Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

Xiong, Haiou

doi:10.1155/2021/6623563

Cited by 26 publications

(20 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Here, p ω represents the severity of the failure (the weight can bring a good number for the maximum value of the target job). Combined with the actual policy, p ω is 1000 [ 13 ].…”

Section: Logistic Distribution Path Optimization Based On Genetic Alg...mentioning

confidence: 99%

See 1 more Smart Citation

Logistics Distribution Route Optimization Based on Genetic Algorithm

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Aiming at the problem of logistic division based on genetic algorithm, it is planned to study the improvement of logistic distribution methods. We first meet the requirements of the genetic algorithm of logistic development, use the division method to divide the delivery area of the gene, and formulate a functional delivery plan, which generally includes weight measurement, measurement time, customer value measurement, instrument measurement time, and the whole process index. We set weight goals and find the best way to improve genetic algorithm delivery. The experimental comparison results show that the optimal method takes less than 2 minutes to find the optimal method, while the normal process takes 4 minutes to find the optimal method, and the longest can reach 5 minutes. The comparison shows that the traditional algorithm takes longer to find the correct way than the algorithm developed this time. Finally, the simple logistic distribution optimization method model and the soft time-limited logistic distribution processing optimization model are calculated and simulated by the genetic testing algorithm and genetic algorithm development. The effectiveness of the improved genetic algorithm in local research and the effectiveness of the logistic transportation allocation solution are determined.

show abstract

“…Here, p ω represents the severity of the failure (the weight can bring a good number for the maximum value of the target job). Combined with the actual policy, p ω is 1000 [ 13 ].…”

Section: Logistic Distribution Path Optimization Based On Genetic Alg...mentioning

confidence: 99%

“…According to the functions and weights of the above logistic distribution indicators, the distribution path is optimized, as shown in equation (13).…”

Section: Genetic Algorithm Logistic Distribution Pathmentioning

confidence: 99%

Logistics Distribution Route Optimization Based on Genetic Algorithm

Liu

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…In order to improve the robustness and the performance in handling complex computation, Blackmore et al have proposed a disjunctive program to optimize the path planning algorithm, which utilizes the existing constraint methods to form an optimal path, and such method operates directly in the workspace and reduces a lot of costs [6]. Xiong et al have pointed out that the distribution path is the key problem of cold chain distribution so as to ensure goods to be delivered in time and to reduce the cost as much as possible, but the search time of traditional path planning is too long, thus, they have proposed a cold chain distribution routing optimization scheme based on improved ant colony algorithm which can successfully obtain the optimal distribution path of the enterprise within a predefined time [7]. Zhai et al have proposed an effective path planning method through the porous structure, generalized Tyson polygon method is also used to find the subregion, and then genetic algorithm (GA) is used to solve the traveling salesman problem, traverse the path of each subregion, merge the subregions and improve the calculating efficiency [8].…”

Section: Traditional Path Planningmentioning

confidence: 99%

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

Nai¹,

Yang²,

Lin³

et al. 2022

Journal of Mathematics

View full text Add to dashboard Cite

The transportation system of those countries has a huge traffic flow is bearing great pressure on transportation planning and management. Vehicle path planning is one of the effective ways to alleviate such pressure. Deep reinforcement learning (DRL), as a state-of-the-art solution method in vehicle path planning, can better balance the ability and complexity of the algorithm to reflect the real situation. However, DRL has its own disadvantages of higher search cost and earlier convergence to the local optimum, as vehicle path planning issues are usually in a complex environment, and their action set can be diverse. In this paper, a mixed policy gradient actor-critic (AC) model with random escape term and filter operation is proposed, in which the policy weight is both data driven and model driven. The empirical data-driven method is used to improve the poor asymptotic performance, and the model-driven method is used to ensure the convergence speed of the whole model. At the same time, in order to avoid the model converging local optimum, a random escape term has been added in the policy weight update process to overcome the problem that it is difficult to optimize the non-convex loss function, and the random escape term can help to explore the policy in more directions. In addition, filter optimization has been innovatively introduced in this paper, and the step size of each iteration of the model is selected through the filter optimization algorithm to achieve the better iterative effect. Numerical experiment results have shown that the model proposed in this paper can not only improve the accuracy of the solution without losing the accuracy but also speed up the convergence speed and improve the utilization of data.

show abstract

“…Wang studied the effect of temperature changes in the quality of frozen vegetables, etc., during cold chain logistics distribution, providing a corresponding theoretical basis for reducing the losses in the distribution process of agricultural products [ 19 ]. The Xiong design column generation method solves the vehicle distribution problem for larger demand conditions, where the pricing problem is determined by a local search algorithm [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Exploration of Joint Optimization and Visualization of Inventory Transportation in Agricultural Logistics Based on Ant Colony Algorithm

Dong

Duan

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The problem of joint optimization of inventory and transportation in agricultural logistics and distribution is a typical logistics problem, but agricultural logistics and distribution also have their own characteristics, such as uneven distribution of outlets, complex road conditions, very many outlets, a single order with few goods but high frequency of ordering, centralized distribution, and unified channels. To promote the sustainable development of the economy, it is necessary to save energy and reduce emissions, and eventually enter a new era of “low consumption, low pollution, and low emissions.” Modern logistics vehicle-scheduling process is complex and changeable, and the existing mathematical methods are not perfect in solving this problem, lacking scientific theory as a guide. The joint optimization problem introduces the inventory change factor on the basis of periodic vehicle path optimization and optimizes the inventory decision and path planning in an integrated manner. As a system to support the logistics industry, the visualized logistics information system is capable of video viewing and querying logistics information. In order to reduce gas emissions and save costs, it is necessary to optimize the transportation link, and the focus of optimization is the route optimization of distribution vehicles. Ant colony algorithm (ACA) is an emerging search and optimization technique, which emerged from the research of ACA. In this study, we study the joint optimization and visualization of inventory transportation in agricultural logistics based on ACA. In addition, the experimental results show that the inventory cost/total cost of improved ACA is 0.006 when the unit mileage transportation cost is 10, and the IBM ILOG CPLEX is 0.031, which is reduced by 0.0025, that is to say, in the case of high inventory cost per unit product, the use of improved ACA can lead to a significant reduction in inventory costs. Therefore, it can realize the whole process of control, traceability, and dynamic optimization to ensure the timeliness of emergency finished food security and provide real-time information for decision-making in command as well.

show abstract

Research on Cold Chain Logistics Distribution Route Based on Ant Colony Optimization Algorithm

Cited by 26 publications

References 19 publications

Logistics Distribution Route Optimization Based on Genetic Algorithm

Logistics Distribution Route Optimization Based on Genetic Algorithm

A Vehicle Path Planning Algorithm Based on Mixed Policy Gradient Actor‐Critic Model with Random Escape Term and Filter Optimization

Exploration of Joint Optimization and Visualization of Inventory Transportation in Agricultural Logistics Based on Ant Colony Algorithm

Contact Info

Product

Resources

About