Warehouse Logistics Research Articles

Optimising Warehouse Navigation: A Novel Two-Dimensional Grid Model for Robot Path Planning in Warehouse Logistics

Optimising Warehouse Navigation: A Novel Two-Dimensional Grid Model for Robot Path Planning in Warehouse Logistics

Dynamic path finding for multi-load agent pickup and delivery problem

Recently, the Multi-Agent Pickup and Delivery (MAPD) problem has attracted widespread attention from both academia and industry. In the MAPD problem, each task has its pickup and delivery locations, and the agent needs to pick this task up from the pickup location and deliver it to its delivery location. Therefore, existing works consider the MAPD problem as the core problem in industrial scenarios, e.g., logistics warehouse. Note that the agents considered in the MAPD problem are single-load agents that complete tasks one by one. However, many commercial companies have deployed agents with multi-load instead of single-load agents to improve efficiency and reduce costs. The agents with multi-load can complete multiple tasks at once, so existing solutions cannot work well with the MAPD problem for multi-agents. To solve this issue, we investigate a novel problem in this paper, namely the Multi-Load Agent Pickup and Delivery (MLAPD) problem, where the agents with multi-load not only need to complete assigned real-time tasks but also need to avoid conflicts with each other and the goal is to minimize the total cost in the warehouse. To address this novel problem, we develop a task assignment to complete the assignments between multi-load agents and online tasks in real-time and a dynamic path finding problem that enables multi-load agents to move along conflict-free paths. Finally, extensive experiments in two different warehouses examine the effectiveness of our solutions.

Математическая модель выкладки товаров на полочном пространстве складского стеллажа с одновременной горизонтальной и вертикальной категоризацией товаров

The organization of the product allocation on the shelf space of the warehouse rack with the creation of separate categories allows for a more rational use of available resources. This allows for optimization of warehouse space by ensuring a compact and logical product arrangement, making them easy to find and retrieve. This approach also reduces the amount of time spent on processing orders and reduces the likelihood of picking errors. As a result, the overall efficiency of warehouse operations and inventory management is improved. The purpose of the study is to develop a mathematical model of product allocation on a warehouse rack with simultaneous categorization of goods and shelves vertically and horizontally. The study is important for warehouse logistics.

Development of intelligent mechatronic motion module of mobile industrial robot

Modern industry is inextricably linked with mobile industrial robots, which plays a key role in production processes and warehouse logistics. The intensive introduction of mobile industrial robots has increased the need to improve the energy efficiency of robotic installations due to their significant impact on overall production costs. At the same time, there is a pressing need to reduce the time required to develop and implement mobile industrial robots. This leads to the need to improve and optimize the properties of all components that make up a mobile industrial robot. Currently, electric drives are most widely used in mobile industrial robots. Research shows that the use of modern high-tech solutions in the construction of electric drive control systems can improve the energy efficiency of mobile industrial robots. In turn, the introduction of an intelligent component into mechatronic motion modules allows to reduce the time required for their construction and integration into mobile industrial robots. The purpose of the study is to increase the energy efficiency of an intelligent mechatronic motion module by using a multi-motor electric drive in the movement system of a mobile industrial robot and to develop a stable and energy-efficient intelligent automatic control system based on a neural network, taking into account the nonlinear properties of the control object under conditions of uncertainty.

AR for Warehouses: a Marker-based Augmented System for Navigation and Inventory Management

This study investigates using augmented reality (AR) to assist warehouse operators. By employing head-mounted displays (HMDs), the AR application overlays relevant navigational information and inventory data directly onto the user’s field of vision, enabling a hands-free interaction system that creates a more efficient workflow. Incorporating a marker-based AR architecture offers precise item localization and navigation support, addressing common issues such as time-consuming item checks and potential navigational errors while moving in the warehouse. Through the assistance of these processes, the AR system aims to enhance the efficiency and precision of warehouse operations. This research highlights the advantages and potential challenges of implementing AR solutions in industrial settings, emphasizing the need for innovative warehouse logistics and management approaches.

Towards human-centric warehousing: the impact of rack configuration and cognitive demands on order picking performance

Within warehouse logistics processes, design concepts and management decisions regarding operations systems determine the demands placed on human operators. While the literature proposes that human-centric work system design positively affects the operator, the impact on operator performance remains under-explored. We analyse the case of a German retail warehouse where the shelf numbering follows the pickers' walking direction from right to left. However, all human order pickers working in this system are familiar with a left-to-right writing system due to their schooling and training. Because cognitive psychology proposes that writing system direction impacts spatial orientation in goal-oriented visual search, we are motivated to explore the interplay of rack configuration and human cognitive demands. We ground our mixed-effects model on archival data, including 470,968 storage location visits. Our results indicate that order pickers work 18.7% faster when picking items starting from the left side of the shelf compared to starting their visual search from the right side. Our findings suggest that human search strategies in warehousing operations are influenced by attention templates like writing direction on a similar level as by system design elements of the warehouse itself. This insight underscores the potential for diversified future human-centric design approaches in warehouse logistics.

Direct Mail or Bonded Warehouse? Logistics Mode Selection in Cross-Border E-Commerce under Exchange Rate Risk

The rapid development of cross-border e-commerce (CBEC) has enabled more suppliers to expand into overseas markets, meeting increasingly diverse consumer demands. Selecting an effective logistics mode is a crucial issue for suppliers, yet uncertainty in demand and exchange rate fluctuations make this problem challenging. This paper considers a supply chain consisting of a supplier and a CBEC platform. Using a distributionally robust optimization approach, we provide optimal overseas warehousing strategies and logistics mode selection for suppliers, given partial distribution information of market demand and exchange rate, such as means, variances, and covariances. It is found that when demand and exchange rate fluctuations are small, the supplier chooses the bonded warehouse logistics mode to reduce costs. Conversely, when demand and exchange rate fluctuations increase, the supplier opts for the direct mail mode to respond flexibly to market risk. The correlation between exchange rate and demand also affects the choice of logistics mode. Specifically, with low correlation, the preference is for the bonded warehouse mode, whereas high correlation leads suppliers to choose the direct mail mode. In addition, the impact of demand and exchange rate fluctuations on suppliers’ overseas warehousing volumes depends on the product’s profit margin. These findings provide guidance for the selection of CBEC logistics under exchange rate risk.

基于势场蚁群算法的农业仓库物流搬运机器人路径优化研究

In the layout of modern agricultural warehouse logistics handling industry, it is an inevitable way to realize industrial upgrading by replacing people with mobile robots. Aiming at the problems that the existing obstacle avoidance control algorithm of agricultural handling robot is easy to fall into local optimal solution, and the operation process of agricultural warehouse logistics handling robot is prone to collision, the obstacle avoidance control of agricultural warehouse logistics handling robot is studied, and a control algorithm based on improved potential field ant colony is proposed. The moving trajectory of the agricultural warehouse logistics handling robot during the handling process is studied, and the spatial kinematics equation of the robot is given. The ant colony algorithm is used to optimize the classical artificial potential field algorithm to improve the global optimization ability and balance the interaction between gravity and repulsion. In the aspect of local area obstacle avoidance of agricultural storage and handling robots, the artificial potential field is optimized twice based on the strategy gradient algorithm. By analyzing the probability of the next action command, the randomness of the travel path selection when multiple robots work at the same time is improved. After testing, the path of the proposed control algorithm is the shortest, and under the condition of complex path planning, the number of collisions between robots is also significantly less than that of the traditional obstacle avoidance control algorithm. The practical application can meet the needs of improving the efficiency of warehouse logistics management.

Continuous charging assignment algorithm for heterogeneous robot clusters based on E-CARGO

Forming robot clusters is a common means to improve work efficiency and ensure personnel safety in intelligent logistics warehousing. However, designing a reasonable charging sequence and time for each robot in the cluster poses a major challenge for management personnel. In actual scenarios, the working and charging processes of the robots must be considered simultaneously. This study also considers multiple constraints on robot battery levels and available workstations under different parameters to ensure each team operates normally at any given time. Additionally, to enhance cooperation efficiency and alleviate the workload of management personnel, the problem is transformed from conventional one-time assignments to adaptive continuous assignments within fixed time periods. Accordingly, the ACP-GRAC problem is proposed. Firstly, a group role assignment model with conflicts is introduced to resolve assignment conflicts caused by mismatches between job skills and position requirements. Then, energy information is shared among robots during the charging process, adding adaptive cooperation concepts to address continuous charging issues. Finally, the E-CARGO model is utilized to formalize the ACP-GRAC problem, and the HCCA algorithm is designed for its solution. Furthermore, a classification backtracking strategy is proposed to simultaneously address the constraint of continuous operation at any given time and the problem of constraint violations caused by the HCCA algorithm’s pursuit of maximum team performance. Moreover, since the classification backtracking strategy can compute the theoretical minimum remaining energy of the robot before departing for charging, it further optimizes the overall work efficiency. Additionally, this study addresses the issue of low cluster work efficiency due to the unreasonable initial planning sequence of the algorithm. In the experimental section, comparisons are made with recent excellent heuristic algorithms and deep reinforcement learning algorithms. Experimental results demonstrate that B-HCCA exhibits significant advantages in both solution efficiency and solution quality when addressing adaptive cooperation problems with group role assignment conflicts.

The Optimization of Picking in Logistics Warehouses in the Event of Sudden Picking Order Changes and Picking Route Blockages

The Optimization of Picking in Logistics Warehouses in the Event of Sudden Picking Order Changes and Picking Route Blockages

Based on Internet + Warehousing Logistics Management Exploration

The deep integration of the Internet and warehousing logistics not only promotes the transformation and upgrading of the industry, but also brings the innovation of management mode and service concept for enterprises. This paper analyzes the development status of ware-housing logistics industry, analyzes the problems faced by warehousing logistics management in the Internet environment, and puts forward a series of optimization countermeasures and suggestions. Through the introduction of cloud computing, big data, Internet of Things and other advanced technologies, the establishment of intelligent storage management system, optimize the business process, improve the service level, promote warehousing and logistics enterprises to achieve cost reduction and efficiency, and enhance the core competitiveness. At the same time, it is also necessary to improve industry standards and norms, strengthen the construction of talent teams, build an Internet + warehousing and logistics ecosystem, and help build a strong logistics country.

Discrete event simulation and Digital Twins in warehouse logistics: a bibliometric and content analysis-based systematic literature review

ABSTRACT In Warehouse Logistics (WL), the growing adoption of Industry 4.0 automations coupled with the demand for efficient and almost instantly responsive decision-making has already created a paradigm shift from the traditional Discrete Event Simulation (DES) models into innovative new ones utilizing the inherent functionalities of ‘Digital Twins’ (DTs), one of today’s most disruptive Industry 4.0 technologies. DTs are ‘real-world aware’ through the creation of exact software replicas of actual systems, allowing the convergence of physical and virtual entities and facilitating real-time responsive decision-making. In this paper, a bibliometric and content analysis-based systematic literature review is performed to explore the adoption, implementation and evaluation of DES and DTs in WL. The authors focus on how and to what extent these two methods are integrated into the various WL theoretical and real-life applications to enhance a plethora of performance factors as well as look into their exploitation, as standalone solutions or in tandem with one another and other modelling and simulation approaches, technologies, algorithms, methods, methodologies and strategies in different contexts and environments. Based on the review results, a conceptual framework is proposed combining the multiple perspectives examined, contributing to the systematization and standardization of the field and pinpointing future research directions.

How effective is immersive VR for vocational education? Analyzing knowledge gains and motivational effects

While Immersive Virtual Reality (IVR) technology has been predominantly employed in technical and medical academic education, it also holds significant potential for Vocational Education and Training (VET). IVR's unique properties, such as high immersion could be especially beneficial in VET, where action-oriented skills, domain-specific knowledge, and their application in new work contexts are crucial. This study investigates the effectiveness of IVR in vocational education, focusing on (1) objective knowledge acquisition, (2) subjectively perceived knowledge acquisition, and (3) motivational effects in the domain of warehouse logistics. Through a randomized controlled trial with 72 vocational students, we compared IVR-based learning to traditional paper-based methods. Results show that IVR did not improve immediate declarative knowledge acquisition; in fact, the paper-pencil group outperformed the IVR group on an objective post-test. However, IVR significantly enhanced students' perceived knowledge gains. The study also confirms higher motivation and immersion in IVR settings compared to paper-based learning environments. The identified discrepancy between perceived and actual learning may help explain the unclear state of research regarding knowledge acquisition in IVR studies, based on the measures used. Moreover, the findings underscore the necessity for a nuanced approach to IVR implementation in VET education. While IVR can be recommended for enhancing short-term learner engagement, traditional methods or a blend of IVR and non-immersive techniques may be more effective for fostering declarative knowledge in the short term.

Design of a logistics warehouse robot positioning and recognition model based on improved EKF and calibration algorithm

Automatic guided vehicles for logistics warehousing are a key link in the construction of intelligent logistics. To improve the positioning accuracy of warehouse robots, we designed an advanced extended Kalman filter method integrating multiple synchronous positioning techniques and map construction methods, and completed the calibration and detection of pallets based on color image information. The results revealed that the proposed multi-innovation enhanced model achieved minimum relative rotation and absolute trajectory errors of 0.13 and 0.09, outperforming existing models. It showcased excellent mapping fidelity and integrity (above 0.9) across various datasets, with a high loop detection success rate (0.91) enhancing map precision. The tray fusion detection algorithm's AUC (area under the curve) reached 0.92, reflecting a balanced accuracy-recall tradeoff. This research offers robust positioning and mapping capabilities in logistics warehousing environments, effectively identifying errors and ensuring pallet accuracy. The detection error and accuracy of this method are better than the other three models, with the lowest average absolute error of 0.32 and the lowest root-mean-square error of 0.27, and the overall error in the detection of pallets is small. The findings provide strong theoretical backing and technical support for advancing intelligent logistics warehousing technology. Precise positioning and identification capabilities enable logistics and warehousing robots to accurately and quickly complete tasks such as access, handling and sorting of goods, greatly improving the efficiency of warehousing operations, promoting the digital transformation and intelligent development of the logistics and warehousing industry, and improving the competitiveness of the industry and the level of service.

New Technology in Marine Logistics Warehouse Management

The marine logistics industry faces challenges in improving efficiency, reducing operational costs, and supporting environmental sustainability. One crucial aspect that can be optimized is warehouse management, which is responsible for the storage, handling, and distribution of cargo. This research aims to explore the application of new technologies such as the Internet of Things (IoT), artificial intelligence (AI), robotics, automation, and green technologies in marine logisticswarehousemanagement. Themethodology used includes a comprehensive literature review, in-depth interviews with industry experts, and analysis of case studies of new technology implementation in marine logistics companies. The results revealed significant opportunities and benefits from the implementation of new technologies, such as improved visibility and real-time monitoring of cargo, optimization of cargo storage and handling, increased productivity and safety, and reduced operational carbon footprint. However, challenges such as initial investment costs, data security, system interoperability, and human resource training needs must be overcome. This research presents a comprehensive implementation strategy, including cost-benefit analysis, development of industry standards, implementation of strict security protocols, and collaboration between industry, government, and standardization bodies.A conceptual framework is developed to provide practical guidance for the marine logistics industry to effectively implement new technologies and optimize their benefits. By adopting new technologies appropriately, the marine logistics industry can improve its competitiveness and global supply chain performance, and support environmental sustainability.

Analysis Of Current Development Status of Global Path Planning for Intelligent Carrier Robots in Warehouse Logistics Scenarios

Analysis Of Current Development Status of Global Path Planning for Intelligent Carrier Robots in Warehouse Logistics Scenarios

Disaster Logistics Warehouse Location Selection: An Application for Ankara Province

Afetler, insan, malzeme veya çevresel kayıplara sebebiyet veren, günlük hayatın işleyişinde büyük ölçüde olumsuz etkileri olan, yaşanılan bölgenin kendi imkanları ile üstesinden gelemeyeceği olaylardır. Türkiye’de en çok yaşanılan doğal afetler ise depremlerdir. Depremlerin olumsuz etkilerinin azaltılması ise afet öncesinde gerçekleştirilen etkin afet lojistiğinin sağlanması ile mümkün olmaktadır. Afet sonrasında afetzedelere hizmet veren afet lojistik depolarının uygun yerlere kurulması, afet lojistiğinin başarılı bir şekilde uygulanmasında etkili olmaktadır. Bu yüzden bu çalışmada afet lojistik depoları için yer seçimi problemi incelenmiştir. Literatürdeki çalışmalarda afet lojistik depolarının genelde şehir merkezlerinde kurulmasına yönelik çalışmalar mevcuttur. 6 Şubat 2023 tarihinde meydana gelen depremler çevre illerden gelecek yardımların ne kadar önemli olduğunu gözler önüne sermiştir. Bu yüzden bu çalışmada Ankara’nın merkez dışı ilçelerinde kurulacak afet lojistik depoları ile hem komşu ilçelere hem de çevre illere hizmet verilmesi amaçlanmaktadır. Çalışmada afet lojistik depolarının yerlerinin belirlenmesi ile bu depolara atanacak il ve ilçelerin belirlenmesinde küme kapsama ile p-medyan ve kapasitelendilmiş p-medyan modelleri kullanılmıştır. Çalışmanın son bölümünde ise yapılan duyarlılık analizi ile modellerin etkinliği analiz edilmiştir.

Adaptive dynamic fire danger evaluation of logistics warehouses with fusion of evidential reasoning and smart optimization

Considering the background that the current fire risk assessment methods often rely on subjective assessments based on professional knowledge and experience, they are usually limited to the special fire scene. Given the lack of effective tools for assessing fire risks in logistics warehouses across varying scenes, this paper aims to propose a multi-source data-driven, adaptive dynamic fire danger evaluation method. The method is established based on the fusion strategy of evidential reasoning and artificial fish swarm algorithm. According to the method, rules of danger evaluation levels related to each attribute of fire factors can be generated automatically without necessary of professional knowledges and experiences. The lightweight multi-source data at this moment can be fused to evaluate current fire danger level directly, which does not require training on extensive datasets in advance. Finally, multi-source data including temperature and CO concentration in two fire scenes of one logistics warehouse at different times are utilized to support the ability of the method. The results show that the estimated fire source location according to the center of domain with the evaluated highest danger level matches well with the real fire source location. In conclusion, the main contribution of the research is that the reasonable dynamic danger level of the logistics warehouse fire with time variation can be evaluated automatically. The method has an excellent generality, which is not limited to the specific fire scene. The danger zone of the logistics warehouse under sudden fires can be determined adaptively, which can support the fire prevention and fire safety countermeasures of logistics warehouses.

Роль індустріальних парків та складської логістики у контексті відновлення економіки України

This research delves into the intricacies of industrial park development and warehouse logistics in Ukraine, focusing on the challenges and opportunities arising in the aftermath of a full-scale invasion. It provides a comprehensive analysis of the existing landscape of industrial parks within Ukraine in 2023, encompassing their geographical distribution, developmental status, and financial capabilities. Furthermore, the study investigates the profound impact of the conflict on the progress of industrial parks and warehouse logistics in Ukraine, bringing forth critical issues that have emerged as a consequence. Within this context, the research underscores the dependency on private investments, owing to the limited state support resulting from the reallocation of financial resources towards defense. It argues for the need to foster an environment conducive to attracting foreign investors and to streamline investment procedures, thereby enhancing Ukraine’s appeal for international businesses. Moreover, it anticipates a resurgence in the warehouse logistics market once stability is achieved, with multiple suspended investment projects expected to come to fruition in the short term. This study highlights the significance of creating a favorable regulatory framework and legislative environment for investors, contributing to the growth of industrial parks and warehouse logistics. Additionally, it underscores the need for embracing cutting-edge technologies, including digitalization and eco-friendly practices, to bolster the competitiveness of these industrial parks on both the national and global stages. In conclusion, Ukraine holds substantial potential in the advancement of industrial parks and warehouse logistics. To capitalize on this potential, it is imperative for the government to foster a stable investment climate, attract international partners for collaboration in this sector, and facilitate technological development, ultimately generating new employment opportunities and supporting sustainable economic growth. This research sets the stage for a comprehensive strategy to guide the revitalization of these industries in Ukraine, facilitating their resurgence as integral components of the nation’s post-war economic recovery.

ADVANCING INDUSTRY 4.0 WITH ROS: A CASE STUDY ON AUTONOMOUS MOBILE ROBOT TECHNOLOGICAL ADVANCEMENTS

In the world of Industry 4.0, Autonomous Mobile Robots (AMRs) are now vital parts of modern industrial automation. This study examines how the Robot Operating System (ROS) plays a crucial role in advancing technology for AMRs. By looking at real-life examples, it shows how ROS helps in creating and using AMRs, changing how industrial processes work. The study demonstrates how ROS is being integrated into AMR design and operation, leading to improved autonomy, flexibility, and productivity in industrial settings. This study discusses how ROS-powered AMRs have transformed various tasks like material handling, warehouse logistics, and autonomous navigation, leading to increased productivity and cost-efficiency. It also explores the challenges and opportunities brought about by ROS in the Industry 4.0 era, including sensor fusion, machine learning, and human-robot teamwork. Furthermore, ROS not only influences the design and operation of AMR, but also enables smooth integration with advanced technologies such as sensor fusion and machine learning. This opens up opportunities for improved flexibility and teamwork between humans and robots in the ever-evolving environment of Industry 4.0. The importance of ROS in connecting traditional manufacturing practices with the changing demands of the fourth industrial revolution is emphasized.