Smart Warehouse Research Articles

A smart warehouse framework, architecture and system aspects under industry 4.0: a bibliometric networks visualisation and analysis

ABSTRACT This study contributes to a better understanding of smart warehouse systems and design in a few ways. (1) warehouse underperformance reasons; (2) smart warehouse enablers in Industry 4.0; and (3) the construction of a smart warehouse design from the viewpoint of recommended framework, architecture and system aspects. This study presents a smart warehouse framework, architecture, and system aspects in Industry 4.0. Manufacturing firms require well-designed smart warehouse technology and ecosystem to monitor and control inventory capacity for cyber-physical production environments. However, only a few studies exist to guide the industry on smart warehouse design. The industry remains skeptical of leveraging technology to compete in this digitalisation era. Symptom versus problem and bibliometric networks visualisation and analysis is deployed to observe the thematic patterns. The findings show that inventory mismanagement and communication hurdles between firms and suppliers often cause warehouse underperformance. The insights are useful in extending the literature and designing smart warehouses for creating business competitiveness.

Smart Warehousing as a Wave of the Future

Background: The unprecedented supply chain disruptions caused by the prolonged COVID-19 pandemic forced many firms to change their way of doing business dramatically. These changes include quickly responding to the growing demand for online orders and the corresponding direct shipments to customer locations. These changes have been further accelerated by rapid technological innovations resulting from the fourth industrial revolution (Industry 4.0). One of the most notable technological transformations that we have witnessed is the growing popularity of smart warehousing concepts. Although smart warehousing may represent a wave of the warehousing future, the published literature rarely documents its underlying principles, specific application targets, and potential impacts on supply chain performance. This research aims to identify key drivers of the digital warehousing revolution and describe important value propositions for warehousing automation. Methods: To help companies develop smart warehouses successfully as an integral part of a supply chain link, I conceptualize an ideal smart warehousing system, design its basic architecture, propose specific milestones for monitoring the progress of smart warehouse development, and then, identify critical success factors for its full utilization in today’s volatile warehousing environment. This paper employed qualitative content analysis to conceptualize smart warehousing development and establish a smart warehousing framework. Results: A smart warehouse will bring many managerial benefits, including warehousing cost efficiency, labor productivity, and agility in the era of the knowledge economy. Conclusions: This paper will enable companies to accelerate digital transformation and improve their competitiveness amid the post-pandemic industrial revolution.

Utilizing Fuzzy AHP in the Evaluation of Barriers to Blockchain Implementation in Reverse Logistics

Digital technologies like blockchain, the Internet of Things, and smart warehouses have been developed due to the fourth industrial revolution, or “Industry 4.0.” Any business’ supply chain includes several stakeholders, including manufacturers, distributors, suppliers, and final consumers. The demand for firms to utilize these technologies to gain competitive advantages has intensified in the modern world due to rising worldwide rivalry. Additionally, the adoption of blockchain technology, in particular, can have a huge impact on a company’s reverse logistics, accelerating processes by decentralizing, tracking, and overseeing the delivery of items to final consumers. The goal of this study is to pinpoint those significant obstacles because several must be overcome for blockchain technology to be successfully implemented in reverse logistics. This study identified 16 impediments to the adoption of blockchain technology after a thorough analysis of the literature and expert opinion. The fuzzy AHP approach was used in this study to rank those barriers as this approach helps to address the complexity and uncertainty associated with decision-making in supply chain management and provides a more robust and reliable ranking of the barriers to blockchain adoption. A case study of Pakistan’s e-commerce industry was carried out. The results show that the high installation cost, stakeholders’ resistance to the blockchain, and the lack of top-management support are the critical success factors in blockchain adoption. From an industrial perspective, the study highlights the need for businesses to carefully evaluate the potential benefits and costs of adopting blockchain technology. It also underscores the importance of addressing the barriers to adoption to ensure successful implementation. By doing so, businesses can enhance their supply chain management and improve their overall competitiveness.

Intelligent Warehouse in Industry 4.0-Systematic Literature Review.

The development of Industry 4.0 (I4.0) and the digitization and automation of manufacturing processes have created a demand for designing smart warehouses to support manufacturing processes. Warehousing is one of the fundamental processes in the supply chain, and is responsible for handling inventory. Efficient execution of warehouse operations often determines the effectiveness of realized goods flows. Therefore, digitization and its use in exchanging information between partners, especially real-time inventory levels, is critical. For this reason, the digital solutions of Industry 4.0 have quickly found application in internal logistics processes and enabled the design of smart warehouses, also known as Warehouse 4.0. The purpose of this article is to present the results of the conducted review of publications on the design and operation of warehouses using the concepts of Industry 4.0. A total of 249 documents from the last 5 years were accepted for analysis. Publications were searched for in the Web of Science database using the PRISMA method. The article presents in detail the research methodology and the results of the biometric analysis. Based on the results, a two-level classification framework was proposed, which includes 10 primary categories and 24 subcategories. Each of the distinguished categories was characterized based on the analyzed publications. It should be noted that in most of these studies, the authors' attention primarily focused on the implementation of (1) Industry 4.0 technological solutions, such as IoT, augmented reality, RFID, visual technology, and other emerging technologies; and (2) autonomous and automated vehicles in warehouse operations processes. Critical analysis of the literature also allowed us to identify the current research gaps, which will be the subject of further research by the authors.

Utilisation of drones in achieving various applications in smart warehouse management

PurposeOrganisations and industries are often looking for technologies that can accomplish multiple tasks, providing economic benefits and an edge over their competitors. In this context, drones have the potential to change many industries by making operations more efficient, safer and more economic. Therefore, this study investigates the use of drones as the next step in smart/digital warehouse management to determine their socio-economic benefits.Design/methodology/approachThe study identifies various enablers impacting drone applications to improve inventory management, intra-logistics, inspections and surveillance in smart warehouses through a literature review, a test of concordance and the fuzzy Delphi method. Further, the graph theory matrix approach (GTMA) method was applied to ranking the enablers of drone application in smart/digital warehouses. In the subsequent phase, researchers investigated the relation between the drone application's performance and the enablers of drone adoption using logistic regression analysis under the TOE framework.FindingsThis study identifies inventory man agement, intra-logistics, inspections and surveillance are three major applications of drones in the smart warehousing. Further, nine enablers are identified for the adoption of drone in warehouse management. The findings suggest that operational effectiveness, compatibility of drone integration and quality/value offered are the most impactful enablers of drone adoption in warehouses. The logistic regression findings are useful for warehouse managers who are planning to adopt drones in a warehouse for efficient operations.Research limitations/implicationsThis study identifies the enablers of drone adoption in the smart and digital warehouse through the literature review and fuzzy Delphi. Therefore, some enablers may be overlooked during the identification process. In addition to this, the analysis is based on the opinion of the expert which might be influenced by their field of expertise.Practical implicationsBy considering technology-organisation-environment (TOE) framework warehousing companies identify the opportunities and challenges associated with using drones in a smart warehouse and develop strategies to integrate drones into their operations effectively.Originality/valueThis study proposes a TOE-based framework for the adoption of drones in warehouse management to improve the three prominent warehouse functions inventory management, intra-logistics, inspections and surveillance using the mixed-method.

Smart warehouse: A bibliometric analysis and future research direction

Warehouses are crucial components of the logistics industry because their operational efficiency determines the operational efficiency of the logistics. A warehouse was traditionally thought to be a location where inventory was stored and held before being shipped to customers or distributed to retailers for sale. With the introduction of Industry 4.0 technologies, the warehouse's role has changed dramatically, the scope of warehouse operation has expanded, and the concept of smart warehouse, which denotes increased automation of traditional warehouse functions, has been introduced. Smart warehouses aim to improve overall service quality, productivity, and efficiency while lowering costs and failures. It has emerged as a result of smart technologies, igniting a wave of industry transformation with the potential to bring about dramatic changes. We used the bibliometric analysis method in this paper to analyse and draw conclusions from 295 articles retrieved from the Scopus database between 2017 and June 2022. The methodology used in this paper is divided into four steps: data collection, data analysis, data visualisation, and interpretation. The current study aims to provide a comprehensive understanding of smart warehouses using the Bibliometric R-package and VOS viewer software.

IoT Based Humidity and Temperature Control System for Smart Warehouse

In daily life, vitamins fruits and vegetables play a very important role in human life. The farmer produces grains or vegetables in huge quantities. Due to lack of storage problem farmer suffers. It is necessary to store all these things in a special warehouse. Thus, improper storage of grains or fruits is essential to meet the increasing demands. Storage time and fruit quality are greatly influenced by storage environmental factors. Therefore, it is important for the fruit storage environment to perform multi-parameter monitoring and analyze the collected parameters and then regulate and control. The quality of grains and other perishable goods kept in a warehouse usually deteriorates due to lack of monitoring and proper maintenance of their required suitable conditions in such a large area. The proposed methodology deals with the storage of grains, fruits, etc., and to identify their temperature and humidity patterns so that they will not be degraded. Establishing the environment in relation to the nature of the crop so as to determine an optimum threshold with an appropriate pattern of monitoring and change. The main goal of this research article is to provide solutions to prevent rotting of grains and fruits in the storage area.

An Admission-Control-Based Dynamic Query Tree Protocol for Fast Moving RFID Tag Identification

As one of the key techniques used in the perception layer of the Industrial Internet of Things (IIoT), radio frequency identification (RFID) has been widely applied for object tracing, smart warehouse management, product line monitoring, etc. In most applications, conveyor belts are prevalently implemented to accelerate the sorting efficiency for goods management. However, in such a system, tags quickly go through the reader’s reading range resulting in constant changing of the tag set and limited participating time of moving tags. As a result, it poses more challenges to the tag identification problem in mobile systems than in traditional static applications. In this work, a novel admission-control-based dynamic query tree (ACDQT) protocol is proposed for fast-moving tag identification. In ACDQT, two main strategies are developed, i.e., multi-round admission control (MRAC) and dynamic query tree recognition (DQTR). In MRAC, the reading process of multiple rounds is analyzed, and the number of admitted tags in each round is optimized. Thus, the tag lost ratio is guaranteed, and the identification process can be effectively accelerated. In DQTR, colliding tags are grouped into multiple subsets with the help of consecutive colliding bits in tag responses. By constructing a dynamic query tree, the number of collision slots is greatly reduced, and the identification efficiency in a single round is improved significantly. With MRAC and DQTR, ACDQT can support higher tag flow rate in mobile systems than existing works. Both theoretical analyses and simulation results are presented to demonstrate the effectiveness of ACDQT.

A Dual Forward–Backward Algorithm to Solve Convex Model Predictive Control for Obstacle Avoidance in a Logistics Scenario

In recent years, the logistics sector expanded significantly, leading to the birth of smart warehouses. In this context, a key role is represented by autonomous mobile robots, whose main challenge is to find collision-free paths in their working environment in real-time. Model Predictive Control Algorithms combined with global path planners, such as the A* algorithm, show great potential in providing efficient navigation for collision avoidance problems. This paper proposes a Dual Forward–Backward Algorithm to find the solution to a Model Predictive Control problem in which the task of driving a mobile robotic platform into a bi-dimensional semi-structured environment is formulated in a convex optimisation framework.

Implementation and Critical Factors of Unmanned Aerial Vehicle (UAV) in Warehouse Management: A Systematic Literature Review

Unmanned aerial vehicles (UAVs) have proven to be a key solution for nearly automated or smart warehouse operations, enabling receiving, picking, storage, and shipping processes to be timely and more efficient. However, there is a relative scarcity of review studies specifically on UAV-based warehouse management. Research knowledge and insights on UAV applications in this field are also limited and could not sufficiently or practically support decision-making on commercial utilization. To leverage the potential applications and current situation of UAVs, this study provides a systematic literature review (SLR) on UAV adoption in warehouse management. SLR approach was critically conducted to identify, select, assess, and summarize findings, mainly on the two descriptive research questions; what are the past applications of UAV, and what are critical factors affecting UAV adoption in warehouse management? Five key critical factors and 13 sub-factors could be observed. The results revealed that hardware (e.g., payloads, battery power, and sensors) and software factors (e.g., scheduling, path planning, localization, and navigation algorithms) are the most influential factors impacting drone adoption in warehouse management. The managerial implications of our research findings that guide decision-makers or practitioners to effectively employ UAV-based warehouse management in good practice are also discussed.

Securing Age-of-Information (AoI)-Enabled 5G Smart Warehouse Using Access Control Scheme

Low-power wireless sensor networks (WSNs) and Internet of Things (IoT) have great impact for the real-time applications in future 5th generation (5G) mobile networks due to the wireless-powered communication technologies. The Age of Information (AoI) plays a crucial performance metric in an IoT-enabled real-time smart warehouse application, where the freshness of the aggregated data is very important. However, wireless medium communication among the beacon nodes and the user equipments (tracking nodes) gives an opportunity to an adversary not only to eavesdrop the data but also to corrupt the data by means of deleting, modifying or inserting malicious information during communication among the entities involved in the smart warehouse environment. To mitigate these issues, we design a security scheme for AoI-enabled 5G smart warehouse through an access control mechanism, where the secure communication among the beacon nodes and the tracking nodes will take place by mutual device authentication and key agreement process. The fresh data collected at the enterprise cloud is then used for big data analytics for better predictions and analysis, such as optimal device scheduling so that the data becomes very fresh. The rigorous security analysis and comparative study show that the proposed mechanism has significantly better security and comparable communication and computational costs as compared to the relevant schemes. In addition, through the real-time testbed experiments, we show that the proposed scheme is practical in 5G smart warehouse context.

MAS-based Distributed Cyber-physical System in Smart Warehouse

This paper presents an approach for a multi-agent-based cyber-physical system dedicated to operating the warehouse plant with a distributed approach. The recent technological evolution has improved the quality and robustness of the services for current warehouses. However, systems that operate warehouses do not follow this evolution, presenting predominantly central monolithic or hierarchical approaches, resulting in fragility related to flexibility, scalability, and robustness in the face of disturbances. In the proposed approach, each warehouse physical component has a computational unit associated, i.e. a cyber agent, with communication, negotiation, and data analysis capabilities. Agents contain all the information, algorithms, and functions necessary to operate the physical component, and instead of receiving orders from higher-layer agents, they negotiate and collaborate to perform the tasks. The proposed system was tested in a laboratory testbed, composed of six racks and up to eight robots for transporting products. Extensive experiments show the feasibility of the approach.

Switched Beam Array Antenna Optimized for Microwave Powering of 3-D Distributed Nodes in Clustered Wireless Sensor Network

The IoT applications like smart warehouse, smart industry, and smart vertical farming will require microwave power transmission (MPT) to the densely deployed low-power sensor nodes in 3-D space. To address this issue, a switched beam array antenna (SBA) is synthesized as an MPT system to power the proposed 3-D clustered wireless sensor network (WSN). The 3-D space is divided into nine clusters; each illuminated individually with RF power using a dedicated patch antenna subarray. The subarrays can be switched ON or OFF based on the energy requirement of the WSN cluster nodes resulting in efficient power transmission. Moreover, the low complexity of switched beam design reduces the cost and processing overhead as compared to the exiting adaptive beamforming systems in a dense WSN scenario. The subarray size and excitation coefficients are optimized with a constraint over the harvested dc power ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{dc}$ </tex-math></inline-formula> ) to enhance the 3-D coverage. In addition, the optimal location of the cluster head (CH) is evaluated to minimize the average power consumption of WSN cluster. The subarrays are designed and fabricated with the evaluated excitation coefficients, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{dc}$ </tex-math></inline-formula> measurements are carried out validating analytical results.

“Smart Warehouse”: new technologies for warehouse logistics automation

The article discusses the main trends and examples of the implementation of warehouse logistics automation technologies. The authors note that the introduction of the “smart warehouse” system, warehouse robotization multiply increase the speed of order processing and inventory turnover.

Augmented Random Search with Artificial Neural Networks for energy cost optimization with battery control

Intermittent renewable energy production and dynamic load must be balanced through appropriate control of integrated energy storage to account for the temporal discrepancy among power supply and demand. Intelligent control systems are required to anticipate and optimize the charging and discharging of energy storage. In recent years, reinforcement learning based techniques have been applied to a multitude of problems, including building integrated energy storage solutions. In this work, the focus is on the application of reinforcement learning based techniques to the specific energy optimization problem of controlling a battery energy storage system in a smart warehouse. This paper adopts data from a real operational battery energy storage system installed in a smart warehouse, integrated with photovoltaic, for food distribution on the west coast of Norway. In the smart warehouse, an intelligent energy management system controls the on-site battery energy storage using machine learning predictions of load and photovoltaic production, and an optimization algorithm is presented to generate a schedule for effective utilization of battery energy storage in coordination with a thermal storage system. This paper presents the combination of the augmented random search reinforcement algorithm with artificial neural networks as a basis to design an intelligent energy management system for controlling energy flows of battery energy storage systems to minimize the energy cost. The developed algorithm finds very promising solutions in the considered case-study of a smart house for energy cost minimization through a battery energy storage system, achieving an average of 99.2% accuracy across 10 seeded trials.

Supervised Reinforcement Learning for ULV Path Planning in Complex Warehouse Environment

The rapid development of the logistics industry leads to an urgent need for intelligent equipment to improve warehouse transportation efficiency. Recent advances in unmanned logistics vehicles (ULVs) make them particularly important in smart warehouses. However, the complex warehouse environment poses a significant challenge to ULV transportation path planning. Multiple ULVs need to transport cargoes with good coordination ability to overcome the low efficiency of a single ULV. The ULVs also need to interact with the environment to achieve optimal path planning with obstacle avoidance. In this paper, we propose a supervised deep reinforcement learning (SDRL) approach for logistics warehouses that enables autonomous ULVs path planning for cargo transportation in a complex environment. The proposed SDRL approach is featured by (1) designing the supervision module to imitate the behaviors of experts and thus improve the coordination ability of multiple ULVs, (2) optimizing the generator of the imitation learning based on the proximal policy optimization to boost the learning performance, and (3) developing the policy module via deep reinforcement learning to avoid obstacles when navigating the ULVs in warehouse environments. The experiments over dynamic and fixed-point warehouse environments show that the proposed SDRL approach outperforms its rivals regarding average reward, training speed, task completion rate, and collision times.

Valid Inequality and Variable Fixation for Unrestricted Block Relocation Problems

In modern logistics and smart warehouse, a terminal exists as a hub to connect multiple transportation modes and exchange goods. Solving a block relocation problem (BRP) arising from block retrieval processes in a terminal is fundamentally important to enhance the terminal’s overall efficiency and save its energy. In this paper, we improve the state-of-the-art mixed integer programming (MIP) formulation of an unrestricted BRP by extracting valid inequalities from structural properties and proposing a new variable fixation method to solve it. Computational results show that the improved model can be optimally solved much more easily than the original model. Among over five-hundred benchmark instances with height limits, the improved model can solve 12.3% more than the original one. For the instances that can be optimally solved by the latter, the improved model shows six times faster speed than the latter. This work represents a significant advance in this important area.

Image Information Recognition and Classification of Warehoused Goods in Intelligent Logistics Based on Machine Vision Technology

To sort out warehouse management problems in smart factories, smart warehousing and in-plant smart distribution systems are needed to achieve the goal of lean logistics and distribution in smart factories. There are still some pressing problems in the research on images of warehoused goods in intelligent logistics. For example, a solution hasn’t been found yet to recognise multiple types of warehoused goods in different shapes and colours; static vision image processing solutions have a poor performance in optimising recognition speed and classification accuracy. In response, this paper unveils a study on the image information recognition and classification of warehoused goods in intelligent logistics based on machine vision technology. It presents a process related to warehouse management in intelligent logistics and a corresponding system architecture. It also constructs a YOLOv3 model for the image information recognition and classification of warehoused goods in intelligent logistics. The paper elaborates on the prior box settings and loss function correction methods, and finishes optimising the YOLOv3 model. Experimental results verified the effectiveness of the constructed model.

The transformation from manual to smart warehousing: an exploratory study with Swedish retailers

PurposeTo meet customers' expectations on shorter lead times, high product availability, flexibility, and variation in delivery and return options, retailers have turned their attention to warehousing and are making big investments in technology. Currently, technology providers are pushing for smart warehousing, a new and under-researched phenomenon. This study aims to conceptualize the term and examine pathways toward implementing smart warehousing.Design/methodology/approachAn exploratory survey was administered to 50 leading Swedish retailers in varying segments. A two-tailed t-test for equality of means was used to detect significant differences between current and future states.FindingsThe study found that future smart warehouses will be automated, autonomous, digital, and connected, but that retailers will follow different paths along this journey, driven by contextual trends, e.g. sales growth, wider product assortment, shorter lead-time offerings, and integration of brick-and-mortar and online stores. Interestingly, the study revealed that many of the retailers that aim to create smart warehouses in five years are not the retailers with the most developed technology today.Research limitations/implicationsThe paper operationalizes smart warehousing in two dimensions: degree of automation and degree of digitalization and connectivity of information platforms. Based on the findings, 16 theoretical propositions are put forth that, based on contextual factors, explain different pathways for retailers to implement smart warehousing.Practical implicationsThe empirical insights and theoretical discussions provide practically useful guidance, including outlined trends, for selecting and benchmarking automation and complementary technologies in warehouse operations.Originality/valueThis paper conceptualizes and operationalizes smart warehousing – an original approach. It is also one of the first to investigate the technological transformation in retail warehousing empirically, explaining how and why retailers choose different pathways toward smart warehousing.

Towards Deep Q-Network Based Resource Allocation in Industrial Internet of Things.

With the increasing adoption of Industrial Internet of Things (IIoT) devices, infrastructures, and supporting applications, it is critical to design schemes to effectively allocate resources (e.g., networking, computing, and energy) in IIoT systems, generally formalized as optimization problems. Nonetheless, because the system is highly complex, operation environments are time-varying, and required information may not be available, it is difficult to leverage traditional optimization techniques to solve the optimal resource allocation problem. To this end, in this paper we propose a Deep -Network (DQN) based scheme to address both bandwidth utilization and energy efficiency in an IIoT system. In detail, we design a DQN model that consists of two deep neural networks (DNN) and a -learning model. The DNN network abstracts the features from the highly dimensional inputs and obtains the approximate -function for the -learning model. Based on the -function, the -learning model can generate the -table and reward function. After the training process, the DQN model can select appropriate actions for the agents (i.e., robots in a smart warehouse in this study) to improve bandwidth utilization and energy efficiency. To evaluate our proposed scheme, we design a simulation environment to investigate a typical IIoT scenario: the actuation of robotics in a smart warehouse. We then implement the DQN model and conduct extensive experiments to validate the efficacy of our scheme. Our experimental results confirm that our scheme can improve both bandwidth utilization and energy efficiency, as compared to other representative schemes.