Automated Storage And Retrieval System Research Articles

A Study on E-Commerce Warehousing Dynamics Navigating Order Fulfilment and Returns

The e-commerce over the past decade has experienced remarkable growth, reshaping shopping habits and challenging traditional supply chain methods. This surge in online shopping has compelled e-commerce firms to enhance their warehousing and order fulfilment strategies to meet rising consumer expectations for swift deliveries and hassle-free returns. Effective management of order fulfilment and reverse logistics has become essential for competitiveness in this industry. This study investigates e-commerce warehousing dynamics, emphasizing the optimization of order speed, delivery times, operational costs, and return management. Through industry reports, academic literature, and case studies, we identify best practices in five key areas: warehouse design and automation, demand forecasting, inventory control, order picking and packing processes, transportation strategies, last-mile delivery models, and reverse logistics for returns. Our research underscores the significance of highly automated warehouses equipped with advanced technologies such as robotics, automated storage and retrieval systems (ASRS), automated guided vehicles (AGVs), and intelligent software for material flow management. These innovations empower e-commerce firms to boost throughput, enhance operational flexibility, and adapt to changing demands effectively.

Automated Storage and Retrieval System for Warehouse

Manual picking and retrieval of items from storage locations can be time-consuming, leading to delays in order fulfillment. This can negatively impact customer satisfaction and the overall responsiveness of the supply chain. In addition, manual data entry and tracking systems are prone to human errors, resulting in discrepancies between recorded and actual inventory levels. This can lead to stockouts, overstocking, and difficulties in maintaining accurate inventory records. Therefore, a fully automated storage and retrieval system is proposed to tackle this problem. The overall system consists of infrared sensor as presence of item stored, LEDs indicator, warehouse model with compartments and stepper motors act as actuator to pick and place the item. This project aims to automate the storage and retrieval system with the assist of monitoring system for accurate tracking of inventory and space savings. The use of Programmable Logic Controller (PLC) in this project is a good representation of integrated automation technology in automated storage system design. Furthermore, monitoring system connected to a web-based system will be used to remotely monitor and operate the facility. Implementing an Automated Storage and Retrieval System (ASRS) in the warehouse can address these challenges by automating the storage, retrieval, and tracking of inventory.

Multi-objective simulation-optimization for integrated automated storage and retrieval systems planning considering energy consumption

An Automated Storage and Retrieval System (AS/RS) is one of modern technologies in warehouse operation. Despite many advantages offered by AS/RS such as improving accuracy, efficiency, and safety, AS/RS operation is very complex started from strategical, tactical, to operational level. Reaching optimal combination for all decisions become important. However, due to the dynamic and combinatorial complexity as well as uncertainty in supply – demand, it cannot be solved through the general mathematical optimization. Therefore, this study introduces simulation–optimization (SO) framework for integrated AS/RS planning considering 7 decisions at a time. Furthermore, a comprehensive mathematical model for measuring AS/RS energy consumption is formulated. The proposed framework is implemented in the China’s warehouse company for optimizing multi-objective namely energy consumption and travel time per unit. Non-dominated Sorting Genetic Algorithm II (NSGA-II) is developed as metaheuristics algorithm and discrete-event simulation is modeled based on FlexSim. The results produce non-dominated solutions that are further summarized through clustering algorithm resulting in 4 different clusters with significantly different impacts. This study provides insightful analysis and managerial implications for reaching near-global optimum in AS/RS planning towards green operation.

A Novel Parts-to-Picker System with Buffer Racks and Access Racks in Flexible Warehousing Systems

With the tremendous development of the logistics industry, the global market of automated warehousing has been growing rapidly. Meanwhile, the warehousing industry shows drawbacks, such as low storage capacity and poor efficiency. By comparing and analyzing the shuttle-based storage and retrieval system (SBS/RS), miniload automated storage and retrieval system (AS/RS), and KIVA system, a novel efficient parts-to-picker approach in flexible warehousing systems is proposed. Among them, buffer racks and access racks, associated with the access of automated mobile robots (AMRs) and stackers are used. The results show that compared with other parts-to-picker systems (such as the KIVA system), this system provides a significant increase in storage capacity (more than three times), and the picking efficiency is also very high at various layout scales, where the picking efficiency is no less than the KIVA system when the number of AMRs reaches the max. The novel system is suitable for small-, medium-, and large-scale warehouses in terms of showing high capacity and producing excellent space utilization. More importantly, this system can easily compete with its traditional counterparts by using a layout of high density without much increase in cost. This sustainable improvement realizes the efficient utilization of spatial resources and provides important support for the construction of green supply chains.

Design and research of automated warehouse simulation platform based on virtual visualization framework

The automated storage and retrieval system (AS/RS) has been developed rapidly and has been widely used in various fields. To achieve the ultimate goal of improving the efficiency of warehouse operation, this article studies the problem of goods location- allocation on the premise of a comprehensive analysis of the efficiency of automated warehouses and the overall stability of shelves. The mathematical model is established. Traditional Tabu search algorithms depend on Tabu table construction to avoid multiple continuous cycles that may occur in the direct search process. In this article, the improved algorithm can directly search and store in the Tabu table to prevent the algorithm from infinite search. A complete solution in that Tabu list continues to be passed to act as a point solution to replace the iteratively update product until a set iteration interval of the product is completed. The machine learning system can get the optimal solution. The improved algorithm is applied to solve the model, and the running results and the performance of the algorithm itself are obtained. The improved Tabu Search algorithm has more advantages in the process of solving the model because it reduces the size of the whole problem. The algorithm also uses the penalty optimization function as a measure of each solution. The greater the penalty is, the worse the solution is, and the smaller the penalty is, the better the solution is. The operation process of the material in and out of the warehouse in one day is studied, and the validity of the optimization scheme of goods location allocation is verified by substituting specific data for the solution. Finally, the case of goods in and out of the warehouse is simulated by using simulation software, and the running state and efficiency of the stacker are displayed according to the data. The Tabu direct search table is combined with the structure and function of the principle of amnesty direct search to achieve the goal of direct search and decision-making. The equilibrium coefficient of its operation is calculated by using the data, which proves that the scheme is feasible and has practical significance and research value.

A Biased-Randomized Discrete Event Algorithm to Improve the Productivity of Automated Storage and Retrieval Systems in the Steel Industry

In automated storage and retrieval systems (AS/RSs), the utilization of intelligent algorithms can reduce the makespan required to complete a series of input/output operations. This paper introduces a simulation optimization algorithm designed to minimize the makespan in a realistic AS/RS commonly found in the steel sector. This system includes weight and quality constraints for the selected items. Our hybrid approach combines discrete event simulation with biased-randomized heuristics. This combination enables us to efficiently address the complex time dependencies inherent in such dynamic scenarios. Simultaneously, it allows for intelligent decision making, resulting in feasible and high-quality solutions within seconds. A series of computational experiments illustrates the potential of our approach, which surpasses an alternative method based on traditional simulated annealing.

Automated storage and retrieval system design with variant lane depths

This paper models a warehouse design problem with 2D automated storage and retrieval system (AS/RS) and 3D AS/RS as options, where 3D racks may have different depths. The design problem is first modeled as a mixed-integer nonlinear program to minimize investment and maximize throughput under various business needs, such as cost structure, responsiveness requirement, demand rates, and inventory levels. The model is then converted to a mixed-integer program based on optimality conditions. A basic Branch-and-Bound algorithm and a modified one are developed to overcome computational challenges. A high throughput expectation leads to a design with shallow storage racks, while high land/equipment costs have opposite impacts. When demand rates are similar across SKUs, heterogeneously distributed inventory levels lead to racks with different depths and assigning SKUs with higher inventory levels to deeper racks. More heterogeneous demand rates also make rack depths more variant and have SKUs with higher demand rates assigned to shallower racks. In other words, the heterogeneities of inventory levels and demand rates have a contradictory effect on the SKU-to-rack assignment and result in racks with more uniform depths when SKUs with high demand also have high inventory levels, which is often the case in practice.

Quantum Optimization for Location Assignment Problem in ASSR

In an Automated Storage and Retrieval System (AS/RS), a location assignment of products is important to improve the picking efficiency. In this paper, the optimization of shelf location assignment with a quantum annealing is investigated. Product pairs are considered in order of picking frequency and are assigned to empty shelves in order of distance from an outlet. Then swapping the position of product in the pair is considered as the decision variable. This reduces the number of required qubits and guarantees the feasibility of solution. The efficiency of quantum algorithm is evaluated by comparing with mixed integer programming (MIP).

Towards Logistics 4.0: A Skill-Based OPC UA Communication between WMS and the PLC of an Automated Storage and Retrieval System

In order to bring intralogistics systems to the same level of interoperability as today’s modern production systems, logistics must take the essential steps towards Industry 4.0. This requires an increasing abstraction level of control logic as an enabler for horizontal and vertical integration. The abstraction will lead to the interconnection ofmanufacturing and logistics control with the production planning and warehouse management systems (WMS). A main enabler for these communication paths are service-oriented architectures (SoA). OPC UA has established itself as a widely used and already adopted SoA-based communication standard in industry. The paper describes the realization of an OPC UAbased approach for the communication between a WMS and a PLC of an automated storage and retrieval system (ASRS). The conceptual basis of communication design are skills of the ASRS. The work is supported by an architectural design with a subsequent prototypical implementation.

Automatic Storage and Retrieval System (AS/RS) Design for Manufacturing System

In this article, the Automatic Storage and Retrieval System (AS/RS) desing used in automatic production systems and the AS/RS simulation technique were studied in this design. Part 1, which was created by simulating with computer code, was composed of two types of cells according to the dimensions of the space. Thanks to the prepared simulation, it offers the performance of AGVs, robots and collection-deposit stations. The simulation created has tested how long the warehouse can withstand periods of recession and depression. The simulation was run in two modes as single command mode and dual command mode. According to the obtained data, it has been obtained that the binary mode gives better performance compared to the single instruction. At the same time, this study; He proved that when the input speed is greater than the output speed, the binary instruction allows more items to be translated in a given time frame compared to a single instruction.

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.

Multiple-Rack Strategies Using Optimization of Location Assignment Based on MRCGA in Miniload Automated Storage and Retrieval System

This paper aimed to introduce multiple-rack strategies in miniload automated storage and retrieval systems (AS/RSs), which included first fit (FF) and best fit (BF) assignment methods based on a matrix real-coded genetic algorithm (MRCGA) in the storage and retrieval process. We validated the probability occurrence of item sizes as a contributory factor in multiple-rack strategies, and compared their capacities, utilization of units and space by equal probabilities or the 80/20 law. According to the analytical methods, BF showed a reduction of more than 11.2% than FF on travel distance, and Type B-FF, Type B-BF and Type C-BF were better able to meet high-density requirements. These strategies provide diversified storage and retrieval solutions for the manufacturing and express delivery industry.

MLATSO: A method for task scheduling optimization in multi-load AGVs-based systems

In the context of increasingly competitive intelligent manufacturing, the multi-load Automated guided vehicles (AGVs) based Automated Storage and Retrieval System (AS/RS) has been of particular interest, as reductions in the number of AGVs required can significantly decrease potential congestions and increase the system effectiveness. In comparison with the single-load AGVs system, more difficult and critical issue of scheduling multi-load AGVs to automate storage/retrieval missions and to maximize economic benefits remains unresolved. Therefore, we propose a task scheduling optimization method for multi-load AGVs-based systems, with which, the objectives of least number of occupied AGVs, shortest travel time and minimum conflicts can be met simultaneously. The experiments are conducted in various scenarios, and verify that our work can use fewer AGVs to optimize tasks delivery, which enables the AS/RS stakeholders to reach win-win results for system performance and AGVs investment, thus maximizing economic benefit.

An enhanced vortex search algorithm based on fluid particle density transfer for global and engineering optimization

The vortex search algorithm is a new meta-heuristic algorithm for solving optimization problems. However, it has some disadvantages such as weak stability, tendency to trap into local minima, and high iterations. To overcome these disadvantages, this paper proposes a density transfer vortex search algorithm based on Cauchy distribution and Archimedean spiral surrounding for global and engineering optimization. The algorithm is divided into two populations. In the first population, the mutual action of the Cauchy random step and Gaussian step is introduced to improve the searchability. To improve the exploitation ability of the algorithm in the current optimal solution, an Archimedean spiral surrounding mechanism is introduced in the second population. Moreover, we introduce the vortex flow mechanism, which mimics the physical characteristics of the vortex flow. With the number of iterations increasing, candidate solutions of the first population are gradually affected by the vortex, and their density continuously shifts to the second population. The relationship between the exploration and exploitation in different iteration periods is reasonably adjusted through the density transfer of two populations. In the experiment, the performance of the proposed algorithm and six competitive algorithms are tested on the IEEE CEC2021 global optimization benchmark suite, eight real-world engineering optimizations and a scheduling problem for automated storage and retrieval systems (AS/RS). The experimental results show that the proposed algorithm has significant improvements in convergence precision, speed and stability. The proposed algorithm not only solves complex global optimizations and real-world engineering optimizations with good convergence performance, but also improves efficiency and reduces energy consumption when solving the scheduling problem of AS/RS.

A permutation-combination heuristics for crane-based automated storage and retrieval systems considering order fulfillment time and energy consumption.

An automated storage and retrieval system (AS/RS) is a key component of enterprise logistics. Its performance metrics include, e.g., order fulfillment time and energy consumption. A crane-based automated storage and retrieval system (CB-AS/RS) is used as the study subject in this paper to build a location allocation model with the goal of minimizing order fulfillment time and minimizing energy consumption. The two-objective problem is transformed into a single-objective problem by the weight method. A genetic algorithm (GA) is used to optimize and simulate the model using spatial mapping coding. A permutation-combination heuristics (PCH) is proposed that follows the coding method and cross-operation of the GA and conducts both arrange-operation and change-operation. During the simulation, the influence of different storage utilization rates and different output and input instruction quantities in a batch of orders on the results is considered. Experimental results show that the results of the PCH algorithm are better than the GA and the optimization results are more stable. In this paper, we provide an optimization idea for the CB-AS/RS researchers and managers.

Order-Picking Efficiency in E-Commerce Warehouses: A Literature Review

With the vigorous development of e-commerce, efficient order picking in e-commerce warehouses has attracted the attention of many scholars. To analyze the issues about order-picking efficiency currently being studied by relevant scholars in e-commerce warehouses, this paper reviews the literature on the application of order-picking strategy and efficiency optimization direction from 2020 to 2022. That mainly falls into two categories of picking systems: “picker-to-parts” and “parts-to-picker”. In the “picker-to-parts” picking system, more attention is paid to the picking strategies of storage assignment and order batching and the integration of multiple picking strategies. In contrast, in the “parts-to-picker” picking system, the main attention is on the man-machine cooperation in the Mobile Robot Fulfillment System (RMFS) and the Automated Storage and Retrieval System (AS/RS), as well as the coordination of the picking station. Further, this paper proposes future research directions for two categories of picking systems: further studying the order splitting strategy and order delivery issues; considering the dynamic uncertainties; combining the automated picking system with different picking strategies, and so on.

Efficiency Optimization of LCC-S Compensated Multiple-Receiver Bidirectional WPT System for Stackers in Automated Storage and Retrieval Systems

In automated storage and retrieval systems (ASRS), adopting wireless power transfer (WPT) technology is an available way to avoid the drawbacks of the contact power supply system for multiple stackers. When stackers are dropping goods, it is meaningful to recycle the power generated by the motors and feedback to the other stackers or the ground power supply for improving the overall efficiency of ASRS. This article proposes a bidirectional WPT (BD-WPT) system for multiple receivers. Based on the values of transmission power and the power flow directions of receivers, a general model of the BD-WPT system is established with identifying the system operation modes and their boundaries. To achieve flexible switching among different operation modes and adapt the required transmission power with maximum efficiency point tracking (MEPT), a control strategy of the BD-WPT system is proposed. In the 2-kW experimental prototype built, the power demands can be met in real time, and the power fed back by the stackers can be supplied to other loads or recycled by the ground power supply with good dynamic performance. Compared with the system without MEPT, the efficiency of the proposed system is improved by 26.7% at most, and the maximum efficiency achieves 96.4%.

Travel time analysis and dimension optimisation design of double-ended compact storage system

Considering the storage increasing demand of ‘multi-variety, small-batch' characteristic goods, we developed a new type of compact warehouse a three-dimensional double-ended compact storage system. Compared with the traditional three-dimensional automated storage and retrieval system (AS/RS) and existing three-dimensional compact AS/RS model, the storage system we designed exhibits excellent characteristics such as high density, high throughput efficiency, and more flexible configuration. There are three key points in our work listed as follows. Firstly, we establish the continuous travel time model under specific operating conditions in this new three-dimensional compact warehouse. Secondly, the three-dimensional size of the storage rack is optimised. The optimal three-dimensional proportion of the storage rack is obtained based on the model’s optimal solution. Finally, numerical examples are given to analyse the influence of the I/O port layouts and related parameters on the system throughput efficiency. According to the optimisation results of the system model, a more reasonable rack dimension can be designed, which can provide an effective reference for the construction of a three-dimensional double-ended compact warehouse.

A Mixed Algorithm for Integrated Scheduling Optimization in AS/RS and Hybrid Flowshop

The integrated scheduling problem in automated storage and retrieval systems (AS/RS) and the hybrid flowshop is critical for the realization of lean logistics and just-in-time distribution in manufacturing systems. The bi-objective model that minimizes the operation time in AS/RS and the makespan in the hybrid flowshop is established to optimize the problem. A mixed algorithm, named GA-MBO algorithm, is proposed to solve the model, which combines the advantages of the strong global optimization ability of genetic algorithm (GA) and the strong local search ability of migratory birds optimization (MBO). To avoid useless solutions, different cross operations of storage and retrieval tasks are designed. Compared with three algorithms, including improved genetic algorithm, improved particle swam optimization, and a hybrid algorithm of GA and particle swam optimization, the experimental results showed that the GA-MBO algorithm improves the operation efficiency by 9.48%, 19.54%, and 5.12% and the algorithm robustness by 35.16%, 54.42%, and 39.38%, respectively, which further verified the effectiveness of the proposed algorithm. The comparative analysis of the bi-objective experimental results fully reflects the superiority of integrated scheduling optimization.

A dynamic operative framework for allocation in automated storage and retrieval systems

Two very important aspects in automated storage and retrieval systems (AS/RS) are productivity (or performance) and maintenance costs. In the literature, as in industry, it is very difficult to find a solution that guarantees satisfactory results in terms of both. Moreover, all the solutions that the scientific community has proposed are static, i.e., the system’s behavior does not change as boundary conditions change. In this paper, we propose an innovative solution known as a dynamic operative framework (DOF), which allows the system to react to changes in operating conditions and guarantees good results in terms of productivity, with a consistent reduction in the distance that handling machines travel and a consequent reduction in maintenance and energy consumption costs. To test the proposed solution, we focused on the shuttle-lift crane AS/RS, a common configuration used to store bundles of long metal bars, and we compared the DOF with four benchmark policies that exploit a class-based reorganization of the stock performed during non-working shifts. Many simulation runs’ results indicated that the DOF ensures a throughput time aligned to that of the benchmarks, but without needing to reorganize the stock during nonworking shifts. In this way, it leads to consistent savings in terms of energy consumption and maintenance costs.