Efficiency Of Warehouse Research Articles

Dynamic Scheduling Strategy for Multiple AGVs based on 0-1 Planning Model

The coordination of handling robots and AGVs can greatly improve the efficiency of unmanned warehouse picking operations and realize the automated management of warehouse logistics. The unreasonable scheduling scheme can lead to local congestion and collision of AGVs and reduce the picking efficiency of the unmanned warehouse. Therefore, this paper conducts an in-depth study on the scheduling mode of AGVs. When assigning orders, this paper designs an order-oriented double matching method, with the shortest total path length of AGV as the objective function, and establishes a 0-1 planning model to complete the matching of order-storage position nodes-AGV. The AGV searches and selects the nearest picking station node whose docking position is not full when it leaves the warehouse. In addition to the main order list, a workstation task list is created, and the workstation tasks are released by the picking stations, including return and recovery. In this paper, the default order and workstation tasks have the same priority, and the task selection order is the same as the release order. The 0-1 planning model is solved using a genetic algorithm to match the AGV with the order or station task that has the shortest path to completion.

Forklift with small turning radius and its efficiency

Abstract A forklift is one of the most widely used vehicles in industry sectors in few decades. Therefore, it is important to create or improve their entire designs. This article proposes a method for determining the required corridor width by calculating the turning radius of standard counterweight Forklift and the new construction of forklift and compares their parameters. The effect of the new type of fork on the efficiency of forklifts and warehouses was studied.

A fuzzy logic based assessment algorithm for developing a warehouse assessment scheme

A new integrated assessment algorithm is proposed for a warehouse assessment scheme. This new algorithm integrates three paradigms, namely, Grey Relational Analysis (GRA), Data Envelopment Analysis (DEA), and an Interval Type-2 Fuzzy Logic System (IT2FLS). Based on the defined criteria and the various warehouses assessed according to these criteria, the GRA is employed to determine the grey relational grade. The DEA is, then, utilized to estimate the warehouse efficiency scores. Finally, the IT2FLS is developed to map the efficiency scores of the warehouses to the assessed values of the defined criteria. The developed IT2FLS can be used to assess new warehouses without re-performing the calculations of the GRA and the DEA. Validated on several warehouses, the integrated assessment algorithm can assess warehouses successfully, and can tackle the uncertainties during the assessment process. In addition to comparing the warehouses with the best practice, it can also provide managers with a linguistic understanding of the effect of the defined criteria on the warehouse performance.

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.

Improving Efficiency for Retail Warehouse Using Data Envelopment Analysis

Warehouse has an important role in supply chain management and has many complex activities that require special attention. This study aims to improve warehouse efficiency performance. Data Envelopment Analysis (DEA) method is employed to obtain the level of efficiency and benchmarking on five indicators, namely financial, productivity, utilization, quality, and cycle time along with five business processes in warehousing, i.e. receiving, put away, storage, order picking, and shipping. The decision making unit is a warehouse in four retailers in Yogyakarta province, in Indonesia. The input and output variables are selected based on the highest priority weight using the Analytical Hierarchy Process (AHP). The most important variable for receiving is productivity (receipt per man-hour), variable for put away is cycle time (put away cycle time), variable for storage is utilization (% location and cube occupied), variable for order picking is cycle time (order picking cycle time) and variable for shipping is productivity (order prepared for shipment per man-hour). The research results show that the benchmarking model with DEA can be used to increase warehouse efficiency performance by up to 22% by increasing receiving and shipping productivity, increasing storage utilization and reducing cycle time at put away and order picking.

Human localization in robotized warehouses based on stereo odometry and ground-marker fusion

Modern logistic solutions for large warehouses consist of a fleet of robots that transfer goods, move racks, and perform other physically difficult and repetitive tasks. The shopfloor is usually enclosed with a safety fence and if a human needs to enter the warehouse all the robots are stopped, as opposed to only the ones in the most immediate vicinity of the human, thus significantly limiting the warehouse efficiency. To tackle this challenge, an integrated safety system is needed with human localization as one of its essential components. In this paper, we propose a novel human localization method for robotized warehouses that is based on a suite of wearable visual sensors installed on a vest worn by humans. The proposed method does not require any modifications of the warehouse environment and relies on the already existing infrastructure. Specifically, we estimate the human location by fusing stereo visual-inertial odometry data and distances to the known absolute poses of the detected ground-markers which robots use for their localization. Fusion is performed by building a pose graph, where we treat estimated human poses relative to markers as graph nodes and odometry estimates as graph edges. We conducted extensive laboratory and warehouse facility experiments, where we tested the reliability and accuracy of the proposed method and compared its performance to a state-of-the-art visual SLAM solution, namely ORB-SLAM2. The results indicate that our method can track absolute position in real-time and has competitive accuracy with respect to ORB-SLAM2, while ensuring higher localization reliability when faced with structural changes in the environment. Furthermore, we provide publicly the experimental datasets to the research community.

A Visualization Approach for Monitoring Order Processing in E-Commerce Warehouse.

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset.

Improved efficiency of warehouse picking by co-optimization of order batching and storage location assignment

Improved efficiency of warehouse picking by co-optimization of order batching and storage location assignment

A Proposal of Path Planning for Robots in Warehouses by Model Predictive Control without Using Global Paths

With the development of the e-commerce business, there are high expectations for the use of automated transport robots to improve the efficiency of warehouses. In this study, we propose a path planning method based on Model Predictive Control (MPC) that optimizes the performance of the entire warehouse for multiple robots. In general, when the MPC is used to execute path planning, global path planning such as Dijkstra algorithm are used together. This is because, if the MPC is used alone, the existence of a local optimal solution will cause a situation where the robot cannot move. However, in order to use the global route, it is necessary to prepare the precise map information of the warehouse in advance. In this study, we propose a path planning method that avoids deadlock without using global path by improving an objective function for the MPC. The effectiveness of the proposed method is verified in a numerical simulation.

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.

Towards a conceptualisation of Order Picking 4.0

Order picking is a key task in almost all supply chains and has a significant effect on operational efficiency of warehouses. Although most companies still rely on manual order picking, research on diverse possibilities to automate order picking tasks or support human order pickers with technology is increasing rapidly.This paper conceptualises Order Picking 4.0 (OP 4.0), considering substitutive and supportive technologies. Based on a conceptual background, a framework for OP 4.0 as a sociotechnical system is developed. A systematic literature review is performed to assess the state of knowledge in this field, and prospective research opportunities in OP 4.0 are highlighted.

A novel particle swarm optimization-based grey model for the prediction of warehouse performance

Abstract Warehouses constitute a key component of supply chain networks. An improvement to the operational efficiency and the productivity of warehouses is crucial for supply chain practitioners and industrial managers. Overall warehouse efficiency largely depends on synergic performance. The managers preemptively estimate the overall warehouse performance (OWP), which requires an accurate prediction of a warehouse’s key performance indicators (KPIs). This research aims to predict the KPIs of a ready-made garment (RMG) warehouse in Bangladesh with a low forecasting error in order to precisely measure OWP. Incorporating advice from experts, conducting a literature review, and accepting the limitations of data availability, this study identifies 13 KPIs. The traditional grey method (GM)—the GM (1, 1) model—is established to estimate the grey data with limited historical information but not absolute. To reduce the limitations of GM (1, 1), this paper introduces a novel particle swarm optimization (PSO)-based grey model—PSOGM (1, 1)—to predict the warehouse’s KPIs with less forecasting error. This study also uses the genetic algorithm (GA)-based grey model—GAGM (1, 1)—the discrete grey model—DGM (1, 1)—to assess the performance of the proposed model in terms of the mean absolute percentage error and other assessment metrics. The proposed model outperforms the existing grey models in projecting OWP through the forecasting of KPIs over a 5-month period. To find out the optimal parameters of the PSO and GA algorithms before combining them with the grey model, this study adopts the Taguchi design method. Finally, this study aims to help warehouse professionals make quick OWP estimations in advance to take control measures regarding warehouse productivity and efficiency.

Robotic Warehouse Management System

This study presents two approaches to the prevention of inter-robot collisions. The first approach is to develop trajectory planning and motion control algorithms. The second approach is to reduce the number of auxiliary robots as much as possible. The rigidly programmed systems are less flexible and adaptive than systems capable of independent data analysis and pattern identification. Therefore, this study uses the neural network for robot training and an analytical module (AN) to make decisions regarding the quantity of robots. The AN assisted and non-assisted management systems were examined under the two scenarios, namely the steady and random increment of applications. In both scenarios, using the AN reduced the number of auxiliary robots and, consequently, robot collisions in the operating area. This can help to reduce the warehouse maintenance costs and improve manufacturing scalability. Therefore, the proposed robotic management system has the potential to enhance warehouse efficiency.

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV- picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.

Stochastic premarshalling of block stacking warehouses

Warehouse premarshalling (also pre-marshalling or remarshalling) is the activity of reordering items in a storage location so that subsequent retrieval orders can be serviced with little or no need for further relocations. It has deep impact on warehouse efficiency. We are interested in a stochastic case, where pickup orders become known only at the moment when they are to be retrieved. The problem is framed in a business analytics settings, where a forecasting statistical model based on historic data generates the input of a two stage stochastic optimization module. Computational results both on artificial and real-world data confirm the effectiveness of the approach.

The Implementation of Barcode on Warehouse Management System for Warehouse Efficiency

Technology has the power to affect and shape modern life aspects. Technology has speed up and simplify every task. One of those technologies that we currently use is Barcode. Barcode system can be implemented in the warehouse management system. By using Barcode in every process of the warehouse management system, it helps to minimize human error and provide accurate data in a real-time. Barcode also helps to integrate every process and improves efficiency in the warehouse management system. However, there are lots of warehouses that using manual handling. Therefore, in this research authors would like to discuss about the implementation of the barcode in the warehouse management system to improve warehouse efficiency. This research is conducted by using the qualitative method because this method will explain in depth the process in the warehouse management system as well as data collecting by conducting a semi-structural interview. Authors recommended barcode implementation in the warehouse management system to achieve warehouse efficiency.

Design of warehousing system in order picking process: literature review

The warehouse is one part of a company that is holding an important role in the supply chain as a link between producers with the consumer. One of the most dominant activities in warehouse operations is the order picking process. This activity is very important because it directly affects consumer services. Even order picking generally spends around 55 % of total warehouse operating costs. The purpose of this paper is to build a framework that affects the order picking process using the Genetic Algorithm method. Some variables that influence the order picking process, especially the efficiency of order picking time include travel time, search time, pick time, setup time, number of workers and efficiency of workers. One of the causes of the high time of orders picking is the preparation of products that are not right in the warehouse. Genetic algorithms are used to find the best combination of product compilation. So that it is expected that improvements in the order picking process can improve warehouse efficiency.

Order batch picking optimization under different storage scenarios for e-commerce warehouses

To improve the operational efficiency of e-commerce warehouses, multi-location storage systems which means each stock keeping unit can be stored in multiple locations or a location can contain multiple stock keeping units, have been developed and applied in practice. When orders are picked in a batch, how to select the picking location from storage locations holding the identical stock keeping unit obviously affects how far the pickers must travel to complete the picking tasks. However, few works have systematically studied how to optimize order batch picking from the perspective of different storage systems. This paper formulated the order batch picking optimization problems for three typical storage systems and developed the algorithm package including location interval distance algorithm, location selection algorithm, routing algorithm and order batching algorithm to tackle them. Our work is particularly capable to covering the situation of multi-location storage system. The numerical experiment results show that the performance of the proposed algorithm combinations is satisfactory to solve the problems with different size both in solution quality and computation efficiency. The applicable algorithm combinations used in practice are also recommended by comparative analysis. Our study can provide valuable decision reference to warehouse managers for operating batch picking system especially under multi-location scenarios efficiently.

Simulation modelling and analysis of warehouse operations in a food grain supply chain

This paper deals with simulation modelling and analysis of operations at food grain warehouses owned by an Indian organisation. The organisation undertakes food grain transportation using rail wagons and the delay in unloading food grains at warehouses results in the incurrence of penalty cost called demurrage cost. A discrete event simulation model is developed using Arena to analyse the warehouse operations with a special focus on unloading. The simulation model is run using historical data collected from the organisation and performance measures such as resource utilisation, capacity utilisation and demurrage cost are ascertained to assess the efficiency of warehouses.

Simulation modelling and analysis of warehouse operations in a food grain supply chain

This paper deals with simulation modelling and analysis of operations at food grain warehouses owned by an Indian organisation. The organisation undertakes food grain transportation using rail wagons and the delay in unloading food grains at warehouses results in the incurrence of penalty cost called demurrage cost. A discrete event simulation model is developed using Arena to analyse the warehouse operations with a special focus on unloading. The simulation model is run using historical data collected from the organisation and performance measures such as resource utilisation, capacity utilisation and demurrage cost are ascertained to assess the efficiency of warehouses.