Logistics Distribution Path Research Articles

Improved GA-LNS Algorithm for Solving Vehicle Path Problems Considering Carbon Emissions

Logistics, as a significant field for achieving energy-saving and carbon reduction goals, is recognized as a crucial direction for realizing the global “double carbon” objective, while vehicle path optimization is an effective method for promoting energy efficiency and reducing carbon emissions. In this paper, an improved genetic algorithm is proposed for optimizing logistics and distribution paths concerning the carbon emissions of fuel vehicles throughout the logistics and distribution process, and a low-carbon logistics and distribution path model is constructed based on time windows, vehicle loading, and carbon emissions. The INNC method is adopted to initialize the population, and an enhanced genetic algorithm (GA-LNS) is designed to solve the model in conjunction with a large-scale neighborhood vector search algorithm. The results indicate that the initialization of the population using the INNC method produces a higher-quality initial solution. Compared to traditional genetic algorithms and particle swarm optimization, the GA-LNS algorithm exhibits superior robustness, effectively addressing the limitations of traditional genetic algorithms that rely on initial solutions and are prone to local optima. By comparing the computational results of the low-carbon logistics distribution path model constructed in this study with those of traditional optimization objective models, it is demonstrated that this model effectively balances the trade-offs between objectives and benefits, achieving the lowest total logistics distribution cost while promoting sustainable low-carbon logistics. The research findings provide a theoretical foundation for optimizing logistics vehicle paths and formulating energy-saving and carbon reduction implementation plans in China.

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.

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.

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.

ECONOMIC GROWTH FORECAST MODEL URBAN SUPPLY CHAIN LOGISTICS DISTRIBUTION PATH DECISION USING AN IMPROVED GENETIC ALGORITHM

The proposed way of estimating the associated macroeconomic index based on a supply chain network is a novel strategy that might give useful insights for firms and organizations trying to enhance their supply chain logistics operations. To aid in this process, the suggested technique utilizes multiple regression analysis and adaptive extreme learning machine models to determine the relative importance of each indicator in the supply chain logistics decision-making process. Firms may be able to save expenses and boost economic value by employing enhanced genetic algorithms and mathematical modeling to develop a logistics distribution model for urban supply chains based on reconstructive genetic algorithms. It is possible that the paper's study of the issues plaguing the current distribution of logistics in metropolitan areas would prove valuable to organizations that are seeking to improve their own supply chain logistics procedures. This paper appears to take a thorough strategy that might assist assure the accuracy of the forecasting method by preprocessing macroeconomic indicators using imputation, classification, and other approaches to generate a time series consistency model. Some novel ways that may shed light on logistics in the supply chain include using a two-dimensional discrete mesh structure built on wireless sensors to depict the national economic development scenario and using a coding matrix to convey the extent of economic growth. A thorough and all-encompassing way that might aid organizations in making better judgments about their supply chain logistics strategies is to employ multiple regression analysis, an adaptive extreme learning machine model, and other approaches to examine the effect degree of each key indicator. Experiments demonstrating excellent and steady prediction accuracy of the algorithm model are encouraging and point to the possible usefulness of the suggested forecasting approach. The paper's contributions, including its analysis of supply chain logistics cost accounting, determination of the basic path optimization, improvement of the genetic algorithm, and design of the mathematical model of supply chain logistics distribution path decision, all look promising in terms of their potential to help businesses cut costs and boost economic value. The positive outcome of the successful design of the mathematical model demonstrates the possible efficacy of the suggested technique.

Optimization of low‐carbon cold chain logistics distribution path for agricultural products based on two‐stage algorithm

AbstractWith the development of market economy, cold chain logistics has become the mainstream of the current transportation industry. Reducing transportation costs and optimizing transportation routes from an environmentally friendly perspective is the main research focus. This study starts with an emphasis on environmental protection and cost savings and optimizes existing cold chain logistics expenses. Using the clustering and annealing algorithms, the path optimization model with the lowest cost is constructed and analyzed. The K‐means algorithm is utilized to cluster and partition logistics areas, and then optimized simulated annealing algorithm is used to control and utilize logistics costs and resources. The experimental results show that the optimized algorithm reduces costs by 11.36% and increases the loading rate of the vehicle by 11.95%. The delivery time has been reduced by 18.1%. The two‐stage algorithm can optimize and improve the path model, reduce transportation costs, improve cold chain transportation efficiency, and verify the feasibility of the model.

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.

RESEARCH ON THE OPTIMIZATION OF FRESH AGRICULTURAL PRODUCT COLD CHAIN LOGISTICS DELIVERY PATH

Due to the improvement of the economic level, people's living quality is getting higher and higher. Fresh agricultural products are part of our lives and closely related to each other. However, the cold chain logistics system in China is not yet mature. Fresh agricultural products deteriorate, causing severe waste. In order to improve this phenomenon, the cold chain logistics distribution path of fresh agricultural products should be optimized, the efficiency of cold chain logistics should be improved, and the delivery cost of fresh agricultural products should be reduced to meet people's demand for fresh agricultural products. This article analyzes the problems in the cold chain logistics distribution of fresh agricultural products and explores the optimization strategy of the supply chain logistics path of fresh agricultural products.

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.

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.

Forward and reverse courier logistics distribution path planning based on multi-objective optimization genetic algorithm

In order to optimize the express logistics distribution path and improve the distribution efficiency, the multi-objective optimization model of express logistics distribution path with mixed time windows is proposed. The model takes into account the requirements of express substations for on-time arrival, express enterprises' control of logistics costs, quantifying customer satisfaction with on-time arrival rate, and constituting logistics costs with fixed costs and transportation costs. Based on the multi-objective optimization genetic algorithm, the Pareto solution set is obtained, and the optimal solution is selected by combining four evaluation methods. The feasibility of the method is demonstrated by applying the method to the case of a region in North China.

Optimal selection algorithm of logistics distribution route based on machine vision

In order to improve the centralized planning ability of logistics distribution path data, improve the efficiency of logistics distribution and reduce the cost of logistics distribution, this paper proposes an optimal path selection algorithm based on machine vision. Using machine vision technology to calibrate the coordinates of logistics distribution path, combined with EMD decomposition method and wavelet denoising method to remove redundant data in logistics distribution data, particle swarm optimization algorithm to complete logistics distribution path planning, and ant colony algorithm to realize the optimal path selection of logistics distribution. The experimental results show that the average distribution cost of this method is only 766.7 yuan, the distribution time is less than 0.3 h, and the customer satisfaction is as high as 98%, which shows that this method can effectively optimize the distribution path.

Research on cold chain logistics optimization model considering low-carbon emissions

AbstractCold chain logistics distribution orders have increased due to the impact of COVID-19. In view of the increasing difficulty of route optimization and the increase of carbon emissions in the process of cold chain logistics distribution, a mathematical model for route optimization of cold chain logistics distribution vehicles with minimum comprehensive cost is established by considering the cost of carbon emission intensity comprehensively in this paper.The main contributions of this paper are as follows: 1) An improved hybrid ant colony algorithm is proposed, which combined simulated annealing algorithm to get rid of the local optimal solution. 2) Chaotic mapping is introduced in pheromone update to accelerate convergence and improve search efficiency. The effectiveness of the proposed method in optimizing cold chain logistics distribution path and reducing costs is verified by simulation experiments and comparison with the existing classical algorithms.

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.

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

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