Warehouse Environment Research Articles

Review on Automated Storage and Retrieval System for Warehouse

The swift expansion of e-commerce and supply chain operations has significantly enhanced the efficiency of warehouse management systems, establishing them as vital components in augmenting organizations' competitiveness. This paper delves into warehouse sorting systems to enhance the sorting process, reduce error rates, and simplify internal warehouse procedures. It aims to develop a scalable, adaptable, and efficient warehouse sorting system that can maximize sorting efficiency while effectively responding to changing market demands through the use of advanced automation technologies. The study provides an in-depth review of the literature that has explored the Automated Storage and Retrieval System (ASRS) within the context of warehouse operations. It offers a comprehensive introduction to the operational systems of warehouses, detailing each type of ASRS along with the technologies that can improve the efficiency and accuracy of these systems. Moreover, the paper thoroughly investigates and classifies the ASRS design decision problem and compares multiple types of ASRS. The analysis aims to delineate the distinctions among various ASRS configurations, assessing their scalability, adaptability, and their impact on operational efficacy in warehouse environments Through this comparative review, the paper emphasizes the potential enhancements in sorting processes that modern ASRS can provide, ensuring that warehouse operations can rapidly adapt to market changes and demands. The goal is to highlight best practices and technological innovations that can lead to more responsive and efficient warehouse management systems. This exploration contributes to a better understanding of how cutting-edge automation and adaptable system designs can significantly influence the efficiency of warehouse sorting processes.

Efficacy of selected insecticide sprays against Indian meal moth (Lepidoptera: Pyralidae) wandering larvae on various surfaces

The Indian meal moth, Plodia interpunctella (Hübner) (Lepidoptera: Pyralidae), is a worldwide pest found in food product warehouses. Presence of P. interpunctella wandering larvae causes customer complaints and product rejection from customers. Its prepupal stage is susceptible to insecticide treatment due to their wandering behavior. We evaluated the efficacy of three insecticide sprays, ExciteR (0.017% pyrethrins, 0.174% piperonyl butoxide), Suspend SC (0.053% deltamethrin), and Transport Mikron (0.047% acetamiprid, 0.056% bifenthrin) against P. interpunctella wandering larvae by forced exposure to porous (vinyl tile, unpainted pinewood, concrete, and paper) and non-porous (steel, glazed ceramic) surfaces, and exposure to treated harborages made of two pieces of vinyl tiles, unpainted pinewood, and concrete. Under continuous exposure conditions, both ExciteR and Suspend SC caused significantly higher mortality (90–96%) than Transport Mikron (73–75%) on non-porous surfaces after 4 days (P < 0.05). But on porous surfaces, the three insecticides caused ≤63% mortality with Suspend SC being the most effective. When using 2× amount of insecticides as that in the previous test (2.1 gallon/1000 ft2, which is 4.33, 1.20, and 2.33 mg a.i./m2 of ExciteR, Suspend SC, and Transport Mikron, respectively), Suspend SC was the only insecticide causing high mortality (≥99%) through both forced exposure to concrete surface and exposure to treated concrete harborage. Whereas other treatments caused <12% mortality. Among the porous surfaces, residual efficacy of Suspend SC on concrete was significantly higher than that on vinyl tiles. Application of an effective liquid insecticide (such as 0.053% deltamethrin) into harborages or directly to the possible pupation sites, such as corners, cracks, and crevices could be an effective strategy for managing P. interpunctella wandering larvae in warehouse environment.

YOLOv8 Powered Solutions for Box Identification in Warehouses

In modern warehouse management, the ability to effectively identify and track boxes is critical for optimizing operations and reducing costs. This research investigates the application of YOLOv8 deep learning model for real-time box identification in warehouse environments. Three different approaches were evaluated: using a pre-trained YOLOv8 model, training the model with a dataset obtained from the Internet, and training the model with a custom dataset designed for this application. For the second and third approaches, the model was trained using Google Colab, and image annotation was performed using Roboflow. Each approach is thoroughly tested to assess the accuracy and robustness of the model under various conditions. The results demonstrate the strengths and limitations of YOLOv8 in different scenarios, providing valuable insights into its practical implementation for warehouse automation. This study highlights the potential of YOLOv8 as a useful tool for improving warehouse efficiency.

Development and research of an autonomous mobile platform with reduced computing power requirements

Today, the market is saturated with a variety of autonomous robotic devices that offer a variety of solutions to opti-mize workflows in various industries. One of the leading areas of their application is the automation of warehouse operations, where store robots move and optimize warehouse processes, reducing labor costs and speeding up task completion. These technologies reveal some limitations and shortcomings. For example, categories of robots such as transport carts, forklifts, and automated towing vehicles are limited in their functionality, often resulting in the need to use multiple types of devices for different tasks. This leads to equipment redundancy and complicates integration into existing warehouse systems. Another significant aspect is the need to improve the routing and nav-igation algorithms for these devices. Modern autonomous robot systems often face route optimization challenges, especially in dynamic and complex warehouse environments. This can lead to inefficiencies and increased time delays [1]. The goal of this work is to develop an autonomous mobile platform that would solve possible problems with navigation, have well-developed routing algorithms combined with low cost for affordable use in small busi-nesses. The research methods involve conducting various experiments on a real mobile platform to collect data and evaluate performance, including measuring the speed, accuracy, and power consumption of transportation tasks. Development and optimization of new algorithms for managing a mobile platform, planning routes, analyz-ing signals from sensors, processing images and video from a camera. The result of the work is that the possibility of compatibility of the on-board computer (single board computer) with the flight controller for movement on the ground surface has been investigated. On this basis, a mobile platform was created, which is presented in this arti-cle. This configuration is suitable for moving the platform either autonomously or using a control panel. The role of the technical vision system in providing navigation has been studied. The results of the work highlight innovation and the use of model functionality to create effective autonomous land navigation systems, including coordinates. The mobile platform can be used for transporting goods in warehouses, for working in extreme situations, for environmental monitoring or for educational purposes.

Energy harvesting for automated storage and retrieval system with sustainable configuration of storage assignment and input/output point

The warehouse automation market has experienced significant growth due to the necessity for quick responses to customer needs. The adoption of Automated Storage and Retrieval System (AS/RS) aims to enhance operational efficiency and expedite order fulfillment, although environmental considerations are frequently overlooked. This study introduces the implementation of energy harvesting using Regenerative Braking System (RBS) on AS/RS to minimize the carbon emission impact. The best configuration of storage assignments and Input/Output (I/O) points is examined to improve travel time, response time, and carbon emission as sustainability indicators. This study employs a discrete-event simulation mimicking the AS/RS and warehouse environment under uncertainty. Simulation-based experiment was performed under 96 different scenarios and the result was assessed through statistical tests revealing the main and interaction effects between factors to performance indicators, including the trade-off between them. The result reveals that the implementation of RBS in AS/RS can result in 13% energy saving on average or equal to additional travel range of 28,800 m indicating the suitability adoption towards green operation. However, the lowest carbon emission is followed by higher travel time and response time. Thus, metamodel-based optimization was also performed via desirability function analysis. The optimization result reveals that the sustainable AS/RS configuration is obtained with a single-side for I/O point, non-class for storage classification, closest open location with column-order for slot selection, and closest open location with row-order for retrieval selection.

Optimization of inventory management through computer vision and machine learning technologies

This study presents implementing and evaluating a computer vision platform to optimize warehouse inventory management. Integrating machine learning and computer vision technologies, this solution addresses critical challenges in inventory accuracy and operational efficiency, overcoming the limitations of traditional methods and pre-existing automated systems. The platform uses convolutional neural networks and open-source libraries such as TensorFlow and PyTorch to recognize and accurately classify products from images captured in real time. Practical implementation in a natural warehouse environment allowed the proposed platform to be compared with traditional systems, highlighting significant improvements, such as a 45% reduction in the time required for inventory counting and a 9% increase in inventory accuracy. Despite facing challenges such as staff resistance to change and technical limitations on image quality, these difficulties were overcome through effective change management strategies and algorithm improvements. The findings of this study identify the potential for computer vision technology to transform warehouse operations, offering a practical and adaptable solution for inventory management.

Labor-Saving in Order Picking- A Real Time Study Advancing Lean Operations

Purpose: Efficient warehouse operations rely heavily on optimizing order picking processes, which are known to constitute a significant portion of overall operational costs. The aim is to enhance operational efficiency and cost-effectiveness by minimizing non-value-added tasks in warehouse operations. Materials and Methods: This study investigates the transition from an Assisted-Picker model to a Solo-Picker model at a prominent 3PL service provider in India. The analysis compares two approaches for picking orders—pairing laborers versus employing a Solo-Picker—using metrics such as travel time and picking time. Findings: Results reveal that while the Assisted-Picker model may initially show faster completion times for individual orders, the Solo-Picker model proves more efficient overall. This is attributed to the elimination of idle time associated with helpers and enhanced labor utilization efficiency. Real-time productivity data collected during the study period demonstrate a remarkable 44% increase in productivity with the Solo-Picker model, resulting in substantial Labor-Savings and significant reductions in operational costs. Implications to Theory, Practice and Policy: This study highlights the critical importance of reevaluating traditional labor allocation strategies in warehouse environments. By identifying and eliminating non-value-added work, warehouses can move closer to lean and efficient supply chain management practices, ultimately enhancing overall productivity and cost-efficiency.

Hybrid Optimization Path Planning Method for AGV Based on KGWO.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method's potential in real-world applications.

Integrating deep reinforcement learning and improved artificial potential field method for safe path planning for mobile robots

PurposeThis paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.Design/methodology/approachThe IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.FindingsSimulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.Originality/valueThis method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

Differentiable programming for gradient-based control and optimization in physical systems

This paper presents an exploration of the application of control theory, particularly utilizing a gradient-based algorithm, to automate and optimize the operation of photovoltaic panels and refrigeration systems in warehouse environments. The study emphasizes achieving coordination between energy generation and consumption, specifically harnessing surplus solar energy for efficient refrigeration. The complex interplay between fluctuating solar irradiance, thermal dynamics of the warehouse, and refrigeration needs underscores the significance of control theory in designing algorithms to dynamically adjust PV panel output and refrigeration system operation. The paper discusses foundational control theory principles, proposes a tailored framework for warehouse operations, and highlights the potential for sustainable energy practices. This paper explores the use of data-driven approaches based on NeuralODEs vs classical ones using physics equations.

Designing Digital Twin with IoT and AI in Warehouse to Support Optimization and Safety in Engineer-to-Order Manufacturing Process for Prefabricated Building Products

Engineer-to-order manufacturing, characterized by highly customized products and complex workflows, presents unique challenges for warehouse management and operational efficiency. This paper explores the potential of a digital twin as a transformative solution for engineer-to-order environments in manufacturing companies realizing prefabricated building components. This paper outlines a methodology encompassing users’ requirements and the design to support the development of a digital twin that integrates Internet of Things devices, Building Information Modeling, and artificial intelligence capabilities. It delves into the specific challenges of outdoor warehouse optimization and worker safety within the context of engineer-to-order manufacturing, and how the digital twin aims to address these issues through data collection, analysis, and visualization. The research is conducted through an in-depth analysis of the warehouse of Focchi S.p.A., a leading manufacturer of high-tech prefabricated building envelopes. Focchi’s production processes and stakeholder interactions are investigated, and the paper identifies key user groups and their multiple requirements for warehouse improvement. It also examines the potential of the digital twin to streamline communication, improve decision-making, and enhance safety protocols. While preliminary testing results are not yet available, the paper concludes by underlining the significant opportunities offered by a BIM-, IoT-, and AI-powered digital twin for engineer-to-order manufacturers. This research, developed within the IRIS project, serves as a promising model for integrating digital technologies into complex warehouse environments, paving the way for increased efficiency, safety, and ultimately, a competitive edge in the market of manufacturing companies working in the construction industry.

Dynamic unbalanced task allocation of warehouse AGVs using integrated adaptive large neighborhood search and Kuhn–Munkres algorithm

Dynamic task allocation poses a complex and challenging decision problem for automated guided vehicles operating within warehouse environments. In this study, we investigate a new method for dynamically allocating unbalanced tasks to a multi-AGV system, with a focus on real-time task arrivals. The research treats this problem as a dynamic vehicle routing problem with pickups and deliveries and proposes the use of a rolling horizon strategy to periodically reallocate tasks by iteratively solving mixed integer programming. To enhance the computational efficiency, a novel metaheuristic is developed, which integrates adaptive large neighborhood search and the Kuhn–Munkres algorithm. Comprehensive numerical experiments are conducted to demonstrate the potential of the proposed approach, in comparison with state-of-the-art heuristics and metaheuristic algorithms, providing insight into the efficiency and effectiveness of the proposed dynamic unbalanced task allocation method for multi-AGV systems in warehouse environments.

Data reconciliation of indoor positioning data: Improve position data accuracy in warehouse environment

This article focuses on improving indoor positioning data through data reconciliation. Indoor positioning systems are increasingly used for resource tracking to monitor manufacturing and warehouse processes. However, measurement errors due to noise can negatively impact system performance. Redundant measurement involves the use of multiple sensor tags that provide position data on the same resource, to identify errors in the physical environment. If we have measurement data from the entire physical environment, a map-based average measurement error can be determined by specifying the points in the examined area where measurement data should be compensated and to what extent. This compensation is achieved through data reconciliation, which improves real-time position data by considering the measurement error in the actual position as an element of the variance-covariance matrix. A case study in a warehouse environment is presented to demonstrate how discrepancies in position data from two sensor tags on forklifts can be used to identify layout-based errors. The algorithm is generally capable of handling the multi-sensor problem in the case of indoor positioning systems. The key points are as follows:•The layout-based error detection is determined with the indoor positioning system measurement error.•This article shows how redundant measurements and data reconciliation can improve the accuracy of such systems.•Improving the accuracy of position data with the layout-based error map using a data reconciliation algorithm.

To ensure the safety of storage: Enhancing accuracy of fire detection in warehouses with deep learning models

Warehouses are important places with storage functions in process units, containing many flammable and explosive materials. Nevertheless, fire protection systems often fail to provide adequate information when fires break out, hindering firefighting and leading to catastrophic outcomes. This research aims to develop a deep learning (DL) model capable of precisely locating the fire source and estimating its intensity, providing graphical representations of fire information. We propose a novel Multi-head Attention-based Long Short Term Memory (MHA-LSTM) model and apply Computational Fluid Dynamics (CFD) to generate datasets for model training on warehouse environments. The results demonstrate losses on the validation set and test set are less than 0.003, and all errors are below 0.15, indicating that MHA-LSTM has good generalization ability for fires in various locations. Compared to 3 common DL models, such as LSTM, the loss of MHA-LSTM on the test set is reduced by at least 51.6 %, greatly improves the accuracy of fire detection in complex scenarios. Finally, based on firefighters' situational awareness (SA) requirements, graphical examples of estimated fire information are generated to provide accurate and visualized fire data. This study utilizes artificial intelligence (AI) to overcome the limitations of complex internal structures in warehouses, offering a feasible approach to improve process safety and the accuracy of fire protection systems.

Design of Part Request Form Application and Data Migration

Abstract: The migration of the data form legacy systems to modern enterprise solutions is critical for optimizing business processes and enhancing performance. This paper focuses on comparison of the data migration techniques LSMW and S/4 HANA migration cockpit. The main objective is to optimize the business process through the creation of Part Request Form application in fiori launchpad and performing data migration using S/4 High performance analytic appliance(HANA) migration cock- pit. Traditional methods of data migration has several problems, including performance bottlenecks. Addressing this limitation is essential to ensure seamless data transfer and accurate system integration. This report explores these issues and presents a methodology to address them by using advanced capabilities of the S/4 HANA Migration Cockpit over the conventional Legacy system migration workbench(LSMW) tool. The primary objective of this work is to optimize business process using part request form application. The data comes from the application will be saved in the database table in the user system. That data is extracted into a file and send it to the IT firm where that data will be migrated to the HANA database. The tool used for the data migration is s/4 HANA migration cockpit. The software used for this project is SAP-ERP software. The Results from the simulation shows that migration cockpit process is 48% faster than LSMW technique. The part request form application uses 12 kilo bytes of memory which is very less and the processing time is 0.1 seconds which is very less. These results highlight advantages of using an application in data warehouse and the effectiveness of the S/4 HANA Migration Cockpit in optimizing business processes within warehouse environment.

Solving an Order Batching and Sequencing Problem with Reinforcement Learning

The purpose of this research is to determine whether a DRL solution would be a suitable solution for the OBSP problem and to compare it with traditional methods. For this purpose, models trained with the PPO algorithm were tested in a complex and realistic warehouse environment, and an attempt was made to measure whether a strategy was developed to decrease the number of orders being late. A heuristic method was also applied and the results were compared on the same environment and data. The results showed that DRL approach that combines heuristics with the PPO algorithm has a better performance than the heuristics in minimizing the tardy order percentage in all tested scenarios.

Analisis Sistem Manajemen Pergudangan Pada Pt. Liquid Kencana Abadi

A warehouse is a separate area that functions as a place to store raw materials, parts and supplies. Every warehouse cannot be separated from a system. Warehouse Management System which is a warehouse management system that regulates/controls all warehouse activities, as is done by PT information. Liquid Kencana Abadi, a company engaged in the sale and distribution of goods. The objectives of this research are 1) To find out the warehouse management system at PT. Liquid Kencana Abadi, 2) To find out the problems faced by PT. Liquid Kencana Abadi in terms of warehouse management systems, and 3) To find out efforts to overcome warehouse management system problems at PT. Liquid Kencana Abadi. The type of research used is a descriptive qualitative approach method. Based on the known results of research conducted by researchers, they can find out the implementation of the warehouse management system implemented at PT. Liquid Kencana Abadi cannot yet be implemented optimally, this can be seen through the process of ordering and storing goods which is still not carried out effectively and efficiently. Furthermore, in this research it can be seen that the problems faced in implementing the warehouse management system are internal obstacles, such as human error problems and a less conducive warehouse environment. The efforts that have been made by PT. Liquid Kencana Abadi in overcoming this problem is by re-evaluating the warehouse management system process that has been carried out.

Towards Integration of IndoorGML and GDF for Robot Navigation in Warehouses

Abstract. With the development of the navigation technology, the outdoor navigation has made great progress, whereas the indoor navigation has some areas which is underdeveloped, insufficient to meet the rapidly increasing demands of people as well as the robotics. Even though, the advance in indoor navigation technology still has really brought a wide range of applications and a broad market, for instance, the flourishing intelligent warehouse system utilizes multi-robot operation which have the certain requirement for an accurate indoor navigation system. As for the indoor navigation, the OGC standard IndoorGML has been released and undergoing revision constantly. While the document really provides more advantageous support for the applications of Indoor Location-Based Services (LBS), in some aspects, especially the door-to-door navigation and the warehouse environment, it is not sufficiently adaptable, with still some room for improvement. IndoorGML is powerful for the common indoor scenarios like malls and offices, while as for carefully-arranged warehouse environment and other large-scale operation scenarios with multi-robots that is more similar to an ordered system, it is obviously insufficient. In this paper, we discuss about the potential to combination of IndoorGML and ITS standard ISO 20524 (GDF5.1), and extend the OGC standard indoorGML. We analyze the definition as well as function of related concepts, making some comparisons between these two standards. We conclude that these two standards are well-matched with vital potential to merge and unify the indoor and outdoor systems for spatial information.

An ontology of 3D environment where a simulated manipulation task takes place (ENVON)

Thanks to the advent of robotics in shopfloor and warehouse environments, control rooms need to seamlessly exchange information regarding the dynamically changing 3D environment to facilitate tasks and path planning for the robots. Adding to the complexity, this type of environment is heterogeneous as it includes both free space and various types of rigid bodies (equipment, materials, humans etc.). At the same time, 3D environment-related information is also required by the virtual applications (e.g., VR techniques) for the behavioral study of CAD-based product models or simulation of CNC operations. In past research, information models for such heterogeneous 3D environments are often built without ensuring connection among different levels of abstractions required for different applications. For addressing such multiple points of view and modelling requirements for 3D objects and environments, this paper proposes an ontology model that integrates the contextual, topologic, and geometric information of both the rigid bodies and the free space. The ontology provides an evolvable knowledge model that can support simulated task-related information in general. This ontology aims to greatly improve interoperability as a path planning system (e.g., robot) and will be able to deal with different applications by simply updating the contextual semantics related to some targeted application while keeping the geometric and topological models intact by leveraging the semantic link among the models.

Particle filtering supported probability density estimation of mobility patterns

This paper presents a methodology that aims to enhance the accuracy of probability density estimation in mobility pattern analysis by integrating prior knowledge of system dynamics and contextual information into the particle filter algorithm. The quality of the data used for density estimation is often inadequate due to measurement noise, which significantly influences the distribution of the measurement data. Thus, it is crucial to augment the information content of the input data by incorporating additional sources of information beyond the measured position data. These other sources can include the dynamic model of movement and the spatial model of the environment, which influences motion patterns. To effectively combine the information provided by positional measurements with system and environment models, the particle filter algorithm is employed, which generates discrete probability distributions. By subjecting these discrete distributions to exploratory techniques, it becomes possible to extract more certain information compared to using raw measurement data alone. Consequently, this study proposes a methodology in which probability density estimation is not solely based on raw positional data but rather on probability-weighted samples generated through the particle filter. This approach yields more compact and precise modeling distributions. Specifically, the method is applied to process position measurement data using a nonparametric density estimator known as kernel density estimation. The proposed methodology is thoroughly tested and validated using information-theoretic and probability metrics. The applicability of the methodology is demonstrated through a practical example of mobility pattern analysis based on forklift data in a warehouse environment.