Logistics Distribution Cost Research Articles

Application of integrating reinforcement learning and intelligent scheduling in logistics distribution

Logistics distribution is an indispensable part of the modern economy and is crucial for ensuring the efficient operation of the supply chain. With the continuous progress of technology, the application of intelligent scheduling systems in the field of logistics distribution is becoming increasingly widespread. Reinforcement learning, as one of the hot technologies in the field of artificial intelligence, is gradually receiving attention in its application in intelligent scheduling. Reinforcement learning can continuously learn and predict the same thing to enhance memory, while intelligent scheduling requires continuous prediction and optimization of logistics distribution paths. In response to the current problems of slow logistics distribution efficiency and low customer satisfaction, this article analyzed the application of intelligent scheduling in logistics distribution from the aspects of basic data maintenance, basic data review, intelligent scheduling, scheduling result review, distribution information management, and vehicle tracking. By using reinforcement learning, the traffic network weight in logistics distribution was studied to improve logistics distribution efficiency and customer satisfaction. This article analyzed the efficiency of logistics distribution, vehicle tracking accuracy, vehicle scheduling ability, and logistics distribution costs under different logistics distributions. The results showed that the logistics distribution under the integration of reinforcement learning and intelligent scheduling reduced 12.047 km compared to traditional distribution paths, and its distribution cost decreased by 129.718 yuan compared to traditional logistics distribution costs. The efficiency of logistics distribution that integrates reinforcement learning and intelligent scheduling has significantly improved, with optimized distribution costs and paths. It also has a positive effect on improving the utilization rate of logistics distribution vehicles.

A customer satisfaction-based optimization model for the charging and discharging path and battery swapping stations’ site selection of electric vehicles

This paper studies the problem of considering customer satisfaction in the no-battery-swap mode and in the power-swap mode. First, with the goal of maximizing customer satisfaction, the total cost of charging and discharging and the minimum construction cost of swapping stations, the customer time window, and the load constraints of electric vehicles are considered. A model of electric vehicle charging and discharging route optimization and replacement station location without battery swapping behavior, considering customer satisfaction, is established, and then, a two-stage improved ant colony–genetic algorithm is designed to solve the model, and finally, the comparative analysis considers customer satisfaction. Based on the path optimization results and location decisions considering the cost of charging and discharging, the following conclusions are obtained: 1) electric vehicle route optimization and swap station location planning considering customer satisfaction can not only effectively reduce logistics distribution costs and replacement costs but also improve customer satisfaction levels. 2) Reducing the number of route crossings in the process of logistics distribution routes can save electricity costs for electric vehicles and logistics distribution costs, and help reduce the total cost of the entire logistics distribution network. 3) The gradient setting of the electricity price for electricity exchange will reduce the cost of electricity exchange, improve the utilization efficiency of the battery, reduce the cost of logistics and distribution, and improve the electricity exchange revenue of the electricity exchange station.

Optimization of Open Distribution Path for Rural Fresh Food under Genetic Algorithm

In recent years, as the market scale of China's cold chain logistics continues to expand, the use of fresh food e-commerce platforms such as Temu, Taocaicai, and Meituan Grocery has become more and more popular in rural areas. In order to reduce the cost of rural fresh logistics, this paper introduces the fresh electricity supplier "community group purchase" distribution mode, starting from improving the distribution efficiency of rural fresh logistics, and comprehensively considering the commodity demand of rural users, the freshness of fresh goods delivered to users, open route planning distribution vehicles and other factors. A capacity-constrained open vehicle routing optimization model with the goal of minimizing the total distribution cost was constructed and solved by genetic algorithm. This paper proves that the open vehicle distribution can improve the efficiency of logistics distribution more effectively, reduce the total cost of logistics distribution, and promote the development of rural fresh food market.

Deep Learning Algorithm Aided E-Commerce Logistics Node Layout Optimization Based on Internet of Things Network

INTRODUCTION: In recent years, e-commerce has shown a booming trend. Influenced by e-commerce, people's logistics needs have also increased sharply in recent years. OBJECTIVES: Research on the node layout and optimization of e-commerce logistics is conducive to improving the scientificity and rationality of logistics node layout, improving logistics distribution efficiency, reducing logistics distribution costs, and better meeting consumers' logistics needs. However, due to the unreasonable layout of logistics nodes in some areas, it has brought huge logistics cost investment to e-commerce companies, and also laid hidden dangers for the long-term development of e-commerce companies. METHODS: Based on this, this paper studied the node layout and optimization of e-commerce logistics by using IoT big data and deep learning algorithms, and proposed an improved logistics node layout scheme based on IoT big data and deep learning algorithms. The experimental research was carried out from five aspects: the transportation cost of logistics, the efficiency of logistics distribution, the accuracy of logistics information transmission, the location and traffic conditions of logistics nodes, and the evaluation of the plan by e-commerce enterprises. RESULTS: The research results showed that the improved logistics node layout scheme can improve the efficiency of logistics distribution by 3.69% and the accuracy of logistics information transmission by 4.34%, and can reduce the logistics transportation cost of e-commerce enterprises. CONCLUSION: The node locations selected by the improved logistics node layout scheme are more reasonable, and e-commerce companies have higher evaluations of the improved logistics node layout scheme.

Optimization Model and Algorithm of Logistics Vehicle Routing Problem under Major Emergency

The novel coronavirus pandemic is a major global public health emergency, and has presented new challenges and requirements for the timely response and operational stability of emergency logistics that were required to address the major public health events outbreak in China. Based on the problems of insufficient timeliness and high total system cost of emergency logistics distribution in major epidemic situations, this paper takes the minimum vehicle distribution travel cost, time cost, early/late punishment cost, and fixed cost of the vehicle as the target, the soft time window for receiving goods at each demand point, the rated load of the vehicle, the volume, maximum travel of the vehicle in a single delivery as constraints, and an emergency logistics vehicle routing problem optimization model for major epidemics was constructed. The convergence speed improvement strategy, particle search improvement strategy, and elite retention improvement strategy were introduced to improve the particle swarm optimization (PSO) algorithm for it to be suitable for solving global optimization problems. The simulation results prove that the improved PSO algorithm required to solve the emergency medical supplies logistics vehicle routing problem for the major emergency can reach optimal results. Compared with the basic PSO algorithm, the total cost was reduced by 20.09%.

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.

Site-selection method of agricultural products logistics distribution centre based on blockchain

In order to overcome the problems of low on-time delivery rate and high distribution cost existing in the existing location methods of agricultural products logistics distribution centre, this paper proposes a new location method of agricultural products logistics distribution centre based on blockchain. Based on the analysis of the basic problems affecting the location of agricultural products logistics distribution centre, combined with the blockchain technology, the location model of agricultural products logistics distribution centre based on input-output ratio was constructed. Combining the idea of mountain climbing algorithm and particle swarm optimisation algorithm, the hybrid particle algorithm is used to solve the location model of agricultural products' logistics distribution centre, and the optimal location scheme is obtained. The experimental results show that the proposed method can effectively improve the on-time delivery rate, customer satisfaction, and reduce the logistics distribution costs. The maximum on-time delivery rate is 97.4%.

Control Optimization Design of Radio Frequency Identification Technology in IoT Express Logistics Distribution System

In order to solve the problems of high consumption cost and long transportation and distribution cycle of cross-border logistics, a control optimization design method of radio frequency identification technology in the express logistics distribution system of the Internet of Things is proposed. The details of the method are RFID positioning technology, laser ranging technology, RFID and laser fusion positioning feasibility analysis, and moving target state estimation. The experimental results show that when the phase shift threshold φ and the included angle of the antenna are 90°, the error can be reduced to 0.36 m, and the recognition rate can be increased to 94.8%. The simulation results show that, on the premise of meeting the customer’s expectation of timeliness, the actual logistics distribution cost is significantly lower than the customer’s expected logistics distribution cost, which verifies the effectiveness of the proposed method.

Research on the site selection and path layout of the logistics distribution center of marine ships based on a mathematical model

For logistics enterprises, site selection and path layout are related to the cost and efficiency of distribution, which is a very critical issue and has an important impact on the development of enterprises. Compared with land logistics, the cost of marine ship logistics is higher due to the high cost of ships, so the research on the location and path layout of its distribution centers is also particularly important. This paper established a two-layer model under the assumption that unit transportation costs and administration expenses are known for the site selection and path layout problems of marine ship logistics distribution centers. Corresponding constraint conditions were set. The upper layer was the optimization model of the site selection problem of the distribution center, and the objective function was to minimize operating and construction costs and was solved using the quantum particle swarm optimization (QPSO) algorithm. The lower layer was the optimization model of the distribution path layout, and the objective function was to minimize the logistics distribution cost and was solved using the ant colony optimization (ACO) algorithm. The model was veri-fied through an example analysis. It was assumed that there were three ships, five candidate distribution centers, and ten customer points. The model was solved in MATLAB software. The results of the example analysis showed that com-pared with K-means, genetic algorithm (GA), and particle swarm optimization (PSO)-ACO algorithms, the QPSO-ACO algorithm had the shortest running time, about 60 s, which saved about 50% compared to the K-means algorithm. The optimal cost of the QPSO-ACO algorithm was 293,400 yuan, which was significantly lower than the K-means, GA, and PSO-ACO algorithms (459,600 yuan, 398,300 yuan, and 357,700 yuan). In this example, the site obtained by the QPSO-ACO algorithm was distribution center 2, and the obtained path distribution was 1-7-5-4, 2-6-3, and 10-8-9. The results verify the effectiveness of the QPSO-ACO algorithm in solving the problem of site selection and path layout. The QPSO-ACO algorithm can be applied in the actual marine ship logistics.

The Optimization of Distribution Path of Fresh Cold Chain Logistics Based on Genetic Algorithm.

The products of the enterprise are the logistics objects of the enterprise. Therefore, the company's products are the primary factor affecting logistics costs. The products of different enterprises may be different in terms of the type, nature, volume, quality, and physical and chemical properties of the products, which will have different impacts on the cost of logistics activities such as warehousing, transportation, and material handling of enterprises. More and more enterprises have taken the cost of logistics distribution as one of the important indicators affecting the development of enterprises; especially, cold chain logistics has been rapidly developed and valued in recent years, because the requirements of such products for delivery time, distribution efficiency, and distribution environment are very strict, whether the goods can be distributed in a standard and reasonable environment and whether the delivery vehicle can deliver the goods within the time specified by the customer have greatly affected the safety of frozen and refrigerated food. Therefore, this paper reduces the cost of the distributor through the optimization of the distribution path of cold chain logistics and makes the goods distributed can be delivered to the customer faster and more reasonably by establishing an integrated optimization platform, which is of great significance for how to reduce the cost of enterprises. Therefore, this paper starts from the function with the lowest distribution cost as the goal, comprehensively considers the specific characteristics of cold chain logistics, given the relevant constraints, uses the improved genetic algorithm to iterate on the given scheme, sends the improved new scheme to the simulation software for simulation operation, then sends the results obtained by the operation to the genetic algorithm for the next iteration, and repeats it in turn until the prespecified conditions can be terminated. Therefore, this paper summarizes some problems and development status in cold chain logistics and distribution routes by consulting relevant literature, optimizes the scheme by using VRP model combined with constraints, establishes a distribution system model, and finally verifies and analyzes to obtain a more reasonable and satisfactory solution. The innovation of this paper is that the research on the VRP problem is optimized through an improved genetic algorithm, certain improvements have been made in the coding method and the operation of selection, crossover, variation, etc., and the improved genetic algorithm can greatly reduce the number of program iterations. Then, we use the integrated optimization platform to import the solution into FlexSim for simulation, each simulation of the new solution will be transmitted to MATLAB through the Excel table for the next optimization iteration, and we repeat the above steps until the preset conditions are met after the termination. This would lead to a more realistic and satisfactory solution.

Application of the Logistics Management System Based on Cloud Computing Technology

In order to solve the vehicle routing optimization problem of cold chain logistics distribution, this paper proposes a vehicle routing optimization method of cold chain logistics distribution based on cloud computing technology. This paper analyzes the advantages of processing the real-time routing of distribution vehicles under the cloud computing mode and establishes the application service architecture of vehicle routing optimization in cold chain logistics; under this architecture, multisource real-time traffic information is obtained, the vehicle distribution time and comprehensive cost are analyzed, the vehicle routing optimization model of cold chain logistics distribution is constructed, and the coarse-grained parallel genetic algorithm is used to solve the model in the cloud computing environment. The experimental results show that if W 1 = 0 , W 2 = 1 , the optimal path is 0-6-7-3-1-4-2-8-5, the total cost is 1620 yuan, the total mileage is 72 km, the vehicle driving time is 2.17 hours, and the calculation time is 10.79 s; let W 1 = 1 , W 2 = 0 , the optimal path is 0-5-6-7-2-4-3-1-8, the vehicle travel time is 1.84 hours, the total cost is 1736 yuan, the total mileage is 76 km, and the calculation time is 11.56 s. The optimal path is obtained in the effective time range. Conclusion. The real-time route optimization method of refrigerated vehicles in the cloud computing environment is effective. This method has practical significance to realize fine control of cold chain logistics distribution cost and improve distribution service efficiency.

Cold Chain Logistics UAV Path Optimization Enlightenment

As the market size of cold chain logistics in China grows year by year, consumers have significantly improved their requirements for the quality of cold chain items. Meanwhile, national laws and policies supporting the development of cold chain logistics have been introduced successively, which makes the field related to cold chain logistics develop rapidly. However, there are some problems in the development of the cold chains, such as waste of logistics data supervision and high cost of logistics distribution. Especially in the critical situation of the epidemic, the "last mile" delivery without contact is advocated, and the transportation time of cold-chain vehicles is long and the cost is high, which causes great resistance to solving the "last mile". This paper puts forward the corresponding enlightenment according to the relevant literature.

Two-Stage Vehicle Routing Optimization for Logistics Distribution Based on HSA-HGBS Algorithm

Aiming at the problems of complex urban road network, low efficiency of logistics distribution, and the difficulty of large-scale logistics distribution area division and routing planning, this paper proposes a two-stage logistics distribution vehicle routing optimization (VRP) method based on the establishment of a multi-factor complex road network constrained logistics distribution mathematical model. Considering the complex traffic elements and road network topological structure in logistics and distribution, in the first stage, a heuristic simulated annealing (HSA) distribution region partitioning algorithm is proposed with the objective of balancing vehicle task load to divide the urban logistics distribution network under complex road networks, so as to reduce the area scale and path search cost. In the second stage of route decision making, aiming at minimizing the total cost of logistics distribution, combining the VRP problem with complex road network conditions, a heuristic path search method combined with complex road network model constraints is proposed. In this stage, a hybrid genetic beam search(HGBS) algorithm is used to plan the path nodes, reduce the randomness of the model in the initial search for paths by heuristic genetic algorithms, then combine with Beam graph search methods to reduce the space and time used for the search, and use optimization algorithms to improve the accuracy of independent sub-region routing optimization and the rationality of overall physical distribution route selection. Finally, the proposed method is validated in this paper with two practical cases. The experimental results show that the two-stage decision-making algorithm proposed in this paper has certain advantages in partitioning schemes, minimizing total cost and iteration times. Through comparison, the optimization ability of this method for logistics distribution networks is proved.

Logistics Distribution Location Algorithm Based on Improved Imperial Competition Algorithm

To solve the problems of warehouse explosion and delay of logistics distribution network under the sudden explosion of demand on “double 11” and “618,” this paper proposes a logistics distribution network location algorithm that can consider reliability, green environmental protection, and path optimization. Firstly, the transportation model of logistics distribution network location-route optimization is established. Under the condition that the transportation model satisfies the vehicle path reliability constraint, it can minimize the total cost, including logistics distribution cost, transportation oil consumption, and CO2 emission cost. This paper designs an improved imperial competition algorithm to solve it according to the characteristics of the transportation model. Firstly, the competition mechanism of “United Lian Heng” was introduced in the initial national stage, enhancing the information exchange and retaining the superior population. Secondly, in the process of empire assimilation, we can learn from the colonial rule strategy, which is gradually infiltrated and assimilated by all levels of the country to enhance the development ability of the algorithm. Finally, the algorithm designs a mechanism to judge and jump out of local optimum, so as to avoid “premature” affecting the optimization performance. The rationality of the model and the effectiveness of the improved imperial competition algorithm are verified by simulation experiments of different scales, while the influence of reliability level is analyzed. Experimental results show that the proposed method can effectively solve problems of different scales and maintain stable performance under different reliability levels. Moreover, its algorithm performance is better than that of the standard imperial competition algorithm.

An optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window

In order to improve the distribution efficiency of cold chain logistics and reduce the distribution cost, an optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window is constructed. According to the complexity of cold chain logistics network, a multi-objective optimization model of cross-docking scheduling of cold chain logistics vehicle routing with fuzzy time window is established. In order to ensure the lowest total cost of cold chain logistics distribution and improve the overall customer satisfaction with service time, the Drosophila optimization algorithm is used to solve the model to obtain the optimal vehicle routing of cross-docking scheduling optimization of cold chain logistics. The simulation test results show that: after the application of the model, the cold chain logistics distribution time is significantly shortened, the distribution cost is significantly reduced, the damage cost is reduced, the carbon emission of vehicles is reduced, and the economic and low-carbon benefits are significantly improved, which can be used as an effective tool to solve the cross-docking scheduling optimization problem of cold chain logistics.

Research on Optimization of Joint Distribution of Cold Chain Logistics Adopts Carbon Emission

Aiming at high energy consumption, high carbon emission and high cost of cold chain logistics distribution. In this paper, an optimization model of cold chain joint distribution terminal distribution path with carbon emission is established, and the model takes fixed cost, carbon emission cost, transportation cost, damage cost and penalty cost with time window into consideration, and optimizes the total distribution cost. In this paper, genetic algorithm is used as the optimization algorithm to solve the model, and combined with concrete examples to demonstrate.

The Impact of GIS/GPS Network Information Systems on the Logistics Distribution Cost of Tobacco Enterprises

The improvement of logistics operation management for many tobacco companies highly depends on the reduction of distribution costs. In order to optimize routes, cigarettes are delivered through online real-time distribution system equipped with GIS/GPS. These network information technologies are crucial to creating and maintaining an efficient distribution system. Considering the applicability and flexibility of GIS/GPS network information system, this paper assesses the impact of its implementation on the logistics distribution cost from both overall and household levels. Specifically, DID and pseudo-DID models were adopted and monthly panel data of 14 prefecture-level branches of Hunan Tobacco Company during 2016-2019 were used for regression.We find that the implementation of GIS/GPS network information systems increased the total distribution cost. However, the average distribution cost was significantly reduced at per household level as the customer base expanded. A series of robustness and sensitivity tests supported above results. Furthermore, the implementation of GIS/GPS network information system is beneficial to service quality and employee performance. In addition, more time and logistics costs were saved when drivers chose “dynamic + fixed optimization” route model.

Research on the Optimization Method of Regional Logistics Distribution Integration Based on Intelligent Information Processing

At present, the integration method of logistics distribution does not exchange regional logistics distribution information, the related logistics distribution information is not timely understood, and the integration effect of regional logistics distribution is poor, resulting in long distribution time, high distribution cost and small distribution scope. Therefore, the intelligent information processing technology is introduced to optimize the regional logistics distribution integration method. This paper established a regional logistics distribution integration mechanism which is able to process logistics distribution integration information through hierarchical authorization, transform the business data between the upstream and downstream enterprises in the supply chain of the regional logistics canter and integrate vehicle resources and human resources to optimize regional logistics distribution based on intelligent information processing. Taking the distribution time, distribution cost and distribution distance as experimental indicators, the experiment is carried out. The results show that the logistics distribution time, cost and distance of the proposed integrated optimization method are better than those of the traditional methods.

Research on Optimization of Food Cold Chain Logistics Distribution Route Based on Internet of Things

with the Rapid Development of Social Economy and the Continuous Improvement of People’s Living Standards, People Pay More and More Attention to the Quality of Life, and Put Forward Higher Requirements for the Quality of Products, Especially the Quality of Cold Chain Products. as a Guarantee of the Quality of Cold Chain Products, Cold Chain Distribution Has Received the Attention of Enterprises. Based on the Technology of Internet of Things, This Paper Establishes a Path Optimization Model for Cold Chain Logistics by Adding Refrigeration Cost and Cargo Loss Cost to the Traditional Path Optimization Model. the Experimental Results Show That the Real-Time Route Optimization Method for Refrigerated Vehicles under the Internet of Things Environment is Effective. the Method Has Practical Significance in Achieving Fine Control of Cold Chain Logistics Distribution Costs and Improving Distribution Service Efficiency.

Innovation Mode and Optimization Strategy of B2C E-Commerce Logistics Distribution under Big Data

With the advent of big data era and rapid development of Internet technology, e-commerce has had a strong development tendency that causes many problems, such as redundant and complex business processes, low efficiency and a high cost for e-commerce logistics in the distribution sector. It is not difficult to conclude that the key to improving logistics distribution efficiency—and reduce logistics distribution costs—is to optimize logistics distribution under big data. In this study, the management model, influence factors and development status of B2C e-commerce logistics distribution under big data are analyzed in detail. Then big data processing, business process and route optimization strategies for B2C e-commerce logistics distribution under big data are deeply studied. Furthermore, an optimization model of product sales and logistics distribution of B2C e-commerce by big data platform is discussed in order to propose an innovative optimization strategy for B2C e-commerce logistics distribution under big data. Big data technology is applied in B2C e-commerce logistics business management, which is studied in detail. These findings achieve the optimal distribution of B2C e-commerce, reduce the B2C e-commerce logistics distribution cost and improve the B2C e-commerce logistics distribution efficiency under big data. In addition, enhanced competitiveness of B2C e-commerce logistics distribution is examined in this study. This study provides a reference for follow-up big data studies in the field of e-commerce.