Intelligent Logistics Research Articles

Container loading problem based on robotic loader system: An optimization approach

With the development of intelligent logistics technology, some companies began to use robots instead of humans to load cargo. This paper studies a novel container loading problem based on robotic loader system (CLP-RLS). Different from the existing robot-packable pattern in the literature, the robotic loader system in this paper consists of a depalletizing robot, an automatic telescopic roller line and a loading robot, in which the loading robot will enter the carriage along with the automatic telescopic roller line. In CLP-RLS, it is necessary to consider not only many practical constraints already in the literature, including load balancing, orientation, stability, and multi-drop but also two new constraints related to robotic loader system: pallet continuity constraint (the loading sequence of cargo on the same pallet is continuous) and robot position constraint (the robot can only load cargo incrementally from the front to the back of the truck). Due to the difficulty in modeling CLP-RLS and the large scale of the real-case instances, we present a tree search approach based on wall-building to solve CLP-RLS, intending to find a feasible loading scheme for the RLS to minimize the length required to load all cargo. The effectiveness of the proposed approach is verified through both real-case instances and numerical instances.

Research on Multi-AGV Task Allocation in Train Unit Maintenance Workshop

In the context of the continuous development and maturity of intelligent manufacturing and intelligent logistics, it has been observed that the majority of vehicle maintenance in EMU trains still relies on traditional methods, which are characterized by excessive manual intervention and low efficiency. To address these deficiencies, the present study proposes the integration of Automatic Guided Vehicles (AGVs) to improve the traditional maintenance processes, thereby enhancing the efficiency and quality of vehicle maintenance. Specifically, this research focuses on the scenario of the maintenance workshop in EMU trains and investigates the task allocation problem for multiple AGVs. Taking into consideration factors such as the maximum load capacity of AGVs, remaining battery power, and task execution time, a mathematical model is formulated with the objective of minimizing the total distance and time required to complete all tasks. A multi-population genetic algorithm is designed to solve the model. The effectiveness of the proposed model and algorithm is validated through simulation experiments, considering both small-scale and large-scale scenarios. The results indicate that the multi-population genetic algorithm outperforms the particle swarm algorithm and the genetic algorithm in terms of stability, optimization performance, and convergence. This research provides scientific guidance and practical insights for enterprises adopting task allocation strategies using multiple AGVs.

Research on AGV cart control system based on fuzzy PID control

Abstract As an important carrier of intelligent logistics, AGV can effectively reduce the labor cost of factories and warehouses. With the comprehensive promotion of “smart manufacturing”, the manufacturing industry has a stronger demand for automated AGV logistics systems. This paper firstly analyzes the principle of fuzzy PID controller and establishes its kinematic model to obtain the intrinsic relationship between the motion form and the speed of each driving wheel. Secondly, through the data measured and fed back by the fuzzy PID control system, the AGV driving angle deviation and position deviation are used as the input of the fuzzy controller; its fuzzy deviation correction technology is applied to realize the path correction of the AGV and ensure the stable operation of the AGV through the cooperative motion control technology. Finally, MATLAB software establishes the joint simulation model of the AGV fuzzy PID control system. The results show that through the two algorithms of PID control and fuzzy PID control correction test, the fuzzy PID controller system corrects the deflection efficiency by 47.41%; in the kinematic test, the path tracking efficiency is increased by 30.76%, the distance correction efficiency is increased by 69.56%, the angle correction efficiency is increased by 69.56%, and the average performance is increased by 53.44%. Through the two comparisons, it is concluded that the fuzzy controller designed in this paper has the advantages of fast response speed, good stability, and strong deflection correction performance, which substantially improves the speed and accuracy of intelligent search of intelligent vehicles.

Application Research of Computer Artificial Intelligence Technology in Urban Smart Logistics System

This paper proposes the application objective and path of intelligent logistics. Urban smart logistics system is constructed by digitization of underlying elements, modeling of intermediate operations and intelligent management decision-making system. The system in the standardized logistics mode to promote the supply side and the demand side of the maximum degree of fit. Then this paper takes e-commerce platform, e-logistics platform and e-government platform as the basic framework to build an intelligent logistics system composed of supply subsystem, demand subsystem and supervision subsystem. Finally, the system realizes the effective integration of information flow, business flow, logistics and capital flow in the operation process of intelligent logistics mode based on big data cloud computing. Finally, this paper analyzes the practical application of intelligent logistics in the field of intelligent management and decision making. Experimental simulation shows that the logistics system can improve the efficiency of scheduling, transportation and other processes.

Autonomous Control Method for Object Grasping of Logistics Sorting Manipulator Considering Changes in Lighting Environment

The autonomous grasping operation is the key to the intelligent logistics sorting manipulators. Since the current logistics sorting manipulators mostly use visual sensors to identify objects, they are extremely vulnerable to the changes in the lighting environment. Therefore, the research considers the influence of complex lighting environment on the autonomous grasping of logistics sorting manipulators. Furthermore, it verifies the effectiveness of SAC-AE-ICM algorithm through simulations and experiments. The experimental results show that SAC-AE-ICM algorithm can quickly achieve convergence in many experiments and can obtain global optimization. In the process of automatic capture, the success rate of SAC-AE-ICM algorithm can reach 90%, which is 25% higher than that of the method without ICM and has better convergence. The success rate was as high as 77% for unknown or irregular targets. In practical experiments, SAC-AE-ICM can effectively capture under good lighting conditions. However, under low light conditions, the probability of capturing unknown targets is about 72.5%. Overall, the success rates for single-object and multi-object capture are 88% and 85%, respectively.

Federated Multiagent Actor–Critic Learning Task Offloading in Intelligent Logistics

Intelligent logistics empowered by artificial intelligence (AI) has become an inevitable trend in the development of modern logistics, thus a convenient and efficient logistics system has attracted widespread attention. However, how to use AI to execute computation-intensive applications on resource-constrained logistics vehicles still faces enormous challenges. For the dependent applications in intelligent logistics, this paper investigates a task dynamic offloading strategy for logistics vehicles-edge collaboration with multiple dependent tasks, considering the inter-task dependency, to guarantee the quality of service (QoS) requirements of logistics vehicles. Firstly, the dependent application ARCore is modeled and transformed into a model with a linear execution sequence. Then, based on this task model, the joint task offloading and resource allocation problem is formulated. The goal is to minimize the weighted sum cost of the execution delay and energy consumption while guaranteeing the delay tolerance and computing resource constraints of the tasks. Furthermore, we propose a federated logistics vehicles-edge collaborative computation (FECC) offloading framework to solve the optimization problem, which only requires each agent to share its model parameters without sharing local training data, thereby reducing the computation complexity and signaling overhead of the multi-agent training process. Numerical results show that the proposed strategy has significant advantages in terms of total system cost compared to the baseline strategy.

Design and Development of Intelligent Logistics Tracking System Based on Computer Algorithm

Design and Development of Intelligent Logistics Tracking System Based on Computer Algorithm

Research on an intelligent logistics control system with a focus on monitoring the hygienic safety of the logistics chain

The main objective of the paper is to scientifically elaborate the idea of a methodology for ensuring the safety of transported goods in terms of antiviral and antibacterial protection. The output is a description in the field of transport with the possibility of applying the knowledge also in the aerospace industry. The core of the paper is formed by the description of an intelligent logistics control system with a focus on monitoring the hygienic safety of the logistics chain is a challenge to solve the current problem of ensuring the stability and infection-free transport of goods, primarily needed for production and consumer. Using advanced digital numerical simulations of logistics processes throughout the life cycle of the supply-customer chain and will fit into the issues outlined in the Industry 4.0 strategy. The prerequisite is the constant requirement for stability and infection-free or to ensure the continuity of the supply of goods also in the future to react to the arrival of different epidemiological situations. The need for an intelligent logistics management system is based on the changing actual requirements of hygiene measures, which are determined differently for each region. It is possible to respond to the situation with an intelligent solution tailored to the chosen carrier.

Leveraging and Refining Image Recognition Technology for Intelligent Logistics Sorting Systems

Obstacles in the realm of target detection, tracking accuracy, real-time performance, and robustness have been identified within the context of intelligent logistics sorting systems, which incorporate image recognition technology. Furthermore, the critical role of detecting anomalous states in augmenting sorting efficiency and curtailing errors is underscored. Image recognition technologies of a conventional nature tend to suffer from limitations in their applicability and robustness. In practical working environments, a paucity of abnormal data from logistics sorting targets is observed, which inhibits the application of supervised learning methods of deep learning. Addressing these challenges, an unsupervised deep learning method is introduced for the detection of anomalous states in logistics sorting targets. This approach reinterprets the detection of logistics sorting targets as an anomaly detection problem and utilizes Variational AutoEncoders (VAE) for modeling the distribution of normal data. This method's dependency rests exclusively on normal data for training, thereby circumventing the need for a substantial quantity of abnormal samples. In practical deployments, the anomalous state of logistics sorting targets is discerned by the method through the computation of similarity and implementation of labeling algorithms, evidencing robustness, generalizability, and adaptability. Overall, this method is presented as an effective solution for the detection of anomalous states within intelligent logistics sorting scenarios, serving to decrease labeling costs, enhance detection accuracy and efficiency, and satisfying the practical requisites of logistics sorting systems for abnormal state detection.

Study on an autonomous distribution system for smart parks based on parallel system theory against the background of Industry 5.0

The autonomous distribution systems used in smart parks against the background of Industry 5.0 require not only the consideration of the single goal of the economic benefits of enterprises, but also the fulfillment of their social responsibilities. Consequently, the scheduling of autonomous distribution systems and the trajectory planning of intelligent logistics vehicles have become increasingly more complex. Although technologies such as swarm intelligence have gradually been applied to the solution of independent distribution systems, there remain challenges in how to ensure that the production enterprises bear their responsibility to the public and consumers. Parallel system theory provides theoretical support for the concrete embodiment of people-oriented values in the smart park environment. In this work, based on parallel system theory, a parallel autonomous driving system is established. The system is mainly used for the autonomous transportation of finished products and materials in smart parks. The goal is to enhance the flexibility and efficiency of the distribution system in the park, and to highlight the people-oriented goal. Based on swarm intelligence theory and the A* algorithm, an improved swarm search optimization algorithm called IGSO-A* is developed to support the scheduling of parallel distribution systems and the trajectory planning of intelligent logistics vehicles. In two types of simulation experiments, compared with three other cutting-edge algorithms, the performance of the designed IGSO algorithm is improved by 4.6% on average. Moreover, compared with the A* algorithm, the performance of the proposed IGSO-A* algorithm is improved by 11.49%. The results prove the effectiveness of the proposed parallel autonomous distribution system in the distribution of finished products and materials in smart parks.

FPGA-based Intelligent Logistics Management for Universities

Abstract With the strong drive for economic and cultural development, China’s higher education has flourished and formed a higher education system and scale with typical Chinese characteristics, and the healthy development of higher education cannot be separated from the strong support and guarantee of university logistics. Based on FPGA technology, this paper explains in detail the design method and computing steps of FPGA technology to build a university intelligent logistics management data analysis system. The performance evaluation is carried out for the system designed in this paper, and the data analysis of the indicator examples is carried out by using five examples of logistics management service indicators of the University of W, namely campus environment, property service, commercial service, residence management service, and utility maintenance. From the performance evaluation data of the FPGA university intelligent logistics management system, the average accuracy, precision, recall, and AUC values of the FPGA-based university logistics management system are 91.90%, 95.78%, 97.28%, and 97.41%, respectively. From the index instance analysis, the average evaluation values of the five services of very satisfied, satisfied, generally satisfied, and dissatisfied are 24.89%, 25.16%, 40.97%, and 8.98%, compared with the previous average values of very satisfied and satisfied evaluations increased by 18.36% and 4.61%, respectively, and the average values of generally satisfied and unsatisfied evaluations decreased by 7.65% and 15.31%, respectively. The FPGA-based intelligent management system of colleges and universities has higher stability and stronger data analysis ability, which can more effectively propose corresponding reform directions for the problems and provide technical support to promote more intelligent logistics management of colleges and universities. At the same time, it enables university teachers and students to carry out relevant academic research and study life without any worries, contributes technical power to promote the overall healthy development of universities, and provides new research directions to broaden the development field of FPGA technology.

Mobility 5.0: Smart Logistics and Transportation Services in Cyber-Physical-Social Systems

The logistics and transportation industry has made significant progress in improving sustainability, efficiency, and ac- cessibility, primarily due to the continuous development of tech- nologies, including clean energy, advanced algorithms, unmanned vehicles, and privacy-enhancing techniques. Along with these ad- vancements, the Mobility 5.0 paradigm has been proposed in the ITSS Intelligent Vehicle 5.0 project meeting. This letter discusses the changes in the supply and demand of mobility resources and looks at the basic components on which the implementation of the Mobility 5.0 relies on. By integrating emerging technologies into Cyber-Physical-Social Systems (CPSS), we believe that Mobility has tremendous potential in shaping a new era of intelligent logistics and transportation services and will bring significant benefits to individuals, businesses, the government, and the entire society.

Modern Logistics Technology Based Ship Segment Outfitting Scheme

Shipbuilding occupies an important position in national defense and economy, which belongs to a typical large-scale complex production. In the modern shipbuilding mode, the segment outfitting process is the bottleneck of the whole shipbuilding. The outfitting cycle is long, and there are many resource constraints in the process. At present, China’s shipbuilding enterprises adopt the method of storage-yard based segment outfitting, which is relatively extensive in management and low in efficiency. This article points out that storage-yard based segment outfitting depends on complex dynamic scheduling, involving complex theoretical planning problems such as three-dimensional spatiotemporal coordination and flatbed transporter scheduling. Inspired by the segment outfitting line proposed in Germany and the modern logistics technology, the station design of the segment outfitting line was determined, and the centralized distribution with the intelligent logistics mode of outfitting parts is analyzed. The material and personnel coordination framework of the segment outfitting line is established. In the end, an assembly line based collaborative scheme of ship segment outfitting is proposed, with the characteristics of plan oriented, comprehensive perception, and dynamic synergistic.

Application and Prospect of Intelligent Manufacturing in Practice

Intelligent manufacturing is the core technology paradigm of the new round of scientific and technological revolution, and the main direction of strengthening the integration of digital and real technology and building a manufacturing power. This paper focuses on intelligent manufacturing. Take intelligent mechanical engineering, intelligent logistics, industrial robots and intelligent production workshops as examples. Exploring the phased achievements and applications of intelligent manufacturing technology. Research showed that the realization of intelligent manufacturing has adopted technologies such as the Internet of Things, big data, cloud computing, machine learning, intelligent sensing, interconnection and remote operation and maintenance, effectively solving many current engineering and scientific research problems. Indicating that the intelligent manufacturing industry supported by information technology and advanced digital technology has broad application prospects. It also provides reference for the application and high-quality development of intelligent manufacturing.

Analysis of Intelligent Logistics and Supply Chain Management Reform in the Digital Era

Owing to the constant progress and application of information technology, the digital transformation of intelligent logistics and supply chain management has become an inevitable trend. In the digital era, enterprises need to rely on various technical means to build intelligent logistics and supply chain management systems, improve operational efficiency and management level, to adapt to market changes and customer needs.

CADEIA DE SUPRIMENTOS INTELIGENTE: ESTUDO DE CASO EM UMA EMPRESA DO SUBSETOR DE CONCENTRADOS PARA BEBIDAS NÃO ALCOÓLICAS DO POLO INDUSTRIAL DE MANAUS

At the present time, industries are experiencing digital transformation with the advent of the 4th Industrial Revolution, also called Industry 4.0. Although Industry 4.0 started as an industrial policy, the new technologies of Industry 4.0 can be implemented in a practical way to create different solutions to the needs of companies. In this way, Industry 4.0 is considered as a new stage of maturity for companies. In this sense, this work has as main objective the measurement of the level of maturity in relation to Industry 4.0 technologies in the supply chain of a company. For that, a case study was carried out in a factory, in the subsector of concentrates for non-alcoholic beverages, in the Industrial Pole of Manaus. This is a quantitative-qualitative and descriptive research. At first, some maturity models were identified, where specific models for the intelligent supply chain were found. Being chosen, for application in the case study, the Maturity Model for Logistics 4.0, based on 07 dimensions proposed by Facchini (2020). The selected model focuses on identifying the company's maturity level in the implementation of Industry 4.0 technologies in its logistics processes. The result of applying the chosen model indicated strong integration between the management, material flow and information flow of the participating company and pointed out implemented advanced solutions that improved the entire supply network (inbound logistics) and distribution (outbound logistics), being observed potential points of improvement in its operations with regard to the transition to Logistics 4.0. The evaluation of the result obtained with the scale of the model showed the level of maturity Managing for the dimensions Employees, IT Systems, Intelligent Products and Technologies; and level of maturity Adopting for the dimensions, Knowledge, Strategy and Leadership and Intelligent Warehouses, in relation to Intelligent Logistics. The study reflected the actions under development and ongoing investments in the participating company, in the quest to achieve intelligence in Supply Chain Management.

Enough Waiting for the Couriers: Learning to Estimate Package Pick-up Arrival Time from Couriers’ Spatial-Temporal Behaviors

In intelligent logistics systems, predicting the Estimated Time of Pick-up Arrival (ETPA) of packages is a crucial task, which aims to predict the courier’s arrival time to all the unpicked-up packages at any time. Accurate prediction of ETPA can help systems alleviate customers’ waiting anxiety and improve their experience. We identify three main challenges of this problem. First, unlike the travel time estimation problem in other fields like ride-hailing, the ETPA task is distinctively a multi-destination and path-free prediction problem. Second, an intuitive idea for solving ETPA is to predict the pick-up route and then the time in two stages. However, it is difficult to accurately and efficiently predict couriers’ future routes in the route prediction step since their behaviors are affected by multiple complex factors. Third, furthermore, in the time prediction step, the requirement for providing a courier’s all unpicked-up packages’ ETPA at once in real time makes the problem even more challenging. To tackle the preceding challenges, we propose RankETPA, which integrates the route inference into the ETPA prediction. First, a learning-based pick-up route predictor is designed to learn the route-ranking strategies of couriers from their massive spatial-temporal behaviors. Then, a spatial-temporal attention-based arrival time predictor is designed for real-time ETPA inference via capturing the spatial-temporal correlations between the unpicked-up packages. Extensive experiments on two real-world datasets and a synthetic dataset demonstrate that RankETPA achieves significant performance improvement against the baseline models.

Effect of the logistics industry on the promotion of China's position in the global value chain: An international trade perspective

In an increasingly complex international environment and deepening economic globalisation, the logistics industry, as the basis of international trade circulation, has a far-reaching effect on promoting a country's position in the global value chain. Based on the perspective of international trade, this study presents a two-way fixed effects model using the 1995–2018 panel data of 59 economies. The empirical results show that the development of the logistics industry can significantly promote the status of a country's global value chain. National economic development level, human capital, technological innovation and application level, and logistics infrastructure level are the main channels through which this promotion effect takes place. Therefore, China should accelerate the construction of export logistics infrastructure and cross-border intelligent logistics systems, and enhance the customs clearance and logistics hub capacity of its ports. ‘Reinforcing’, ‘strengthening’, and ‘extending’ the chain as a means to optimise chain integration force will allow smooth circulation at home and abroad, high-quality development of the logistics industry, and greater opportunities. Ultimately, this will promote the status of the global value chain in the high-quality development stage of the national economy.

Design of Logistics Intelligent Warehouse Management System Based on Radio Frequency Identification Technology for 5G

At present, the operation energy consumption of intelligent logistics warehouse management system based on B/S is too large, and the operation efficiency is low. In order to improve this situation, a design method of intelligent logistics warehouse management system based on radio frequency identification technology is proposed to optimize the hardware structure of the system. Based on the overall structure of the system, the network topology of the warehouse management system is divided into physical level, network level, data level, and application level. Further optimize the function and operation of the system software based on RFID technology, transfer the goods to the warehouse by logistics, count the goods, determine the goods location in the warehouse management system in the outbound management procedure, and collect the internal data of outbound goods through the RFID reader. Use the handling tools to transfer the picked goods to the warehouse out. The user management module mainly controls user login and user management. The statistical management of inventory is to clear and count the goods, which can provide a basis for the research in the field of intelligent logistics warehousing.

An Optimization-Based High-Precision Flexible Online Trajectory Planner for Forklifts

There are numerous prospects for automated unmanned forklifts in the fields of intelligent logistics and intelligent factories. However, existing unmanned forklifts often operate according to offline path planning first followed by path tracking to move materials. This process does not meet the needs of flexible production in intelligent logistics. To solve this problem, we proposed an optimized online motion planner based on the output of the state grid as the original path. Constraints such as vehicle kinematics; dynamics; turning restriction at the end of the path; spatial safety envelope; and the position and orientation at the starting point and the ending point were considered during path optimization, generating a precise and smooth trajectory for industrial forklifts that satisfied non-holonomic vehicle constraints. In addition, a new rapid algorithm for calculating the spatial safety envelope was proposed in this article, which can be used for collision avoidance and as a turning-angle constraint term for path smoothing. Finally, a simulation experiment and real-world tray-insertion task experiment were carried out. The experiments showed that the proposal was effective and accurate via online motion planning and the tracking of automated unmanned forklifts in a complicated environment and that the proposal fully satisfied the needs of industrial navigation accuracy.