Logistics Distribution Path Research Articles

Research on Multi-objective clustering Optimization of Logistics Distribution Line

In order to improve the ability of logistics distribution line optimization, a multi-objective clustering algorithm based on particle clustering is proposed. The maximum density sparse detection and adaptive optimization method are used to schedule the logistics distribution route. The particle swarm optimization (PSO) algorithm is used to construct the multi-objective optimization model of the logistics distribution line. The global optimization characteristics of the particle swarm optimization algorithm are used to optimize the logistics distribution route. PSO adaptive clustering method is used to realize multi-objective optimization of logistics distribution path. The simulation results show that the method has better performance in the optimization control of logistics distribution line, improves the efficiency of logistics, reduces the loss of logistics line, and improves the throughput performance of logistics line.

On-line Ship Route Planning of Cold-chain Logistics Distribution Based on Cloud Computing

Yu, X., 2019. On-line ship route planning of cold-chain logistics distribution based on cloud computing. In: Guido-Aldana, P.A. and Mulahasan, S. (eds.), Advances in Water Resources and Exploration. Journal of Coastal Research, Special Issue No. 93, pp. 1132–1137. Coconut Creek (Florida), ISSN 0749-0208.In order to improve the efficiency of cold chain logistics distribution in cloud computing environment, it is necessary to optimize the location of ship route in cold chain logistics distribution. An online planning algorithm of cold chain logistics distribution ship route in cloud computing environment is proposed based on the shortest path optimization control. In the cloud computing environment, the shortest path search model is used to optimize the ship route of cold chain logistics distribution and the location design of cold chain logistics distribution path node. Based on the load balancing model of distribution network, the ship route location and distribution route planning of cold chain logistics under cloud computing environment are carried out. The optimal distribution route control protocol is used to control the cold-chain logistics shipping route in cloud computing environment, and the minimum distribution route is used as the constraint cost. Realize the on-line planning algorithm optimization design of cold chain logistics distribution ship route under cloud computing environment. The simulation results show that under the cloud computing environment, the on-line planning route overhead of cold chain logistics distribution ship route is lower, the balance of distribution network is better, and the output of cold chain logistics distribution is high. Improve the efficiency of cold chain logistics distribution under cloud computing environment.

Logistics distribution path optimization research based on adaptive chaotic disturbance flies optimization algorithm

With the economic globalization and fast-changing of information technology, the logistics industry and production efficiency have greatly improved, and the logistics distribution have become an important part of the modern logistics industry. The selection of the distribution path in the logistics is the key issue because the choice of reasonable distribution route have great significance on increasing delivery speed, improving service quality, reducing distribution cost and increasing economic benefit. Aiming at this characteristic in the process of logistics distribution, this paper presents an adaptive chaotic disturbance flies optimization algorithm (ACD-FOA), which can optimize fruit flies algorithm in a state of convergence for global optimization. It also can overcome the shortcoming of flies optimization algorithm which is easy to fall into local faults in the solving process to some extent. Based on this advantage, the convergence speed, reliability and optimization on the accuracy of optimization algorithm are much better than the pure flies. Finally the instance simulation test have been carried out, which proved that the adaptive chaotic flies optimization algorithm has better ability of global optimization. In order to prove that the proposed adaptive chaotic disturbance fly optimization algorithm has higher optimization accuracy in logistics distribution, the results were compared with three other models, which indicates that the ACD-FOA established in this paper is more applicable to the current Logistics distribution path optimization. It also can find the optimal logistics distribution path quickly and reduce the logistics distribution cost effectively at the same time.

Road condition prediction and logistics distribution path optimization algorithm based on traffic big data

In view of the complex road conditions in today’s cities, the traditional prediction methods for road conditions are not so accurate, and the optimization algorithm for the logistics distribution path is not sensitive to changes in the road conditions so that its application in an actual logistics distribution system is not effective. This article proposes a road condition prediction and logistics distribution path optimization algorithm based on traffic big data. First, it analyses the characteristics of the road condition information of traffic big data. By combining the powerful feature extraction and self-learning ability of a deep belief network, it establishes a road condition prediction model based on a deep belief network and completes the model training and verification through the learning of traffic big data. Then, it combines the road condition prediction (result) information, traffic network information, and logistics distribution information to construct the time-share weighted traffic network. It then modifies the access set and pheromone variables of the ant algorithm based on the time-share traffic network to establish the road condition prediction and logistics distribution path optimization algorithm based on traffic big data. Finally, it conducts comparative experiments with other logistics distribution path optimization algorithms. The experimental results show that the proposed algorithm is superior to other logistics distribution optimization algorithms. Therefore, this algorithm is an effective method for optimizing logistics distribution.

MULTI-OBJECTIVE DISTRIBUTION ROUTING OPTIMIZATION WITH TIME WINDOW BASED ON IMPROVED GENETIC ALGORITHM

In order to solve the shortcomings of the traditional genetic algorithm in solving the problem of logistics distribution path, a modified genetic algorithm is proposed to solve the Vehicle Routing Problem with Time Windows (VRPTW) under the condition of vehicle load and time window. In the crossover process, the best genes can be preserved to reduce the inferior individuals resulting from the crossover, thus improving the convergence speed of the algorithm. A mutation operation is designed to ensure the population diversity of the algorithm, reduce the generation of infeasible solutions, and improve the global search ability of the algorithm. The algorithm is implemented on Matlab 2016a. The example shows that the improved genetic algorithm reduces the transportation cost by about 10% compared with the traditional genetic algorithm and can jump out of the local convergence and obtain the optimal solution, thus providing a more reasonable vehicle route.

Selection of Cold Chain Logistics Distribution Center Location Based on Improved Genetic Algorithm

With the rapid development of food refrigeration and freezing technology, food cryogenic storage and vehicle transportation scheduling technology, the cold chain logistics industry has entered a period of rapid development. According to the problem of urban cold chain distribution route, based on the vehicle distribution model with time window, the minimum cost of transportation, cost of energy, cost of goods, penalty cost is the objective function, and the urban cold chain logistics distribution path is established. Optimized mathematical model. According to the actual case, the analysis of the cold chain distribution model is carried out by using the analytical genetic algorithm, and the optimal combination of the distribution paths with the lowest total cost is obtained, which has certain reference significance for the urban cold chain logistics distribution route problem.

Study of logistics network optimization model considering carbon emissions

Carbon emissions trading is a mitigation of greenhouse important measures, as well as one of the hot researches. As large energy consumption, reducing emissions of carbon during the development of logistics plays crucial role in the development of low carbon economy. The establishment of a multi-level logistics network optimization model was divided into two stages: the first stage is to realize the optimization of each network node in the supply chain, and the second stage is about the optimization of logistics distribution path. Considering coefficient of carbon emissions and integrating it into the objective function, it used Lingo software respectively to analyze numerical of the benefits of whether considering carbon emissions or not in logistics network. The results of two cases show that the design of logistics network which considering carbon emissions of can effectively reduce logistics more significantly enhance the social benefit of logistics and it can also conform the basic state policy of sustainable development in other words. Finally, logistics network optimization model considering carbon emissions has more extensive practical significance.

Research on Kruskal Crossover Genetic Algorithm for Multi-Objective Logistics Distribution Path Optimization

To effectively optimize multi-objective logistics distribution path, the distance and distance related customer satisfaction factor are used as the objective function, a novel kruskal crossover genetic algorithm (KCGA) for multi-objective logistics distribution path optimization is proposed. To test the optimization results, the terminal distribution model and the virtual logistics system operating model are built. Experiment results show that, compared with basic genetic algorithm (GA), the run time of KCGA takes a slightly higher. But the average distribution distance and the best distribution distance are reduced by 6%-8%. Achieve the goal of multi-objective logistics distribution path optimization.

Logistics Route Optimization Based on Improved Fish Swarm Algorithm

In order to achieve a low cost and low exhaust pollution in logistics distribution path. In view of the shortages of existing genetic algorithm and ant colony algorithm which have the characteristics of some limitations, such as ant colony algorithm's convergence slow, easy going, the characteristics of such as genetic algorithm premature convergence in the process of path optimization, process complex, the paper proposed the improved artificial fish swarm algorithm in order to solve logistics route optimization problem. At last, through simulation experiment, the improved artificial fish swarm algorithm is proved correct and effective.

Research of Logistics Distribution Path Planning Based on Improved NSGA-II

In this paper, the path planning algorithm in logistics based on improved NSGA-II is considered which aims to make shortest transportation distance, the least used vehicles and the lowest transportation cost under the constraints of the road condition, the goods demand, the vehicle capacity and the transportation miles. First, the constraint-insert method is introduced to accelerate population initialization. Secondly, the density information is brought into Pareto sorting to maintain distribution uniformity. In the end, the sub-path reversal operation is designed to ensure the population diversity and the good individual inheritance. The simulation results show that the improved NSGA-II has better convergence effect and convergence value than the standard algorithm.

Research on Logistics Distribution Path Planning Algorithm Based on GIS

Algorithm of vehicle scheduling optimization could be integrated in the GIS platform. Therefore, distribution software can automatically make the delivery plan and managers also can make the optimizing choice of the optimal distribution route. Firstly, this paper introduces the necessity of introducing GIS into the logistics industry. Moreover, advantages and disadvantages of the current path planning in the logistics distribution which often used in some algorithms (genetic algorithm, mountain climbing algorithm, ant colony algorithm, etc.) will be listed. Finally, a more practical hybrid algorithm will be used to the GIS so that managers can optimize the logistics distribution path selection.

Agricultural Optimization of Cold-Chain Logistics Distribution Routing Based on PSO

The definition of the cold chain logistics and the problem of the agricultural cold chain logistics are introduced. According to the characteristics of cold chain logistics, exploring the cold chain logistics distribution path optimization method. Based on the model of the agricultural products cold chain logistics distribution routing optimization, particle swarm optimization algorithm is constructed to solve the optimization problem. Taking an example, the results have been conducted finally to demonstrate the effectiveness of the algorithm for agricultural products cold chain logistics distribution routing optimization problem.

Random Choice of Logistics Distribution Route Based on Dynamic Programming

It is important for logistics enterprise to choose the best logistics distribution route to improve efficiency. Dynamic programming is analyzed. Traffic jam factor of logistics distribution path is imported in the basic algorithm to modify the path value according to random distribution path condition. An example is used to verify the improved algorithm is practical.

The Research about Simulated Annealing Ant Colony Algorithm in Emergency Logistics Path Optimization

Emergency logistics distribution, as an important role, is the key to the whole logistics. Emergency logistics is mainly reflected in emergency. For the disaster, economic benefit is not the first thing to be thought about. High efficiency is the key to emergency logistics. This article concentrates on path optimization of emergency logistics distribution. Propose emergency logistics distribution path optimization model which considers timeliness as the first goal. Utilize Simulated Annealing Ant Colony Algorithm to search for optimization. Based on simulation, Simulated Annealing Ant Colony Algorithm owns better timeliness to solve emergency logistics distribution.