Research to key success factors of intelligent logistics based on IoT technology

Chen, Xiaozhong; Chen, Rongli; Yang, Cairu

doi:10.1007/s11227-021-04009-7

Cited by 16 publications

(5 citation statements)

References 29 publications

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“…In the intelligent logistics management system designed in this paper, RFID technology is used to identify and label the pallets, cargo spaces, and shelves to form a data carrier, which is convenient for reading or writing related item data information, and at the same time, it is transmitted to the computer for processing. Te ROS robot is used as a handling tool, and the label is used as a positioning label for multiple vehicles as well as a data carrier [14,15]. At the same time, the system performs robot management, route management, monitoring management, and task management on the established user management system by digitizing the warehouse environment.…”

Section: Design Of Intelligent Logistics Management Systemmentioning

confidence: 99%

Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot

2023

Journal of Robotics

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With the rapid development of the Internet of Things (IoT), the logistics and transportation industry is booming. At the same time, with the advancement of AI technology, intelligent logistics is also gradually emerging, and the purpose of intelligent logistics is to use different types of automatic guided transport machines to replace people to handle and move products. At the same time, with the help of big data, cloud computing, artificial intelligence, sensor technology, and other technologies, we can achieve logistics automation. However, the current logistics robot creation platforms are diverse, which makes intelligent logistics robots diverse in variety and wide in application and also makes the creation and use of robots more challenging. Robot Operating System (ROS) is an open-source software platform that supports programming in multiple languages and has excellent adaptability. In addition, most of the currently used path planning focuses on a single target point, which is insufficient to support the current needs of multitasking in intelligent logistics. Therefore, this paper aimed to design an intelligent logistics management system based on ROS robot and proposed to use the A-star algorithm to calculate the shortest path of the robot so as to achieve the optimal path. In the simulation experiment, 20 ROS robots were selected and divided into two groups. In the logistics warehouse of different transportation nodes, 20 ROS robots were set up to transport goods of different weights in the experiment, and the transportation data were collected at last. The final simulation results have shown that the power consumption and response delay performance of the ROS robot are good, and the logistics transportation speed is significantly improved. In addition, compared with the traditional transportation method, the daily transportation weight of each robot is up to 310.1% and the monthly profit is up to 171%, which shows that the intelligent logistics management system designed in this paper is more efficient in logistics and transportation and can bring more profits.

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Section: Design Of Intelligent Logistics Management Systemmentioning

confidence: 99%

Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot

2023

Journal of Robotics

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“…The conical stacking algorithm and the hybrid rounded algorithm are commonly used in feature extraction and categorization of intelligent data [22]. These algorithms are used to obtain the optimal solution by hierarchically dividing the obtained data into two types, optimal and discretized, and thus achieve the solution of the target problem [23]. In recent years, tapered iterative methods have been widely used for many speci c engineering problems.…”

Section: Resultsmentioning

confidence: 99%

A Study on IOT Technology and Its Application in the Appreciation of Tang and Song Literature

Sheng

Jia

2023

Preprint

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How to achieve efficient appreciation and intelligent parsing of Tang and Song literatures are two major difficulties in the field of studying Tang and Song literatures nowadays. Based on this, this paper investigates the application of multidimensional spatio-temporal IoT parsing model combined with cone stacking algorithm in the appreciation of Tang and Song literatures. Firstly, we summarize the current research status of Tang and Song literature appreciation, and propose a multidimensional spatio-temporal IoT analysis model based on cone stacking algorithm according to the characteristics of Tang and Song literature, and combine the application ideas of various appreciation models in Tang and Song literature appreciation to normalize the multidimensional analysis of traditional Tang and Song literature database to achieve accurate appreciation from different perspectives, and then analyze the accuracy and objectivity of the appreciation model. The accuracy and objectivity of the appreciation model are then analyzed. Secondly, the set of evaluation functions of the multidimensional spatio-temporal IoT analysis model in this application is constructed, and the characteristics and appreciation ideas of current literary masters are combined in the optimal appreciation, and the error of the composite appreciation analysis function is improved, while the cone stacking algorithm is combined with the multidimensional spatio-temporal IoT analysis appreciation model to realize the internal intelligent learning. Finally, a validation experiment is designed, and the results show that the multidimensional spatio-temporal IoT analysis model based on the cone stacking algorithm can quickly and accurately achieve the appreciation of conventional Tang and Song literature from multiple levels.

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“…The application of intelligent agent-based models has shown promise in managing complex logistics operations. Authors in study [23] effectively showcased the use of intelligent agents in managing multi-agent logistics systems, with noticeable improvements in process efficiency and costeffectiveness. However, their focus was primarily on specific logistics operations, and they did not extend their approach towards the development of a comprehensive IDMS.…”

Section: Intelligent Agent-based Modelsmentioning

confidence: 99%

Development of Intellectual Decision Making System for Logistic Business Process Management

Kozhamkulova,

Kuntunova,

Amanzholova

et al. 2024

IJACSA

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This research paper delves into the design and development of an Intellectual Decision Making System (IDMS) incorporated into a Logistic Business Process ManagementSystem (LBPSMS), employing advanced Machine Learning (ML) models. Aimed at streamlining and optimizing logistics business operations, the focal point of this study is to significantly elevate efficiency, enhance decision-making precision, and substantially reduce operational costs. This research introduces a pioneering hybrid approach that amalgamates both supervised and unsupervised machine learning algorithms, creating a unique paradigm for predictive analytics, trend analysis, and anomaly detection in logistics business processes. The practical application of these combined methodologies extends to diverse areas such as accurate demand forecasting, optimal route planning, efficient inventory management, and predictive customer behavior analysis. Empirical evidence from experimental trials corroborates the efficacy of the proposed IDMS, showcasing its profound impact on the decision-making process, with clear and measurable enhancements in operational efficiency and overall business performance within the logistics sector. This study thus delivers invaluable insights into the realm of machine learning applications within logistics, extending a comprehensive blueprint for future research undertakings and practical system implementations. With its practical significance and academic relevance, this research underscores the transformative potential of machine learning in revolutionizing the logistics business process management systems.

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Research to key success factors of intelligent logistics based on IoT technology

Cited by 16 publications

References 29 publications

Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot

Optimization Path and Design of Intelligent Logistics Management System Based on ROS Robot

A Study on IOT Technology and Its Application in the Appreciation of Tang and Song Literature

Development of Intellectual Decision Making System for Logistic Business Process Management

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