Automated Warehouse System Research Articles

Multi‐AGV route planning in automated warehouse system based on shortest‐time Q‐learning algorithm

AbstractRoute planning for automated guided vehicles (AGVs) is one of the key factors that affects work efficiency of automated storage and retrieval systems (AS/RSes). Route planning plays an important role in the operation of AGVs. Since the characteristic of AS/RSes is chessboard‐like, the environment is more complex than traditional route planning environments because the number of nodes is large, more than one shortest route exists between two nodes, and the routes with the shortest distance may not be the most energy‐saving routes. Although the traditional route planning algorithms such as the classical Q‐learning algorithm can work well in AGV route planning, it also has some limitations. This paper proposes a novel multi‐AGV route planning approach to solving the AGV route planning problem in the chessboard‐like warehouse, which can improve the route planning efficiency greatly. First, by combining adjacency matrix and reward matrix, we propose a low‐dimensional adjacency‐reward matrix for route planning. This algorithm improves the efficiency of classical Q‐learning algorithms and accelerates dynamic route planning significantly. We further improve the algorithm by considering the travel directions to minimize the number of turns in the route and additionally by considering whether the AGV is loaded or not and plan routes accordingly. Finally, we propose a multi‐AGV online collision‐free route planning algorithm based on these considerations for dynamic route planning for multi‐AGVs operating in a large‐scale warehouse. The proposed algorithms are validated with several case studies.

Optimizing Replenishment Base on Order Structure in Combined Automatic Warehouse System

Optimizing Replenishment Base on Order Structure in Combined Automatic Warehouse System

Transaction selection policy in tier-to-tier SBSRS by using Deep Q-Learning

ABSTRACT This paper studies a Deep Q-Learning (DQL) method for transaction sequencing problems in an automated warehousing system, Shuttle-based Storage and Retrieval System (SBSRS), in which shuttles can move between tiers flexibly. Here, the system is referred to as tier-to-tier SBSRS (t-SBSRS), developed as an alternative design to tier-captive SBSRS (c-SBSRS). By the flexible travel of shuttles between tiers in t-SBSRS, the number of shuttles in the system may be reduced compared to its simulant c-SBSRS design. The flexible travel of shuttles makes the operation decisions more complex in that system, motivating us to explore whether integration of a machine learning approach would help to improve the system performance. We apply the DQL method for the transaction selection of shuttles in the system to attain process time advantage. The outcomes of the DQN are confronted with the well-applied heuristic approaches: first-come-first-serve (FIFO) and shortest process time (SPT) rules under different racking and numbers of shuttles scenarios. The results show that DQL outperforms the FIFO and SPT rules promising for the future of smart industry applications. Especially, compared to the well-applied SPT rule in industries, DQL improves the average cycle time per transaction by roughly 43% on average.

Quantum behaved particle swarm optimization of inbound process in an automated warehouse

The inbound process is of great importance in enhancing the efficiency of automated warehouse operations. This study investigates an optimization problem on the inbound warehouse process by coordinating multiple resources in a type of automated warehouse system, i.e., Shuttle-Based Storage and Retrieval System (SBS/RS). A mixed-integer programming model is formulated to determine the assignment decisions of the pallets towards three types of the resources in the SBS/RS (i.e., forklifts, lifts and shuttles), the sequencing & timing decisions of these three types of resources for transporting the pallets. Then, a novel solution method, called Adaptive Quantum behaved Particle Swarm Optimization (AQPSO) algorithm, is designed to solve the proposed model. The introduction of the quantum mechanism prevents the algorithm from falling into a local minimum. The integration of the adaptive adjustment strategy improves the efficiency of the algorithm by dynamically adjusting the search scale. The efficiency of the proposed algorithm is verified by comparative experiments that use the CPLEX solver and the basic particle swarm optimization algorithm as rivals. The experimental results indicate that the proposed algorithm have an advantage in the solution quality and the computing time. A series of sensitivity analyses are also conducted to bring out some managerial insights. For example, it is beneficial to reduce energy consumption by adjusting the relative velocity and power of the three types of equipment, and setting the best ratios of shuttles to forklifts.

Order picking optimization with rack-moving mobile robots and multiple workstations

In this paper, we study an automated warehousing system, where racks are moved by robots to multiple workstations so that pickers at each workstation can retrieve the products from the racks to fill up the orders. In this context, the order and rack sequences should be considered simultaneously and the workload balance and rack conflicts among multiple workstations should also be taken into considerations. However, these factors have not been addressed in the current literature. To fill this gap, we formulate a comprehensive multi-workstation order and rack sequencing problem as a mixed integer programming model that accounts for workload balancing and rack conflicts. To solve the model, we propose an adaptive large neighborhood search method, which builds on a newly developed data-driven heuristic that exploits the structure of the problem and simulated annealing. We show that our proposed approach performs well on both small-scale problem instances with synthetic data and a large-scale real-world dataset supplied by a large e-commerce company. In the latter case, it can save up to 62% in rack movements compared to the company’s current practice.

Scheduling Storage Process of Shuttle-Based Storage and Retrieval Systems Based on Reinforcement Learning

The Shuttle-Based Storage and Retrieval System (SBS/RS) has been widely studied because it is currently the most efficient automated warehousing system. Most of the related existing studies are focused on the prediction and improvement of the efficiency of such a system at the design stage. Hence, the control of existing SBS/RSs has been rarely investigated. In existing SBS/RSs, some empirical rules, such as storing loads column by column, are used to control or schedule the storage process. The question is whether or not the control of the storage process in an existing system can be improved further by using a different approach. The storage process is controlled to minimize the makespan of storing a series of loads into racks. Empirical storage rules are easy to control, but they do not reach the minimum makespan. In this study, the performance of a control system that uses reinforcement learning to schedule the storage process of an SBS/RS with fixed configurations is evaluated. Specifically, a reinforcement learning algorithm called the actor-critic algorithm is used. This algorithm is made up of two neural networks and is effective in making decisions and updating itself. It can also reduce the makespan relative to the existing empirical rules used to improve system performance. Experiment results show that in an SBS/RS comprising six columns and six tiers and featuring a storage capacity of 72 loads, the actor-critic algorithm can reduce the makespan by 6.67% relative to the column-by-column storage rule. The proposed algorithm also reduces the makespan by more than 30% when the number of loads being stored is in the range of 7-45, which is equal to 9.7%-62.5% of the systems' storage capacity.

A Set of Tracking Car Scheduling Management System

With the rapid improvement of China’s economic level and the rapid development of e-commerce, the demand for logistics warehousing, which is one of the most important links in the logistics transportation system, has also greatly increased. The dispatching management system applied in the warehouse has a wide range of applications in the logistics storage system. Improving the operational efficiency of the dispatching management system can effectively improve the efficiency of the automated warehouse system and reduce the cost of logistics transportation. Therefore, this paper designs a set of dispatching systems with the tracking car as the control target and realizes the positioning and path planning functions of the tracking car in the system. At present, the tracking car can completely receive the commands sent by the system, accurately follow the established path. At the same time, the system software can also obtain the video images taken by the camera on the car and play them smoothly.

Development Possibilities of the High-tech Logistics Laboratory Established at the Institute of Logistics of the University of Miskolc

The Institute of Logistics of the University of Miskolc started training logistics in Hungarian higher education for the first time in the early 1990s. In recent decades, the training structure and laboratory infrastructure of the institute have changed significantly, relying on the relevant educational and research connections in Germany. The publication presents one of the institute's laboratories, the High-tech Integrated Logistics Laboratory, which contains a number of material handling equipment (pallet handling cell, AGV, automated warehousing system, etc.), which provides an opportunity for fulfillment of high-quality teaching and research tasks. In addition to a brief presentation of the laboratory, the researchers present their future ideas about the laboratory in different areas of development, which can also serve as a useful guide for other professionals involved in logistics education and research.

Design Optimization of Redundantly Actuated Cable-Driven Parallel Robots for Automated Warehouse System

This study focuses on the optimization of the geometry configurations and mobile platform parameters of redundantly actuated cable-driven parallel robots (RA-CDPRs) for automated warehouse system (AWS). Owing to their potential structural advantages, RA-CDPRs are proposed to replace conventional stackers in AWS, to achieve a high payload mass and low cable tension in frequent automated retrieval/storage operations. To meet these operational requirements, the maximal payload criterion satisfying the wrench-feasible condition is used to determine the optimal geometry configuration. Based on the optimal geometry configuration, the minimal cable tension is designed as the other criterion for optimizing the mobile platform parameters of RA-CDPRs. The optimization method is validated on a 6-DOF RA-CDPR, and the optimal results are simulated and implemented under static equilibrium conditions. Both the simulation and experimental results verify the optimal results can significantly enhance the payload mass and decrease the cable tension of AWS.

Travel time models for the rack-moving mobile robot system

The rack-moving mobile robot (RMMR) system is a special parts-to-picker automated warehousing system that uses hundreds of rack-moving machines to accomplish the repetitive tasks of storing and retrieving parts by lifting and transporting unit racks autonomously. This paper investigates the operation cycle of the rack-moving machine for storage and retrieval from the perspective of the lane depth, especially exploring the particularity of the RMMR system in multi-deep lanes, and proposes expected travel time models of the rack-moving machine for single- and multi-deep layouts of the RMMR system. To validate the effectiveness of the proposed models, an experimental simulation was conducted with a 1–4-deep layout under six scenarios of different numbers of aisles and layers, and results were compared with results obtained using proposed models. The paper presents useful guidelines for the configuration of the RMMR system layout including the determination of the optimal lane depth.

Automated Ware Housing System for Differently Abled Person

Automated Ware Housing System for Differently Abled Person

Storage Assignment with Rack-Moving Mobile Robots in KIVA Warehouses

This paper treats an automated warehousing system, known as the KIVA system, where mobile robots lift complete racks and autonomously move them to stationary pickers. This innovative parts-to-picker system relieves the pickers from unproductive walking times without requiring high investment costs into inflexible hardware such as conveyors, storage and retrieval machines, or lifts. In this context, we treat the decision problem where to park the racks during order processing when they are consistently moved between the picking stations and the storage area. We formalize the resulting rack assignment problem as a special interval scheduling problem and introduce a new matheuristic dubbed adaptive programming. In a comprehensive computational study, we compare the results of our optimization approach with simple rule-based assignment policies. Our results reveal that the well-established rules widely applied in traditional picker-to-parts warehouses, for example, random and dedicated storage, lead to considerable optimality gaps. Adapted rules, however, which consider the peculiarities of KIVA warehouses, lead to very good results. The e-companion is available at https://doi.org/10.1287/trsc.2018.0826 .

SIMULATION SOFTWARE FOR MODELING THE MOVEMENT OF MATERIAL FLOWS

The Computer modeling is one of the best tools for the development of a real automated warehouse system. It aims to explore and define the behavior of the system and make the evaluation of its performance. In this paper is analyzed and simulated a software for the design of logistic systems.

Collision-Free Route Planning for Multiple AGVs in an Automated Warehouse Based on Collision Classification

An automated warehouse system contains a number of materials, workstations, and multiple Automated Guided Vehicles (AGVs). The automated warehouse is server-controlled. This paper proposes a collision-free routing method for AGVs based on collision classification. This method can deal with collisions arising in the automated warehouse. It first divides the warehouse environment into five areas, and then performs route planning. In this paper, the environment map for AGVs is described by using the grid method. The initial route of each task is predetermined by improved Dijkstra’s algorithm. The server detects the potential collisions by comparing each workstation’s ID and corresponding time window in every route. This paper presents four collision classifications and three solutions. Based upon the analyses and experiments, we select the corresponding solution for each type of collision. Presented case studies demonstrate the efficiency of the proposed collision-free route planning approach.

Design and optimization of an RFID-enabled automated warehousing system under uncertainties: a multi-criterion fuzzy programming approach

In this paper, we investigated the design and optimization of a proposed radio-frequency identification (RFID)-enabled automated warehousing system in terms of the optimal number of storage racks and collection points that should be established in an efficient and cost-effective approach. To this aim, a fuzzy tri-criterion programming model was developed and used for obtaining trade-off decisions by measuring three conflicting objectives. These are minimization of the warehouse total cost, maximization of the warehouse capacity utilization, and minimization of the travel time of products from storage racks to collection points. To reveal the alternative Pareto optimal solutions using the developed model, a new approach was developed and compared with a recently developed fuzzy approach so-called Selim and Ozkarahan (SO). A decision-making algorithm was used to select the best Pareto optimal solution, and the applicability of the developed model was examined using a case study. Research findings demonstrate that the developed model is capable of generating an optimal solution as an aid for the design of the proposed RFID-enabled automated warehousing system.

자동화창고 시스템의 신뢰성 확보를 위한 연구

최근 국내 외 물류환경이 급변하고 있어 국내기업들은 물론이거니와 해외기업들까지 자동화창고 개발 및 투자를 확대하고 있다. 이러한 환경속에서 자동화창고 시스템은 급변하고 있으며, 그에 따라 다양한 기능을 갖춘 자동화창고 시스템이 개발되고 있다. 이렇게 자동화창고 시스템의 새로운 기술들이 많이 개발되어 양적인 면은 커져가고 있지만, 자동화창고 시스템의 제품 품질평가체계인 질적인 면은 제자리걸음이다. 본 논문에서는 자동화창고의 제품평가체제 확립을 위해다양하게 발전하고 있는 자동화창고 시스템의 특성과 기술의 동향을 분석하고, 이를 기반으로 자동화창고 시스템의 기능성과 신뢰성을 확보하기 위해 제품평가에 관한 국제품질평가 표준인 ISO/IEC 25000 시리즈를 참조하여 기능적합성과 신뢰성의 평가항목을 도출하였으며, 도출한 평가항목을 통해 평가모델을 개발하였다. 본 논문을 통해 자동화창고 시스템의 품질수준을 높이기 위한 기능적합성과 신뢰성의 평가기준을 확립할 것으로 사료된다. Recently, not only domestic businesses but also overseas businesses are expanding development and investment in automated warehouses as domestic and foreign logistics environments undergo drastic change. In such an environment, automated warehouse systems are being rapidly changed, and automated warehouse systems equipped with diversified functions are being developed accordingly. But the assessment systems of developed automated warehouse system is at a standstill. In the present article, characteristics of the automated warehouse systems in diversified development and trends of technology were analyzed, based on which evaluation items of functional suitability and reliability were derived by referring to ISO/IEC 25000 as the international quality assessment standard to secure reliability of the automated warehouse system, and an evaluation model was developed through the derived evaluation items. Through the present article, evaluation standards of functional suitability and reliability to enhance quality levels of automated warehouse systems are considered to be secured.

Packing Order Splitting Optimization Based on Cloud Model Culture Algorithm

In online-shopping, the size and quantity of most orders are different. So how to pick and pack the objects of orders becomes a critical problem. To solve this problem, a picking and packing integrated model in automated warehouse system is proposed. The objects of one order can be split into smaller orders according to the volume and loading weight of container. Then the objects can be packaged in the suitable containers. A dispatching algorithm based on cloud model and culture algorithm is proposed. The steps of the proposed algorithm are discussed in detail. Finally, the example result shows that it can effectively optimize picking and packing operations and reduce the waste of containers.

Fleet sizing of laser guided vehicles and pallet shuttles in automated warehouses

The paper deals with emerging automation technologies for pallet handling in warehouses characterized by multiple-depth rack storage systems. In particular, this study addresses the adoption of Pallet Shuttles (PSs) for material handling within the rack structure, i.e., for put-away and retrieval operations from the rack face to the specific pallet location and vice versa. PSs are used in combination with Laser Guided Vehicles (LGVs) so as to obtain a fully automated warehousing system. The study develops an analytical model for the joint LGV–PS fleet sizing problem, also taking into consideration stochastic phenomena and queueing implications. This enables the analyst to compute the utilization of LGVs and PSs as a function of sizes of the two fleets. Then, a queueing model can be applied to estimate the cycle time of the orders in the warehousing system. An example to clarify the application of the method is also provided.

A Reconfigurable Mechanism of Cantilever Storage Racks

An automated warehousing system often consists of storage modules, storage and retrieval mechanism, lifting equipment and conveyor systems. These facilities are used to overcome some disadvantages of manually operated warehousing systems which often lead to a high frequency of human errors, high labour cost and low efficiency. The usage of these automated facilities may also be helpful to maximise utilisation of shop floor space and improve efficiency of warehousing operations. On the other hand, at this age of information technology that the access to the internet is just at the fingertip, it has been seen a rising trend for online shopping in the past decade. Thus, manufacturers, suppliers or distributors have been seeking for even more efficient and effective new methods to store and distribute their goods. A number of studies through a literature review suggest that future generation warehouses may be designed and implemented as more centralised and distributed warehousing systems in which customers place their orders on-line and goods will then be dispatched directly to their door steps from these centralised distribution centres. This paper presents a proposal for designing and implementing future generation warehousing systems using fast-growing IT technologies such as radio frequency identification (RFID), wireless sensors and networks. The overall purpose of this study is to develop a conceptual design of an integrated system for future generation automated warehouses by incorporating a new design of storage and retrieval modules with the applications of these IT technologies used for automation activities. The presentation of this paper only highlights the part of this work including a design of a core mechanism for automated storage module namely the cantilever storage racks, which have the feature of scalability and reconfigurability.

Route Optimization of Stacker in Automatic Warehouse Based on Genetic Algorithm

Today, automatic warehouse system gradually replaced manual labor, and played an important role in the production work, especially in the cargo handling work. It was important to research the time-consuming and efficiency of stacker in the automated warehouse system. This paper researched the path of stacker in automated warehouse and calculated the operation time of stacker working path according to actual working condition, and then put forward a route optimization method of stacker based on genetic algorithm, finally simulated this algorithm by using MATLAB. The simulation results showed that this algorithm could shorten the way of stacker, and increase the working efficiency of the warehouse. DOI: http://dx.doi.org/10.11591/telkomnika.v11i11.2959