scheme, especially the distribution path. This study aims to study the method of optimizing the logistics distribution path and strives to find the optimal distribution scheme and the shortest path, which can reduce the distribution error rate, distribution time, and labor costs, thus realizing intelligent management. In this study, intelligent management methods such as radio-frequency identification technology and ant colony algorithm are proposed to optimize the logistics distribution path and realize the safe and efficient logistics distribution. The experimental results of this study show that for the current logistics distribution, the RFID system has a positioning rate of close to 100% when the number of fixed points reaches about 300, and almost all accurate positioning is achieved. The ant colony algorithm can also accurately find the shortest distance after selecting appropriate parameters, which is about 200 meters away from the actual shortest distance.

Research on the Optimization of Cold Chain Logistics Distribution Path of Agricultural Products E-Commerce in Urban Ecosystem From the Perspective of Carbon Neutrality

In order to further cope with climate change, achieve green and low-carbon economic development and further improve the construction of ecological civilization, a research method for optimization of cold chain logistics distribution path of agricultural products e-commerce from the perspective of carbon neutrality was proposed. This method takes full account of the requirements of distribution path optimization, it is based on ecological theory and applies ecological methods to study the structure, function and dynamics of human-centered urban ecosystems, as well as the laws of interaction between system components and between the system and the surrounding ecosystems, which is in line with the optimization goal of minimizing distribution operating costs, and the ant colony algorithm is used to construct the cost optimization model of cold chain logistics distribution path based on the weight limit of distribution vehicles and the time window limit. By comparison of simulation experiments, the best parameter solution is 2,873.9931. The difference with other combinations is only 1–2%, and the optimal parameter matching of the algorithm isα = 1,β = 2,ρ = 0.5. Experiments show that the optimal parameter combination is determined on the basis of reflecting different distribution results.

Logistics Information Intelligent Platform Based on Dynamic Network Data

In order to reduce the carbon emissions of cold chain logistics and promote the construction of ecological civilization, a logistics distribution path optimization model with the minimum total cost as the objective function under the carbon tax system was proposed. The model comprehensively considers the technical advantages of the Internet of things and the characteristics of cold chain logistics; introduces soft time window, customer satisfaction, and carbon emissions as the main constraint conditions; uses the improved genetic algorithm to solve the mathematical model; uses Matlab to encode; and shows the effectiveness and rationality of the model and algorithm through examples. The experimental results show that: The total cost is 9917.12 yuan, customer satisfaction is 97.07%, carbon emission is 161.06 kg, carbon tax is 5.64 yuan/ton, fixed cost is 600 yuan, transportation cost is 72.72 yuan, refrigeration cost is 8811.73 yuan, cargo damage cost is 427.03 yuan, penalty cost is 0 yuan, The validity of the algorithm and the rationality of the model are verified. When enterprises add a carbon tax constraint, the total cost of cold chain distribution is reduced, and the carbon emission is reduced compared with that without carbon tax. The research can provide a reference for enterprise cold chain logistics distribution decisions.

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.

Optimization of Regional Logistics Distribution Information Network Platform

In order to solve the problem of regional logistics distribution, a study on the optimization of regional logistics distribution information network platform is proposed. Firstly, in order to meet the development requirements of regional logistics and improve customer satisfaction, the regional logistics path is optimized to solve the problem of long time consumption for the optimization algorithm of regional logistics distribution path to find the optimal path. The clustering algorithm is used to divide the logistics distribution area, and the multiobjective function of logistics distribution path optimization is established, mainly including weight index, timeliness index, customer importance index, time window index, and total path index. The distribution target weight is set to find the best target path, so as to realize the optimization of regional logistics distribution path. The experimental results show that the time for the designed optimization algorithm to find the optimal path is less than 2 minutes, while the time for the traditional algorithm to find the optimal path is more than 3 minutes, up to 4.6 minutes. The comparison shows that the traditional algorithm takes more time to find the optimal path in five times than the algorithm designed this time. Therefore, the optimization method of this design takes less time to find the optimal path than the traditional method, which proves the effectiveness of the regional logistics distribution path optimization algorithm.

Intelligent Selection Algorithm of Optimal Logistics Distribution Path Based on Supply Chain Technology.

How to realize the intelligence of logistics distribution is a hot research topic at present. How to reasonably allocate vehicles, optimize driving routes and travel time, deliver goods to customers on time at the lowest cost, and realize efficient and low-cost operation of the logistics distribution system has always been a problem in academia and industry for many years. Logistics enterprises face problems such as low efficiency of logistics operation, lack of scientific rationality of logistics resource planning, and lack of overall optimization of logistics management operation mode. These are severe tests that steel companies must accept. Under the background of logistics supply chain, the integrated service platform of logistics supply chain has become an urgent research topic. This study takes a steel enterprise as the main research background. On this basis, the two core modules of warehousing and distribution in the logistics business of iron and steel enterprises are qualitatively analyzed, the concept of business process reengineering is proposed, and the logistics supply chain of iron and steel enterprises is established. The concept of comprehensive service platform is realized through RFID technology. In addition, this study conducts a comprehensive analysis and research on the logistics distribution path optimization and vehicle scheduling problem, designs and implements a logistics vehicle scheduling management system, and then adopts the multiobjective method to solve the logistics distribution path planning problem, SMEs. Genetic algorithm and a simulation decision-making subsystem suitable for this problem are designed, which can better solve the problem of route optimization and vehicle scheduling in small-scale distribution.

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.

DBNTFPO: ANN-Based Approach for Logistics Distribution Optimization

This research aims to improve the optimization ability of traditional algorithms under complex urban road conditions. Henceforth, in this paper, the logistics distribution path optimization problem model is established. Each part of the model is introduced in detail, the weight update of the ant algorithm is introduced, the problem of unreasonable road parameters set by the ant algorithm is solved, and the Deep Belief Network Traffic Forecast Path Optimization (DBNTFPO) algorithm is proposed. The related applications of deep learning technology are analyzed, and the relationship between deep learning technology and real-time distribution vehicle routing problem is discussed. Finally, the challenges brought by the real-time logistics distribution path optimization problem to deep learning are introduced. Finally, the effectiveness and feasibility of the algorithm in the actual logistics distribution are demonstrated through an example analysis.

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.