Intelligent Warehouse Research Articles

A route and speed optimization model to find conflict-free routes for automated guided vehicles in large warehouses based on quick response code technology

This paper focuses on exploring conflict-free routes for automated guided vehicles (AGVs) based on quick response (QR) code technology. Intelligent warehousing has become an industrial development trend, with an increasing number of enterprises deploying AGVs based on QR code technology for transportation tasks, especially in large warehouses. Unlike the early single lane path following navigation systems, AGVs using QR code technology can freely switch tracks at any position. And two AGVs can be accommodated side by side. The conditions of conflict-free routes are analyzed firstly when AGVs use QR code navigation system. After that, a route and speed optimization model (RSOM) that aims at minimizing energy consumption and taking conflict-free routes with time windows as the constraint condition is established. Then a branch-and-bound (B&B) algorithm scheme is proposed by altering AGV routes or speed to arrive at schemes with conflict-free routes to complete transportation tasks. Lastly, the effectiveness and validity of our proposed model and algorithm are verified by numerical examples and a case study. The results show that resolving conflicts during route planning for AGVs guided through QR code have advantages over avoiding collision in the running phase by technology and path following techniques in both aspects of solution time and energy. In addition, the established B&B algorithm has better performance than the rapid-exploringrandomtree (RRT) algorithm in numerical examples. The gaps between them widen as the size of AGVs increases. The research work has potential for application to meet the requirements of AGV development.

Design of Dual-LiDAR High Precision Natural Navigation System

With the further development of online shopping and the impact of the COVID-19 pandemic, the logistics industry has further increased the demand for unmanned, automated warehousing and logistics handling. To realize intelligent warehousing and logistics handling, reliable positioning navigation technology is indispensable. Therefore, this paper designs a Dual-lidar high-precision natural navigation system based on the ROS (Robot Operating System) platform, which can fulfill the basic warehousing and logistics requirements. The natural navigation system uses the Lidar-SLAM method based on graph optimization to construct the 2D environment map, the PF (Particle Filter) algorithm in MRPT (Mobile Robot Programming Toolkit) is used for system positioning, and the real-time correction algorithm is used for motion control. On the built hardware platform, the navigation system completed the fixed-point cruise navigation task, and finally achieved a navigation accuracy of 4 cm and an average repeatable navigation accuracy of 6 mm. The designed navigation system has reference significance for multi-sensor fusion navigation. In reality, it can be applied to the transportation of warehousing and logistics, and it is expected to be mass-produced in the future.

Research and implementation of high priority scheduling algorithm based on intelligent storage of power materials

The Smart Supply Chain technology has been gradually established and improved as a popular power system over different application fields, which raises the development of informatization and intelligence in intelligent warehouse management. Intelligent scheduling systems are eagerly required to standardize warehouse operation management and intelligently schedule inbound and outbound tasks, maximizing the rational use of intelligent devices and realizing the automation unmanned operations in actual scheduling situations. Since the scheduling algorithm is the key component of the intelligent scheduling system that acts as the most powerful tool adding automation and intelligence to electric power material storage, this paper studies the principle of the high-priority based scheduling algorithm carefully and proposes a novel data model based on the algorithm to improve the execution efficiency and performance of intelligent scheduling system. The superiority of the proposed algorithm is verified by its principle analysis and comparing with other state-of-the-art methods, ensuring that our algorithm is perfectly applicable in actual scheduling systems. Additionally, we design an efficiency scheduling system satisfying the needs of intelligent warehouse management, and further demonstrate our algorithm works well in realizing the automatic loading and unloading operation in the storage of electric power materials under unmanned condition with simulation experiments.

Research on Multi-side Joint Industrial Parts Inspection Based on Model Fusion

ABSTRACT Rapid identification of many industrial material parts is the key to effectively improving the efficiency of the industrial production process and intelligent warehousing. Accurate identification of both similar and special parts is an important problem. In this paper, a multi-side joint industrial part recognition method based on model fusion learning is proposed. A multi-channel visual acquisition system is designed to construct a fast industrial part data set with time and space consistency. Two joint identification methods for multi-side acquisition are proposed, and the classification results are fused to improve the model’s accuracy and solve the classification problem for similar parts. The experimental results show that compared with the traditional model, the prediction accuracy of the multi-channel multi-input model proposed in this paper is improved by approximately 6%, and the accuracy of the single-channel multi-input model is improved by approximately 10%. The accuracy of part recognition is far better than that of the traditional model, and it therefore provides a new strategy for the rapid training and recognition of industri-al parts in intelligent storage.

Research on Intelligent Warehousing and Logistics Management System of Electronic Market Based on Machine Learning.

This study applies the Internet of things information-aware technology to the process of electronic market warehousing and logistics management, effectively perceives warehouse electronic product logistics information, and improves the real-time perception of electronic product logistics information and the efficiency of electronic product storage logistics management. This study first analyzes the needs of the intelligent electronic market warehouse logistics management system and then builds the IoT architecture of the intelligent warehouse logistics assembly logistics management system for electronic warehouses based on machine learning algorithms, which solves the problems that exist in the current workshop electronic market warehouse logistics management. Then, the principle of RFID technology is introduced. The accuracy of RFID tag estimation is analyzed by the PEPC tag estimation algorithm. It is concluded that the PEPC tag estimation algorithm reduces the tag estimation error and improves the accuracy of tag estimation. Finally, an intelligent warehousing logistics management system based on IoT RFID technology is established. The test results show that the system can meet the requirements of intelligent warehousing function in the electronic market, which will greatly improve the warehousing efficiency of electronic products.

E-Commerce Logistics Inspection System Based on Artificial Intelligence Technology in the Context of Big Data

With the development of science and technology, the logistics industry has generally introduced information technology, and the construction of intelligent warehouse management is the development trend of the logistics industry. In order to improve the operation efficiency of intelligent warehouse and make full use of freight resources, this paper proposes an intelligent logistics inspection system based on big data. The system takes the platform data of unmanned monitoring points with multiple data sources as the research object and also uses a deep learning-based method for parcel detection and tracking in order to overcome the defects of traditional parcel detection and tracking technology. The role of the system is to obtain the parcel flow data of each unmanned monitoring point and then upload these important data to the scheduling system of the warehouse to realize the intelligent management of the warehouse.

Intelligent Transportation Logistics Optimal Warehouse Location Method Based on Internet of Things and Blockchain Technology.

In order to cut down on costs to the greatest possible extent, enterprises hope to distribute goods to different customers at the lowest costs possible. Based on this, this paper proposes an optimal location method for an intelligent transportation logistics warehouse. This scheme combines a variety of complex mechanisms to allow IoT devices to provide input. The scheme makes full use of the irreducibility of a blockchain system to promote the development and design of blockchain logistics applications. This method is aimed at tracking the progress of transportation of products in the whole supply chain. Experimental results show that, compared with traditional methods, the optimal positioning method has the advantages of fewer calculations, a high positioning accuracy, and a low overall cost, and it obtains the best warehouse positioning results. Based on the Internet of Things and blockchain technology, the application of intelligent logistics systems enables enterprises to intuitively understand their current inventory and the transportation status of goods, thus better controlling changes in enterprise resources.

Research on Path Planning Algorithms of Multiple Mobile Robots in Intelligent Warehousing

Research on Path Planning Algorithms of Multiple Mobile Robots in Intelligent Warehousing

Overseas Hoisting and Handling Equipment Innovations

Digitalization and automation determine the main trends in the development of logistics at the present stage. The same trends set the way for the improvement of handling equipment, as the basis for the development of logistics and mainly internal logistics. An important place in the implementation is occupied by fl oor vehicles, especially transport systems with fl oor carts operating without drivers. The innovative development of these systems is considered. The relevance of automating the determination of the dimensions and mass of cargo is noted, data are given on the systems in which these functions are implemented. The use of robots, including collaborative ones, in lifts, cranes and other types of equipment is described. Examples of the use of new technical means for IT-technologies are shown.Innovations in handling and storage equipment abroad are determined by the requirements of digitalization and automation. This also provides for the introduction of some IT-technologies where automation is ineff ective due to the small amount of work or for reasons of economic effi ciency.Automatic cranes, various types of lifts, automatic sorting systems, automatic systems and means for measuring the mass and size of goods, and other equipment have been widely used.All these innovations create the conditions for dynamic, digitized, intelligent warehouse logistics and modern internal transport in industrial plants and warehouses.Foreign innovative experience should be used in our country in the implementation of promising projects and programs in the fi eld of transport and logistics.

Intelligent Warehouse Robot Scheduling System Using a Modified Nondominated Sorting Algorithm

In intelligent warehouse, the problem of transporting goods in intelligent warehouse is becoming increasingly complex, and the traditional way of automatically guiding vehicles (AGVs) is inefficient, so automated robot systems are introduced into intelligent warehouses. In this paper, a task assignment model for robots is presented with the transportation problem of robots in intelligent warehouse as the research background. To solve the robot task assignment problem in intelligent warehouse, a novel Pareto‐based multiobjective optimization algorithm (MOEA) is proposed, and the aggregation function is invoked to replace the crowding distance; the brain storm operator is used for crossover and mutation. Finally, the ability of the algorithm to solve the benchmark test problem suite and real‐world problems is experimentally confirmed.

JD.com: Operations Research Algorithms Drive Intelligent Warehouse Robots to Work

JD.com pioneered same-day delivery as a standard service in China’s business-to-consumer e-commerce sector in 2010. To balance the urgent need to meet growing demands while maintaining high-quality logistics services, the company built intelligent warehouses that use analytics to significantly improve warehouse efficiency. The brain of the intelligent warehouse system is the dispatching algorithm for storage rack-moving robots, which makes real-time dispatching decisions among robots, racks, and workstations after solving large-scale integer programs in seconds. The intelligent warehouse technology has helped the company decrease its fulfillment expense ratio to a world-leading level of 6.5%. The construction of intelligent warehouses has led to estimated annual savings of hundreds of millions of dollars. In 2020, JD.com delivered 90% of its first-party-owned retail orders on the same day or on the day after the order was placed. The agility of such intelligent warehouses has allowed JD.com to handle 10 times the normal volume of orders during peak sales seasons and has also helped the company respond quickly to COVID-19 and ensure the rapid recovery of production capabilities.

Research on intelligent path planning technology of logistics robots based on Giraph architecture

In the development of modern e-commerce, the logistics robot is an important part of the intelligent warehouse. Especially in large-scale warehouses, the operation efficiency of logistics robots largely depends on the processing ability of patrol efficiency. In order to solve the problems of difficult path planning and complex operation in the logistics robot operating system, based on the analysis of the traditional path algorithm, an improved logistics robot path planning algorithm based on graph architecture, CAA*path planning algorithm, is proposed and used to solve the conflict-free path planning problem of the logistics robot in the intelligent logistics warehouse. The improved CAA*path planning algorithm is compared with the traditional a*algorithm, ant colony algorithm, simulated annealing algorithm, ant colony optimisation algorithm and genetic algorithm. The simulation analysis shows that the improved CAA*path planning algorithm is more efficient in time operation. Under the same number of tasks and the same map specification, the improved CAA*path planning algorithm takes less time and runs more efficiently.

Extending the Intelligent Digital Twin with a context modeling service: A decision support use case

Considering the surrounding context of manufacturing systems at runtime enhances their adaptability, which is advantageous given the increasing demand for flexible production. Context-awareness presents a viable approach to continuously model the surrounding context and relate it to the system’s operation. As the surrounding context is both acquirable from heterogeneous data sources and is dynamic in nature, a coherent and evolving context model is necessary. The context modeling approach needs to be both: system-centric to enable re-usability of the model by different applications as well as continuously and efficiently extendable to continuously represent the environment. Some approaches to model context within the manufacturing domain exist, but are mostly tailored to specific applications or do not consider an extension at runtime. To include the surrounding context of a system effectively and adapt it accordingly, a relation between the modeled context and the system during runtime is necessary. The intelligent Digital Twin provides essential interdisciplinary models of a physical system, which can be used for its analysis and monitoring. Adding a further service in the form of a model to represent the surrounding context enhances the intelligent Digital Twin to be used by various applications, e.g., for reconfiguration or decision-support. In our previous work, we have presented a tier-based model to realize context-awareness for intelligent Digital Twins. The presented tier model has different levels corresponding to different context scopes. In this contribution, we present an approach to realize an extendible and evolving tier-based context model with a generic ontology and model transformation to a labeled property graph with focus on the external context. To show the applicability and added value of the context model, a use case is presented, which addresses an intelligent warehouse’s Digital Twin and its context-model supported decision-making.

Design and Performance Estimation of Mixed-Robotic Fulfillment System

E-commerce retailers face the challenge to assemble a large number of time-critical picking orders. Common parts-to-picker autonomous intelligent warehouses such as automated vehicle storage and retrieval system and robotic mobile fulfillment system are often a little ill-suited for these prerequisites. A mixed-robotic fulfillment system is a hybrid robot picking system based on multi-device collaboration. It is a fusion innovation of traditional automated vehicle storage and retrieval system and robotic mobile fulfillment system. This paper comprehensively considers the characteristics of the system and customer demand, through the construction of a queuing network model to evaluate the performance of the system. A series of problems such as order service time, throughput capacity, and vehicle quantity configuration are analyzed experimentally. The validity of the model is verified by a simulation model.

Development of a Group Decision Support System with the Multi-Stage Multi-Attribute Group Decision Making (MS-MAGDM) Method on the Intelligent Warehouse Management System

Purpose: to find a solution with MS-DAGDM for the problem of different criteria used by decision maker at each stage.Design/methodology/approach: This research was conducted using literature review with a study of the theory of decision-making methods, group decisions, suplier selection processes, and factors that influence decisions in the context of warehousing and MS-MAGDM to solve the problems.Findings/result: This research find that GDSS prototypes which have four methods in making decisions. First, Analytical Hierarchy Process for weighting the division head level. Second, TOPSIS for divison head level decisions. Third, Hybrid Weight Averaging (HWA) manager level. Fourth, Time Weight Averaging (TWA) for manager level decisions.Originality/value/state of the art:The decision-making model of the GDSS system in this study combines four methods at each level of management. The section head level uses AHP for the level weighting and TOPSIS for decision making. Level managers use Hybrid Weight Averaging (HWA) weighting and Time Weight Averaging (TWA) for decisions. The combination of these methods is carried out using a Poisson distribution, for HWA and TWA operators to combine individual decisions into group decisions. Tujuan: Fokus penelitian ini adalah mencari solusi dengan MS-MAGDM untuk permasalahan perbedaan kriteria yang dipergunakan pembuat keputusan dalam setiap stage.Perancangan/metode/pendekatan: Metode yang digunakan yaitu kajian kepustakaan dengan kajian terhadap teori metode pembuatan keputusan, keputusan kelompok, proses pemilihan supplier, dan faktor yang berpengaruh pada keputusan dalam konteks pergudangan serta MS-MAGDM untuk menyelesaikan permasalahan tersebut.Hasil: Hasil penelitian ini berupa purwarupa GDSS yang memiliki 4 metode dalam pembuatan keputusan yaitu Analytical Hierarchi Process (AHP) untuk pembobotan level kepala bagian, TOPSIS untuk keputusan level kepala bagian, Hybrid Weight Averaging (HWA) pembobotan pada level manager dan Time Weight Averaging (TWA) untuk keputusan level managerKeaslian/ state of the art:Model pengambilan keputusan sistem GDSS penelitian ini menggabungkan 4 metode pada setiap tingkatan manajemen. Level kepala bagian menggunakan AHP untuk pembobotan level dan TOPSIS untuk pembuatan keputusan. Level manager menggunakan Hybrid Weight Averaging (HWA) pembobotan dan Time Weight Averaging (TWA) untuk keputusan. Penggabungan metode dilakukan menggunakan distribusi Poisson, untuk operator HWA dan TWA guna memadukan keputusan individu mejadi keputusan kelompok.

Intelligent warehouse task information flow scheduling under fog computing environment

Traditional cloud computing systems used in intelligent warehouses are managed in a centralized manner. Faced with more and more intelligent terminal devices in the warehouse, the resources of the intelligent warehouse cannot be fully utilized. Therefore, a distributed approach is adopted to deploy fog computing nodes near smart terminal devices to provide real-time computing response. The network architecture of intelligent warehouse based on fog computing is designed, and a real-time task scheduling model is established considering the time delay problem. In this model, an improved artificial bee colony algorithm is used to schedule the task information flow and allocate a reasonable amount of computing data to the fog nodes. The simulation results show that the error between the calculated delay and the calibrated delay on the fog node is within the specified range and can meet the requirements of different task types for the time delay.

Warehouse management and informatization in industrial application under the context of “Internet +”

Manufacturing is the cornerstone of comprehensive national power, and ware housing is an important link of manufacturing production activities and a key node of t he logistics process. Under the guidance of the Made in China 2025 strategy, the const ruction of an efficient and rational information warehouse has become an important wa y to transform and upgrade the manufacturing industry. To achieve this goal, a scientif ic and rational management model is required, and accounting is an integral part of it. With the depth of the “Internet+” era, it is of great practical significance to promote the level of accounting informatization. This paper focuses on small and micro manufactu ring enterprises, with the goal of enhancing their accounting informatization level, and optimizes the business process design of warehousing, hoping to provide some referen ce for manufacturing enterprises to build intelligent warehousing systems.

Intelligent warehouse monitoring based on distributed system and edge computing

This paper mainly focuses on the volume calculation of materials in the warehouse where sand and gravel materials are stored and monitored whether materials are lacking in real-time. Specifically, we proposed the sandpile model and the point cloud projection obtained from the LiDAR sensors to calculate the material volume. We use distributed edge computing modules to build a centralized system and transmit data remotely through a high-power wireless network, which solves sensor placement and data transmission in a complex warehouse environment. Our centralized system can also reduce worker participation in a harsh factorial environment. Furthermore, the point cloud data of the warehouse is colored to visualize the actual factorial environment. Our centralized system has been deployed in the real factorial environment and got a good performance.

Design of Warehouse Management System Based on L Company

The rapid development of modern society, for any enterprise, especially manufacturing and logistics enterprises, will be the external power to improve management efficiency. In 2020, COVID-19's impact has caused heavy losses to all enterprises. The improvement of the efficiency of warehouse management in manufacturing enterprises is imminent. Taking L company as an example, this paper develops a C / S warehouse management system based on C # language, in order to realize intelligent warehouse management and efficient warehouse operation, such as material information management, data entry of warehouse in and warehouse out operation, inventory checking, etc., to help enterprises improve efficiency and reduce costs.

Non-negative discriminative collective target nearest-neighbor representation

In intelligent warehouse logistics, image classification is widely used. However, the performance of traditional image classifiers suffers when deployed in new environments because of the lack of labels collected. In this paper, aiming at solving the problem above, a novel method named non-negative discriminative collective target nearest-neighbour representation (NDCTNNR) is proposed. Inspired by the Collective target nearest-neighbour representation (CTNNR), this method introduces a novel regularization term to integrate class discrimination and data locality. Moreover, our method uses non-negative representations to make collaborative representation sparser. The experimental results confirm the effectiveness of the proposed method.