# Logistics Optimization Strategy Based on Deep Neural Framework

Authors: Guangqian Liu; Yiming Zhu
Journal: Computational Intelligence and Neuroscience
 (2022)

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## Abstract

We propose a logistics optimization method based on improved graph convolutional networks to address the current problem of low product delivery rate and untimely product delivery during the peak period of e-commerce activities. Our method can learn excellent planning strategies from previous data and can give the best logistics strategy in time during the peak logistics period, which improves the product delivery rate and delivery time of logistics and greatly enhances the return on investment. First, we add a tensor rotation module to the graph convolution layer to better capture the global features of logistics nodes. Then we add inception structures in the temporal convolution layer to build multiscale temporal convolution filters to obtain temporal information of logistics nodes in different time-aware domains and reduce arithmetic power. Finally, we cooperate with e-commerce platforms to adopt logistics data as the experimental database. The experimental results show that our method greatly accelerates the logistics planning speed, improves the product delivery rate, ensures the timely delivery of products, and increases the return on investment.


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## Body

## 1. Introduction

For the economic development of the country, efficient logistics of the entire economic flow of the joint, for the company and the country, the mobility of logistics will determine the speed of development of the regional economy [1]. The efficiency of logistics is determined by the material distribution network and labor costs, the complex distribution network depends on a perfect infrastructure, the operation of the distribution network of universities is limited by labor costs, and how to integrate the distribution network and labor costs in a hierarchical way becomes the first task of logistics optimization [2]. In addition, for permanent logistics facilities, it is necessary to do an in-depth inspection to ensure the smooth operation of the basic links. When considering labor costs, a reasonable labor cost costing research report needs to be given based on the current labor cost environment. For storage costs, cost risks such as warehouse rent and storage management costs need to be considered in due course [3]. The development of technology has brought the convenience of information data, and while efficiently updating the warehouse information data, it is necessary to suitably complete the purchase and update of equipment to ensure the continuity between equipment and logistics information [4, 5]. The integration and optimization of logistics information can improve production efficiency, reduce costs, ensure efficient output of products, ensure the updating and iteration of warehousing, and avoid the problem of product backlog [6–8]. Logistics optimization needs to focus on logistics information in addition to the integration of logistics storage and transportation data. Common features also exist between the two, and common features between logistics data need to be shared and screened in an adaptive manner [9]. For the management of storage and transportation data, it is necessary to start from the supply chain side, with decision making and supporting operations as key aspects to optimize the management application details [10].The supply chain side has the most complete product analysis information and also encompasses the warehouse update sequence. The supply chain data are integrated through digital information technology. The network nodes of logistics supply are collected according to the transmission and feedback of data flow to better monitor the progress of logistics [11]. The efficiency of information management determines the product updates at the supply chain end, and only with the complete product information of the end supply chain can the efficiency of logistics be controlled efficiently [12]. Information management at the supply chain end can ensure the timeliness of logistics, and in addition to that, security management is also an integral part. The operation data at the supply chain end are a confidential document for a company, and the leakage of data may cause bad industry competition and huge economic loss. Therefore, information security management at the supply chain end is an essential part [13, 14]. The integration between product receipt and delivery and logistics information at the supply chain end needs to be coordinated by an integrated intelligent management system, and all data sources should be centralized with the incoming and outgoing products [15]. To solve the data discrepancy between the supply chain end and the logistics end, the literature [16] used an information mapping method to correspond the acquired product information from the time and location level to the logistics end, which ensures the accuracy of intelligent logistics information management. With the excess supply in the market and the gradual diversification of customer needs, to ensure that each customer's needs can be properly met. The literature [17, 18] conducted research on associated products based on customer needs, and the research reports covered areas such as automation equipment and transport vehicle models. The previous method of product delivery has failed to keep up with the speed of economic development, and the current method of product delivery is gradually becoming network delivery, from product ordering, proofreading, and payment to product packing, shipping, dispatching, and signing for delivery. All the time costs incurred during this period will become a necessary consideration for logistics management [19]. Delayed shipments and product delivery failures due to uncontrollable factors also require corresponding contingency plans as an alternative. Giving maximum freedom to the intelligent logistics management system is the basic issue of logistics management today. Besides, in the face of promotions like shopping carnivals, the maximum upper carrying capacity of logistics should be fully considered, and small price adjustment behaviors can be used to balance the short bursts of growth so as not to paralyze logistics [20].Logistics information management is supported by the Internet of Things (IoT) technology, which perfectly connects the end and the beginning of logistics. In addition, the intervention of IoT technology changes the traditional logistics operation mode, greatly reduces labor costs, accelerates the update of logistics information, and improves the delivery rate and economic efficiency of products. Considering the transition between traditional logistics mode and IoT logistics mode, it requires the support of basic communication facilities and the creation of a logistics node network, which is a huge project. The cost of this piece of overhead in the cost of construction is relatively large, which also has a certain risk for investors. At present, pilot validation has been taken only in some areas of developed cities. The validation results exceeded expectations and considering the huge project to be rolled out nationwide again, the flexibility and adaptability of the manufacturing layout need to be fully taken into account. It also needs to be properly optimized according to the consumer demand, and the assimilation effect brought by geographical differences should be fully considered so that the distribution and production of products under the IoT base can become more efficient and flexible. Before developing a logistics strategy, companies need to conduct a detailed assessment of their marketing strategy so that it can be adapted to the consumer habits of today. The key aspect of product economy is the familiarity with customers. Customers’ buying habits and consumer preferences can set the general direction for the marketing strategy and also provide a general direction for logistics planning. If the marketing plan is formulated wrongly, it will cause a chain reaction of product stagnation.To solve the drawbacks of the traditional logistics model and the shortcomings of the path planning algorithm, we are inspired by the graph convolutional network, we consider the logistics nodes as graph nodes in the graph convolutional network, and use the learning principle of the graph convolutional network to learn the logistics strategy. We then proposed an improved graph convolutional network logistics optimization method. The method improves the product delivery rate and delivery time of logistics, which greatly improves the return on investment and speeds up logistics planning.The remainder of this paper is laid out as follows. Section2 describes the research related to logistics optimization. Section 3 details the principles and implementation procedures related to the improved graph convolutional logistics optimization network. Section 4 presents the relevant experimental data sets and analysis of the results. Finally, Section 5 reviews our findings and reveals some additional research.


## 2. Related Work

In logistics management, logistics tracking can ensure that the products are within the monitorable range, and it is a common visualization effect to depict logistics trajectory feedback to the monitoring end through logistics tracking. Considering a large number of logistics trajectories need to be unified to evaluate individual management. The literature [21] proposes a trajectory data warehouse to integrate and store all logistics trajectory data into a specified information warehouse with correct labels, which corresponds to the product information in the supply chain object database. To rationalize the layout of the vehicle carrying capacity and product inventory in logistics transportation, literature [22] proposes an automatic guided vehicle system, which can operate between different product warehouses with route planning algorithms and traffic control systems, making the efficient in guided vehicle product transportation greatly improved, and also improving the rate of warehouse product receipt and delivery and the rate of warehouse material movement, solving the problem of logistics planning due to rationalize the layout of the vehicle carrying capacity and product inventory in logistics transportation. The literature [23] proposes an automatic guided vehicle system, which can operate between different product warehouses with route planning algorithms and traffic control systems, making the efficiency in guided vehicle product transportation greatly improved. It also improves the warehouse product receipt and delivery rate and the movement rate of warehouse materials and solves the warehouse backlog problem arising from poor logistics planning timeliness.With the gradual internalization of product delivery, the speed and traceability of logistics are gradually becoming the expectations of customers. The introduction of intelligent network technology allows intelligent logistics management to be realized. As new Internet business models become popular, the logistics model does not stay at the level of delivering products. The new logistics intelligent and optimized management system coordinates three areas: product distribution, material storage, and adaptive freight planning. The establishment of the Internet of Things nodes integrates logistics resources, and the optimization process of the optimization management system in the information system, solving the problem of path planning, storage material turnover, product and customer information matching, and nondifferentiated proximity distribution, can advance the intelligent logistics management system to a new height [24].The most important thing in the intelligent process of logistics and distribution is the sharing of orders and the delivery of products. Order sharing is to obtain the order content from the client and share the information with the merchant and logistics party. Product delivery is to check and pack the products according to the order content, and then pass the product labels to the automated distribution system, which will plan the product line according to the product volume and weight, and plan the products to the appropriate vehicles for loading and delivery. When the product orders are delivered according to the normal path [25], the production plant and logistics nodes will use the orders as the basis of big data information, collect all the traffic orders in real time, and provide data support for the optimization of the path of subsequent orders based on this data information. In the case of order modification, the speed of updating the order can provide time for the subsequent product packaging. At the same time as order modification, real-time multidimensional information needs to be obtained to analyze the material trajectory and storage capacity of the nearest logistics node. With the premise of reducing losses, the time cost and product loss generated by modifying orders are coordinated as much as possible.The presentation of logistics intelligence is concentrated on a visual logistics platform, as shown in Figure1. Sensor technology, satellite positioning technology, computer vision, and deep learning technology are integrated. The basic positioning facilities of logistics nodes are upgraded to be able to obtain accurate positioning data. The logistics visualization platform can realize the end-to-end operation process of logistics, refined into six specific links so that once a problem occurs in one link, the problem can be solved precisely and the transportation cost can be saved to the maximum. The most important issue in logistics is the isolation of products from the external environment, to prevent products from being contaminated during transportation and affecting subsequent product delivery [26]. The intelligent logistics system implements fully automated loading, unloading, and moving of goods in the supply chain and warehouses, and other terminals, reducing manual contact as much as possible and ensuring the safety of products. The intelligent logistics system does not only involve the construction of logistics, but also covers the processes of product outbound, transportation, storage, distribution, and processing. To further improve the logistics system, optimization is needed in each process and set up IOT nodes to form a closed loop with the logistics system, to ensure the integrity of the intelligent logistics system.Figure 1
Smart logistics platform.The purpose of the logistics system is to accomplish rapid iteration of warehousing through a complex product distribution network. The distribution network is composed of many logistic nodes, each covering a large amount of infrastructure. All infrastructures are more expensive to maintain, have limited transmission capacity, and have poor dynamic range. To better coordinate each logistics node, the industry mostly uses the neural network model for the layout of nodes, which is supported by cloud computing to form a huge logistics neural network. The supply chain information for a single node responds quickly and can quickly generate an optimal logistics path and timeline based on big data and ensure the timeliness and accuracy of logistics.


## 3. Method

### 3.1. Basic Network

By preliminary examination, we apply the graph convolutional neural network as the base network, whose network structure is shown in Figure2. This network is an upgraded version of the graph convolutional network, which aims to optimize the perceptual domain of the graph convolution and increase the union of graph convolutional networks for time-level feature relationships. The main purpose of this network is to sequence encode logistics nodes and predict the best trajectory by spatial features and temporal associations between logistics nodes. For the acquisition of logistics trajectory features, we usually use the CNN path planning algorithm [27], which uses a convolutional neural network to parse laser information, the A∗ algorithm as marker information, and finally supervised learning to predict the best trajectory.Figure 2
Graph convolutional network infrastructure.


### 3.2. Calculation Principle of the Logistics Node Network

The input is usually a series of logistics node data in CSV format, and each set of data contains information such as time points, product information, logistics routes, path nodes, and storage data. The algorithm can also split and parse each set of logistics node data and map it to each node unit graph node of the IoT to build a complete spatial temporal graph with the outermost node of the IoT as the boundary. In other words, the input of the path planning algorithm can also be understood as the product data information of a set of logistics nodes, the same as the two-dimensional pixel intensity vector input of the convolutional neural network. To obtain a wider range of information, the graph convolutional network is then stacked, and all outputs are then fed to the classifier in parallel.The input in Figure2 is a fixed sequence of logistics nodes, assuming that T denotes the composition sequence of the total number of logistics nodes, V denotes the number of logistics node branches, and G=N,E denotes the set of constructed logistics branch sequences, which N=vti|t=1,…T,i=1,…V is obtained by traversing all-time series of logistics nodes together, and vti denotes all nodes. E denotes the set of connections between branch nodes. E consists of ET and ES. Arbitrary logistics path node i,j, ES=vti,vtj|i,j=1…,V,t=1,…,T denotes the composition of the connections of the specified path logistics nodes within time t. The subset of connections ES within nodes is divided into K disjoint regions in the path principle and is represented using an adjacency matrix encoding A˜k∈0,1V×V. ET=vti,vt+1i|i=1…,V,t=1,…,T denotes the union of connections between all logistics nodes in a continuous time series. The fusion of the above features produces a sequence graph that can be extended in the temporal dimension of the spatial mapping.The literature [28] optimized the spatial submodule of the spatial temporal graph convolutional neural network and proposed the following graph convolution equation.(1)fout=∑kKsfinAkWk,Ak=Dk−1/2A˜k+IDk−1/2,Dii=∑kKsA˜kij+Iij,where A˜s denotes the adjacency matrix of the internal connections of the logistics nodes, I denotes the unit matrix, Ks denotes the size of the convolution kernel in the spatial dimension, and Wk denotes the training weights. The temporal convolution module is 1×Kt. In 2D graph convolution, the perceptual field of the convolution kernel is not considered when operating Cin,V,T in the V,T dimension, where Kt denotes the planning progress of the logistics node per unit time.The graph structures in graph convolution are predefined, and to increase its adaptability, the literature [29–31] uses a fixed adjacency matrix and proposes an adaptive graph convolution formula as follows:(2)fout=∑kKsfinAk+Bk+CkWk,where Bk denotes the parameters learned in training and Ck denotes the connected vertices determined with the over-similarity function.


### 3.3. Optimization Strategy

Our proposed improved graph convolutional logistics node model stems from a two-part optimization of the spatial temporal graph convolutional network. The first part is to optimize the graph convolutional network layer; the second part is to add the inception layer. In the graph convolution layer, the original model aims to obtain spatial location information between branching logistics nodes for the representation of branching logistics nodes. It should build a local perceptual domain starting from the initial neighboring logistics nodes, in which a large number of sample nodes are generated. Although many false samples are generated at this time, adding topological angle restrictions in the subsequent process of filtering the sequence in Euclidean space can filter out the false samples. When all sample nodes are in the Euclidean space, at the global level, all sample nodes can be considered as a point and the sequence of points is considered as a one-dimensional vector. In this case, to capture a large number of sample logistic node features, a large-scale graph convolution sum of a size consistent with the number of nodes is required. To solve this problem correctly, we propose a tensor rotation strategy. We add a tensor rotation module at the beginning and the end of the graph convolution layer. The detailed network structure is shown in Figure3.Figure 3
Tensor rotation graph convolutional layer.By the action of the tensor rotation module, each branch logistics node can share the same set of identical topological matrices, and all logistics nodes can participate in the process of capturing global information. Take a specified planning path logistics node as an example, suppose the path contains 20 logistics nodes, in the fully connected layer, we choose a filter of size 20. The rotate tensor module rotates a separate tensor for each logistics node so that the dimensions of the logistics nodes are aligned with the dimensions of the channel. By tensor rotation, the predefined topological matrix is discarded, and global features are learned adaptively according to the self-looping unit to obtain joint correlations. Finally, the global information is integrated by Conv 1 × 1 dimensionality reduction. Such a structural design can effectively reduce the use of higher order polynomial estimation to capture higher order features layer-by-layer, thus achieving a reduction in the number of parameters.The layout of the inception sparse structure allows obtaining more feature information while avoiding the increase in the number of parameters. We refer to the inception optimization process from V1 to V4 and found the one-dimensional convolutional dimensionality reduction method [32–34]. We are building the initial temporal convolutional network, where the expansion of parameters is exacerbated by exponentially growing expansion factors in the temporal convolutional layers to expand the network. In contrast, the inception tiling structure is incremental by layer, with each branch preceded by Conv 1 × 1 dimensionality reduction, assigning different expansion settings to each branch, allowing time-scale information to be graded into the inception branch, and achieving information integration in different time dimensions. By the above structure of time coefficient assignment, the exponential growth of coefficients is avoided and the purpose of reducing the number of parameters is achieved.The temporal convolutional layer is usually added at the end of the main network and is divided into 4 branches according to the layering principle, each branch generates output to the corresponding group, and its structure is shown in Figure4. The initial value of the expansion coefficient n of the network is 1. As the network deepens, the layer units gradually increase, and the maximum value of the expansion coefficient is 4. This external connection refers to the residual structure, which undergoes a one-dimensional convolution with a stride of 2. This design can avoid the problem of gradient dispersion. Improving the temporal convolutional network by inserting the initial structure can capture more time-scale information while greatly reducing the number of network parameters and reducing the computational cost. A compact and efficient temporal feature extraction network is realized by adaptively selecting the best feature information using different temporal filters to optimize the classification problem.Figure 4
Sparse structure temporal convolutional layer.


### 3.4. Graph Convolutional Network Logistics Optimization Implementation Process

We adopt a graph convolutional network as the basis for logistics node optimization and add a time convolution module, using a predefined structure graph as a topology constraint to achieve the ability of graphs with different time steps to share the same topology, and such a structure makes graph tasks impossible A joint layer that fully captures the relevant features of regional logistics branch nodes. To address this problem, our most common approach is to build a regional neural network, starting with the local receptive field and experimenting with small-scale graph tasks. This is prone to global information omission. To simulate the principle of convolutional neural network computing pixels, each graph node and adjacent graph nodes become the key nodes of graph convolution computation in the graph convolution tasks. Considering the density heterogeneity between adjacent nodes and the problem of local structural narrowness. In our improved network, we use fixed-size node features for feature learning in the temporal dimension, selectively ignore the size of cluster features, and can capture more features in the temporal dimension. Therefore, we apply the initial structure to some network layers to reduce model parameters, widen the network width, and enhance the robustness of the model.The logistics optimization process based on an improved graph convolutional network is shown in Figure5. First, the real-time logistics data of each logistics node are collected, and the logistics data are preprocessed to eliminate abnormal data in the analysis process. The difference of logistics data at different time nodes increases, and we divide the logistics data into peak period and smooth period according to from time level, but the set of logistics data at different two stages obey random distribution. Therefore, we first select the batch standard module in the first layer of the hierarchical distribution of the network to normalize the logistics node data at the temporal level and the spatial level to make the input branch logistics node data more standardized, reducing the error volatility, and optimize the convergence of the algorithm. In the second layer of the network, we choose the attention mechanism, which connects our new rotating tensor convolution layer to the next sparsely structured temporal convolution layer in the network. The rotated tensor convolution layer relies on the tensor rotation operation to obtain global information, after which the obtained global features are fed into the sparse structured temporal convolution to analyze the linkage relationship of node features at the temporal level, supplemented by the attention mechanism to weaken the features that do not fit the bounded range of the model and filter the features at different time scales. The whole network is fully captured and fused by graph feature information, then averaged pooling, then features are classified by a fully connected layer, and finally, the optimal planning of logistics lines is output according to the classification weights.Figure 5
IST-GCN human action recognition process.


## 3.1. Basic Network

By preliminary examination, we apply the graph convolutional neural network as the base network, whose network structure is shown in Figure2. This network is an upgraded version of the graph convolutional network, which aims to optimize the perceptual domain of the graph convolution and increase the union of graph convolutional networks for time-level feature relationships. The main purpose of this network is to sequence encode logistics nodes and predict the best trajectory by spatial features and temporal associations between logistics nodes. For the acquisition of logistics trajectory features, we usually use the CNN path planning algorithm [27], which uses a convolutional neural network to parse laser information, the A∗ algorithm as marker information, and finally supervised learning to predict the best trajectory.Figure 2
Graph convolutional network infrastructure.


## 3.2. Calculation Principle of the Logistics Node Network

The input is usually a series of logistics node data in CSV format, and each set of data contains information such as time points, product information, logistics routes, path nodes, and storage data. The algorithm can also split and parse each set of logistics node data and map it to each node unit graph node of the IoT to build a complete spatial temporal graph with the outermost node of the IoT as the boundary. In other words, the input of the path planning algorithm can also be understood as the product data information of a set of logistics nodes, the same as the two-dimensional pixel intensity vector input of the convolutional neural network. To obtain a wider range of information, the graph convolutional network is then stacked, and all outputs are then fed to the classifier in parallel.The input in Figure2 is a fixed sequence of logistics nodes, assuming that T denotes the composition sequence of the total number of logistics nodes, V denotes the number of logistics node branches, and G=N,E denotes the set of constructed logistics branch sequences, which N=vti|t=1,…T,i=1,…V is obtained by traversing all-time series of logistics nodes together, and vti denotes all nodes. E denotes the set of connections between branch nodes. E consists of ET and ES. Arbitrary logistics path node i,j, ES=vti,vtj|i,j=1…,V,t=1,…,T denotes the composition of the connections of the specified path logistics nodes within time t. The subset of connections ES within nodes is divided into K disjoint regions in the path principle and is represented using an adjacency matrix encoding A˜k∈0,1V×V. ET=vti,vt+1i|i=1…,V,t=1,…,T denotes the union of connections between all logistics nodes in a continuous time series. The fusion of the above features produces a sequence graph that can be extended in the temporal dimension of the spatial mapping.The literature [28] optimized the spatial submodule of the spatial temporal graph convolutional neural network and proposed the following graph convolution equation.(1)fout=∑kKsfinAkWk,Ak=Dk−1/2A˜k+IDk−1/2,Dii=∑kKsA˜kij+Iij,where A˜s denotes the adjacency matrix of the internal connections of the logistics nodes, I denotes the unit matrix, Ks denotes the size of the convolution kernel in the spatial dimension, and Wk denotes the training weights. The temporal convolution module is 1×Kt. In 2D graph convolution, the perceptual field of the convolution kernel is not considered when operating Cin,V,T in the V,T dimension, where Kt denotes the planning progress of the logistics node per unit time.The graph structures in graph convolution are predefined, and to increase its adaptability, the literature [29–31] uses a fixed adjacency matrix and proposes an adaptive graph convolution formula as follows:(2)fout=∑kKsfinAk+Bk+CkWk,where Bk denotes the parameters learned in training and Ck denotes the connected vertices determined with the over-similarity function.


## 3.3. Optimization Strategy

Our proposed improved graph convolutional logistics node model stems from a two-part optimization of the spatial temporal graph convolutional network. The first part is to optimize the graph convolutional network layer; the second part is to add the inception layer. In the graph convolution layer, the original model aims to obtain spatial location information between branching logistics nodes for the representation of branching logistics nodes. It should build a local perceptual domain starting from the initial neighboring logistics nodes, in which a large number of sample nodes are generated. Although many false samples are generated at this time, adding topological angle restrictions in the subsequent process of filtering the sequence in Euclidean space can filter out the false samples. When all sample nodes are in the Euclidean space, at the global level, all sample nodes can be considered as a point and the sequence of points is considered as a one-dimensional vector. In this case, to capture a large number of sample logistic node features, a large-scale graph convolution sum of a size consistent with the number of nodes is required. To solve this problem correctly, we propose a tensor rotation strategy. We add a tensor rotation module at the beginning and the end of the graph convolution layer. The detailed network structure is shown in Figure3.Figure 3
Tensor rotation graph convolutional layer.By the action of the tensor rotation module, each branch logistics node can share the same set of identical topological matrices, and all logistics nodes can participate in the process of capturing global information. Take a specified planning path logistics node as an example, suppose the path contains 20 logistics nodes, in the fully connected layer, we choose a filter of size 20. The rotate tensor module rotates a separate tensor for each logistics node so that the dimensions of the logistics nodes are aligned with the dimensions of the channel. By tensor rotation, the predefined topological matrix is discarded, and global features are learned adaptively according to the self-looping unit to obtain joint correlations. Finally, the global information is integrated by Conv 1 × 1 dimensionality reduction. Such a structural design can effectively reduce the use of higher order polynomial estimation to capture higher order features layer-by-layer, thus achieving a reduction in the number of parameters.The layout of the inception sparse structure allows obtaining more feature information while avoiding the increase in the number of parameters. We refer to the inception optimization process from V1 to V4 and found the one-dimensional convolutional dimensionality reduction method [32–34]. We are building the initial temporal convolutional network, where the expansion of parameters is exacerbated by exponentially growing expansion factors in the temporal convolutional layers to expand the network. In contrast, the inception tiling structure is incremental by layer, with each branch preceded by Conv 1 × 1 dimensionality reduction, assigning different expansion settings to each branch, allowing time-scale information to be graded into the inception branch, and achieving information integration in different time dimensions. By the above structure of time coefficient assignment, the exponential growth of coefficients is avoided and the purpose of reducing the number of parameters is achieved.The temporal convolutional layer is usually added at the end of the main network and is divided into 4 branches according to the layering principle, each branch generates output to the corresponding group, and its structure is shown in Figure4. The initial value of the expansion coefficient n of the network is 1. As the network deepens, the layer units gradually increase, and the maximum value of the expansion coefficient is 4. This external connection refers to the residual structure, which undergoes a one-dimensional convolution with a stride of 2. This design can avoid the problem of gradient dispersion. Improving the temporal convolutional network by inserting the initial structure can capture more time-scale information while greatly reducing the number of network parameters and reducing the computational cost. A compact and efficient temporal feature extraction network is realized by adaptively selecting the best feature information using different temporal filters to optimize the classification problem.Figure 4
Sparse structure temporal convolutional layer.


## 3.4. Graph Convolutional Network Logistics Optimization Implementation Process

We adopt a graph convolutional network as the basis for logistics node optimization and add a time convolution module, using a predefined structure graph as a topology constraint to achieve the ability of graphs with different time steps to share the same topology, and such a structure makes graph tasks impossible A joint layer that fully captures the relevant features of regional logistics branch nodes. To address this problem, our most common approach is to build a regional neural network, starting with the local receptive field and experimenting with small-scale graph tasks. This is prone to global information omission. To simulate the principle of convolutional neural network computing pixels, each graph node and adjacent graph nodes become the key nodes of graph convolution computation in the graph convolution tasks. Considering the density heterogeneity between adjacent nodes and the problem of local structural narrowness. In our improved network, we use fixed-size node features for feature learning in the temporal dimension, selectively ignore the size of cluster features, and can capture more features in the temporal dimension. Therefore, we apply the initial structure to some network layers to reduce model parameters, widen the network width, and enhance the robustness of the model.The logistics optimization process based on an improved graph convolutional network is shown in Figure5. First, the real-time logistics data of each logistics node are collected, and the logistics data are preprocessed to eliminate abnormal data in the analysis process. The difference of logistics data at different time nodes increases, and we divide the logistics data into peak period and smooth period according to from time level, but the set of logistics data at different two stages obey random distribution. Therefore, we first select the batch standard module in the first layer of the hierarchical distribution of the network to normalize the logistics node data at the temporal level and the spatial level to make the input branch logistics node data more standardized, reducing the error volatility, and optimize the convergence of the algorithm. In the second layer of the network, we choose the attention mechanism, which connects our new rotating tensor convolution layer to the next sparsely structured temporal convolution layer in the network. The rotated tensor convolution layer relies on the tensor rotation operation to obtain global information, after which the obtained global features are fed into the sparse structured temporal convolution to analyze the linkage relationship of node features at the temporal level, supplemented by the attention mechanism to weaken the features that do not fit the bounded range of the model and filter the features at different time scales. The whole network is fully captured and fused by graph feature information, then averaged pooling, then features are classified by a fully connected layer, and finally, the optimal planning of logistics lines is output according to the classification weights.Figure 5
IST-GCN human action recognition process.


## 4. Experiment

### 4.1. Data Source

To test our improved graph convolution logistics optimization method, we collaborated with an e-commerce platform to collect one week of logistics information from an independent city. Before collecting the data, we referred to the literature on logistics optimization, investigated the key data in logistics planning, and developed detailed data collection rules based on our experimental requirements and equipment conditions. The main parameters we recorded were the number of products (P), the number of path nodes (PN), the warehouse throughput (WT), and the growth of orders (GO) in the same period. The logistics node data collection was participated by 100 logistics personnel, and we set up sensors, database stations, and warehouse throughput counters as auxiliary facilities at each node without affecting the normal work of logistics. Details of the specific logistics data set are shown in Table1.Table 1
Logistics node data collection details.
PPNWTGOPeak period435125311611360135Normal period20031224966839101Total635435312578199236


### 4.2. Analysis of Results

We have implemented a logistics optimization system that can monitor the distribution, storage, and delivery process of products comprehensively. Also in a complex logistics network, the throughput of each logistics node can be analyzed in real time. The optimal logistics strategy is automatically generated based on the product's order requirements and distribution conditions. For customer orders, the most important metric is the product delivery rate. To validate the accuracy of our method, we evaluate the method in three directions: demand forecasting rate (DFR), return on investment (RI), and product on-time delivery rate (PDR). To compare the quality of our method, we simulated the most primitive logistics rules as a group A control experiment, and we also used the current path planning algorithm as a group B control experiment. The experimental results are shown in Table2.Table 2
Logistics product delivery comparison experiment.
DFR (%)RI (%)PDR (%)A group354321B group767173Our method918996From the experimental results in Table2, it can be seen that, in terms of product delivery rate, Group A represents the most traditional logistics delivery model, with a product delivery rate of only 35%, and product delivery punctuality is not guaranteed, at 21%. This is due to the complexity of modern transportation systems that make many modes of transportation ineffective. In addition, with the development of the e-commerce economy, customer order demand has skyrocketed, making the traditional logistics model unable to cope with the huge order system as it was at the beginning. Group B represents the logistics model of path planning counting, which is a logistics optimization method commonly used in the logistics industry today. The product delivery rate reached 76%, which was enhanced by the intervention of the path planning algorithm under the influence of the current complex logistics route network. However, there is still room for optimization, and in the face of a large number of unexpected situations, logistics accidents still occur, resulting in undeliverable products and inventory backlogs. With a product delivery on-time rate of 73%, there is also a lot of room for optimization. And our method, compared with the methods of Group A and Group B, achieves more than 90% in both product delivery rate and product on-time delivery rate. The comparison of the comprehensive delivery rate of logistics products is shown in Figure 6. The efficient product delivery rate and on-time product delivery generated a large ROI ratio for both the customer and the merchant. It proves the superiority of our method in the ROI analysis.Figure 6
Comparison of product delivery rates by different methods.For the automatic planning and computation speed of logistics nodes, we designed separate experiments for verification. We launched comparison experiments from logistics peak and normal periods, mainly to verify the performance difference between the path planning algorithm (PPA) and our method. In this experiment, we validate two main metrics, one metric is the number of logistics routes planned per hour (LRP) and the other metric is the amount of logistics warehouse storage processed per hour (LWS). The experimental results are shown in Table3.Table 3
Experimental results of logistics efficiency comparison.
Peak periodNormal periodLRPLWSLRPLWSPPA76222644241341Ours127236575041533From the above table, we can see that the efficiency of the path planning algorithm for logistics planning in a normal period is not much different from our method. However, in peak period, our method has a huge advantage, considering that there are more product orders and large logistics demand in peak period, and the huge and complex logistics network will produce logistics accidents when facing some sudden logistics obstacles and order problems, which leads to the low efficiency of the path planning algorithm in peak period logistics. But our method learns various logistics problems and solutions adaptively from the level of deep neural networks and solves the problems in the form of ad hoc decisions when new problems are encountered, so our method performs well in peak periods.


## 4.1. Data Source

To test our improved graph convolution logistics optimization method, we collaborated with an e-commerce platform to collect one week of logistics information from an independent city. Before collecting the data, we referred to the literature on logistics optimization, investigated the key data in logistics planning, and developed detailed data collection rules based on our experimental requirements and equipment conditions. The main parameters we recorded were the number of products (P), the number of path nodes (PN), the warehouse throughput (WT), and the growth of orders (GO) in the same period. The logistics node data collection was participated by 100 logistics personnel, and we set up sensors, database stations, and warehouse throughput counters as auxiliary facilities at each node without affecting the normal work of logistics. Details of the specific logistics data set are shown in Table1.Table 1
Logistics node data collection details.
PPNWTGOPeak period435125311611360135Normal period20031224966839101Total635435312578199236


## 4.2. Analysis of Results

We have implemented a logistics optimization system that can monitor the distribution, storage, and delivery process of products comprehensively. Also in a complex logistics network, the throughput of each logistics node can be analyzed in real time. The optimal logistics strategy is automatically generated based on the product's order requirements and distribution conditions. For customer orders, the most important metric is the product delivery rate. To validate the accuracy of our method, we evaluate the method in three directions: demand forecasting rate (DFR), return on investment (RI), and product on-time delivery rate (PDR). To compare the quality of our method, we simulated the most primitive logistics rules as a group A control experiment, and we also used the current path planning algorithm as a group B control experiment. The experimental results are shown in Table2.Table 2
Logistics product delivery comparison experiment.
DFR (%)RI (%)PDR (%)A group354321B group767173Our method918996From the experimental results in Table2, it can be seen that, in terms of product delivery rate, Group A represents the most traditional logistics delivery model, with a product delivery rate of only 35%, and product delivery punctuality is not guaranteed, at 21%. This is due to the complexity of modern transportation systems that make many modes of transportation ineffective. In addition, with the development of the e-commerce economy, customer order demand has skyrocketed, making the traditional logistics model unable to cope with the huge order system as it was at the beginning. Group B represents the logistics model of path planning counting, which is a logistics optimization method commonly used in the logistics industry today. The product delivery rate reached 76%, which was enhanced by the intervention of the path planning algorithm under the influence of the current complex logistics route network. However, there is still room for optimization, and in the face of a large number of unexpected situations, logistics accidents still occur, resulting in undeliverable products and inventory backlogs. With a product delivery on-time rate of 73%, there is also a lot of room for optimization. And our method, compared with the methods of Group A and Group B, achieves more than 90% in both product delivery rate and product on-time delivery rate. The comparison of the comprehensive delivery rate of logistics products is shown in Figure 6. The efficient product delivery rate and on-time product delivery generated a large ROI ratio for both the customer and the merchant. It proves the superiority of our method in the ROI analysis.Figure 6
Comparison of product delivery rates by different methods.For the automatic planning and computation speed of logistics nodes, we designed separate experiments for verification. We launched comparison experiments from logistics peak and normal periods, mainly to verify the performance difference between the path planning algorithm (PPA) and our method. In this experiment, we validate two main metrics, one metric is the number of logistics routes planned per hour (LRP) and the other metric is the amount of logistics warehouse storage processed per hour (LWS). The experimental results are shown in Table3.Table 3
Experimental results of logistics efficiency comparison.
Peak periodNormal periodLRPLWSLRPLWSPPA76222644241341Ours127236575041533From the above table, we can see that the efficiency of the path planning algorithm for logistics planning in a normal period is not much different from our method. However, in peak period, our method has a huge advantage, considering that there are more product orders and large logistics demand in peak period, and the huge and complex logistics network will produce logistics accidents when facing some sudden logistics obstacles and order problems, which leads to the low efficiency of the path planning algorithm in peak period logistics. But our method learns various logistics problems and solutions adaptively from the level of deep neural networks and solves the problems in the form of ad hoc decisions when new problems are encountered, so our method performs well in peak periods.


## 5. Conclusion

In this paper, we propose a logistics optimization method based on an improved graph convolutional network, which improves the product delivery rate and delivery time of logistics and greatly enhances the return on investment. First, we add a tensor rotation module to the graph convolutional layer to better capture the global features of logistics nodes. Then we add inception structures in the temporal convolution layer to build multiscale temporal convolution filters to obtain temporal information of logistics nodes in different time-aware domains and reduce arithmetic power. Finally, we cooperate with e-commerce platforms to adopt logistics data as the experimental database. The experimental results show that our optimized method has a high product delivery rate, timely product delivery, superior ROI, and high robustness. It not only improves the efficiency of the graph topology learning process but also greatly reduces the number of parameters and greatly accelerates the logistics planning speed.In a normal period, our approach does not differ much from the path planning algorithm, and the peak period of logistics is mainly used to face promotional activities and festive events. Most of the time is a normal period, in the next research, we will refer to more path planning methods to improve the performance of our method in normal period. The proposed method is a general model. In the future, the proposed method can be applied to the fields of time series analysis [35–37].



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*Source: 8367155-2022-06-06.xml*