Research on Joint Distribution Route Optimization of Cold-chain Logistics for Fresh Agricultural Products

Yang, Xuewen; Zhang, Zhiguo; You, Wei

doi:10.1088/1742-6596/1624/4/042032

Cited by 4 publications

(5 citation statements)

References 1 publication

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“…Mixed distribution means that enterprises establish their own distribution system, and large-scale distribution adopts outsourcing mode of small-scale distribution, which can be implemented by outsourcing distribution companies. Based on this situation, the mixed mode can give full play to the advantages of enterprises in short-distance and long-distance distribution, save costs, and improve enterprise efficiency [11]. In addition, the company can control the distribution of goods and adapt to changes and market demands in time.…”

Section: Basic Theorymentioning

confidence: 99%

“…Formula (10) indicates that all operations must be arranged in an orderly manner on a specific tractor. Formulas (11) to (12) represent time series limits for each operation, Formula (13) indicates that all operations must be completed within the working time available to the trac-tor, and Formulas ( 14) and ( 15) represent time window limits at start and end, respectively.…”

Section: Mathematical Modeling Of Vehicle Schedulingmentioning

confidence: 99%

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Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Liang

2022

Journal of Sensors

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Logistics distribution vehicle planning is an important issue in logistics transportation activities, and it is also a research hotspot in theoretical circles at home and abroad. At present, many studies have focused on establishing vehicle planning models and optimizing vehicle planning in different environments and have achieved rich results. As an important part of transportation production process, the efficiency of logistics distribution is very important to the whole production process. Especially for emergency logistics, every minute is very critical for emergency situations such as disaster relief. In order to improve the efficiency of emergency logistics, this paper applies multiagent technology to emergency logistics and puts forward an integrated modeling method of enterprise macromodeling, business process mesomodeling, and micromodel design. Using the agent-oriented system development method, an emergency logistics distribution vehicle planning model system is established. The development process of multiagent automatic trading system is described. The results show that it is feasible and effective to use multi-intelligent fuselage technology for emergency logistics distribution vehicle planning and decision-making. The algorithm proposed in this paper has advantages over the container order sequence processing scheme, and the total cost of order acceptance decreases sharply in the initial stage, which shows the practical convergence of the algorithm. The adjacency search method and Tabu search method deal with the calculation of total labor cost, and the Tabu neighborhood search algorithm obtains better results with lower labor cost.

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Section: Basic Theorymentioning

confidence: 99%

Section: Mathematical Modeling Of Vehicle Schedulingmentioning

confidence: 99%

Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Liang

2022

Journal of Sensors

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“…At the same time, with the progress of science and technology society, people also put forward higher requirements for logistics distribution services, the most important of which is the punctuality of logistics distribution [7][8]. Therefore, for this requirement, it is required that the logistics distribution path should be optimized as much as possible.…”

Section: Prefacementioning

confidence: 99%

“…In order to reduce the amount of parameters, all customers share the weight of one-dimensional convolution. Another one-dimensional convolution layer is used to map the global variable to the vector space of dimension , where the global variable is shown in equation (8).The dynamic and static elements contained in local information and global information learn their characteristics in two different convolution layers, and finally fuse them, just like multi-layer information fusion in supervised learning resnet50, and and are combined in a weighted linear way through the ReLU activation function, and finally the fusion information of customer node i is obtained. As shown in equation (9), it contains both global information and local information.…”

Section: Figure 4 Reinforcement Learning State Input Information Fusi...mentioning

confidence: 99%

Research on logistics distribution route optimization based on deep learning model and block chain technology

Yang¹,

Guan²

2023

3C Empresa

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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.

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“…China's agricultural products are not only rich in resources but also rank steadily among the top in the world in terms of production, but the problem of agricultural logistics has been plaguing agricultural players [ 1 ]. The so-called logistics refers to the flow process of material materials in physical form from the place of supply to the place of consumption in social reproduction [ 2 ].…”

Section: Introductionmentioning

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

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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.

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Research on Joint Distribution Route Optimization of Cold-chain Logistics for Fresh Agricultural Products

Cited by 4 publications

References 1 publication

Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Research on Emergency Logistics Vehicle Route Scheduling and Optimization Method Based on Multi-Intelligent Decision System

Research on logistics distribution route optimization based on deep learning model and block chain technology

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

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