Shuttle-based Storage And Retrieval System Research Articles

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.

Transaction Processing Policies in a Flexible Shuttle-based Storage and Re-trieval System by Real-time Data Tracking under Agent-based Modelling

This study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttle-based storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles be-tween tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data track-ing-based scheduling policies in automated warehousing systems.

Travel time model for multiple-deep shuttle-based storage and retrieval systems

ABSTRACT The paper presents an analytical model for travel time evaluation in double-deep, triple-deep, quadruple-deep and fivefold-deep tier-captive shuttle-based storage and retrieval system (SBS/RS). Under the assumption of the deepest location rule for the storage process and the nearest neighbour rule for the retrieval process, the expressions for single-command and dual-command cycle times of the shuttle vehicle and elevator are developed, from which the SBS/RS performance can be estimated. The model based on the probability theory enables the evaluation of all individual times that make up the cycle times and assessment of the average number of totes relocated during a retrieval process. Special attention is devoted to the modelling of the relocation of totes which block access to the requested tote during the retrieval process. The validity of the analytical model is examined via the developed simulation model. The given case study shows that the proposed analytical travel time model results match the results of the simulation model. The presented modelling strategy could be applied to bigger system racking depths following the same modelling logic.

An analytical approach of multiple-aisle shuttle-based storage and retrieval systems

This paper deals with a method for performance calculation of shuttle-based storage and retrieval system (SBS/RS) with tier-captive aisle-changing shuttles and different storage strategies. The shuttles are assigned to a certain number of aisles in this system. The invented approach takes place in the design process of SBS/RS. A continuous time open-queueing system with a limited capacity approach is applied. A discrete spatial value approach determines the inter-arrival times and service times. A probability-based model mentions the aisle changing. This approach is validated by comparison to a Monte-Carlo simulation. A European material handling provider gave the data used in this comparison to validate this approach. Finally, an example of the usage in the design process of SBS/RS of the invented analytical approach is depicted. The result is an increase in throughput with an increasing number of aisles per shuttle. Likewise, the storage strategy greatly influences the throughput between random storage and retrieval and same aisle storage and retrieval.

Cost and performance comparison of tier-captive SBS/RS with a novel AVS/RS/ML

This paper introduces a novel autonomous vehicle-based storage and retrieval system that utilizes movable lifts (AVS/RS/ML), proposed as an alternative to the tier-captive shuttle-based storage and retrieval system (SBS/RS). The newly proposed system aims to provide an affordable solution with highly utilised AGVs, that can also perform operations out of warehouse. The performance of this novel system is compared with the equivalent tier-captive SBS/RS warehouse design, where each shuttle is dedicated in a specific tier in that design. The comparison is based on the initial system investments costs, throughput rates, and average utilisation of lifts/MLs in the system. Collision prevention rules are also applied to AVS/RS/ML, and its performance is tested through simulation. The results show that the tier-captive SBS/SR system becomes cost-efficient under high throughput rate requirements, while the AVS/RS/ML technology is preferred for relatively moderate and low process rate requirements. The unit-cost per month performance metric of AVS/RS/ML is less sensitive to an increase in number of tiers in the system, compared to the tier-captive SBS/RS case, indicating that AVS/RS/ML may be promising for high-tier warehouse system designs.

Shuttle-Based Storage and Retrieval Systems Designs from Multi-Objective Perspectives: Total Investment Cost, Throughput Rate and Sustainability

This paper studies performance comparison of two shuttle-based storage and retrieval system (SBS/RS) configurations developed on flexible or non-flexible travel policies of shuttles in the system. In the non-flexible SBS/RS, a shuttle is dedicated to a tier so that it cannot travel out of its dedicated aisle and tier. A lifting mechanism is installed in each aisle to provide vertical travel for loads. In flexible SBS/RS, shuttles can travel between tiers by a separate lifting mechanism installed on the other edge point of each aisle. The advantage of that flexible design is that there might be decreased number of shuttles settling in the system compared to the non-flexible design. We simulate the two system configurations and conduct an experimental design for the comparison purpose. Based on the three-performance metrics: total investment cost, throughput rate and energy consumption per transaction, the results show that mainly the flexible system provides better results which might be considered as future system investment for SBS/RS.

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.

Shuttle-Based Storage and Retrieval System: A Literature Review

While the e-commerce logistics industry is developing rapidly, its sustainable development has received certain attention. The ultimate goal of sustainable development is to achieve common, coordinated, fair, efficient, and multi-dimensional development. To promote the sustainable development of logistics, the automation technology of warehousing is undoubtedly an excellent breakthrough, since the automation technology can not only make the warehousing system efficient and with a low-error rate, but also affect the energy consumption of the warehousing system. This paper studies the Shuttle-Based Storage and Retrieval System (SBS/RS) in automated warehousing. Moreover, the paper classifies the existing literature into three categories: Physical design (including depth, configuration, and number of tiers); control strategy (including scheduling rules, storage strategies, scheduling command, and interference); and performance evaluation (including throughput, time, and energy). These categories are all factors that warehouse designers must consider when designing a system. Finally, this paper proposes future research directions for SBS/RS: Adding speed metrics, limiting buffer size, targeting space constraints, applying four-way shuttles, and increasing the carrying capacity of a single machine.

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.

An analytical approach for a performance calculation of shuttle-based storage and retrieval systems with multiple-deep and class-based storage

ABSTRACT This paper presents a method for determining the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier-captive, single-aisle shuttles serving tiers of multiple-deep storage and using a class-based storage policy. This approach is used in the design process of SBS/RS and in the upgrading process of existing SBS/RS. With this approach, it is possible to evaluate the improvement in the performance of multiple-deep storage system by applying a class-based storage policy. The basis of this calculation method is a continuous-time, open-queueing system with limited capacity. The cycle times of lifts and shuttles, as determined by a spatial value approach, combined with a probability-based approach to mention the storage policy. To take the multiple-deep storage into account, another probability-based approach is applied. A European material handling provider had given the data used in this publication. Through an example, the influences of the storage policy and the storage depth are depicted.

A reinforcement learning approach for transaction scheduling in a shuttle‐based storage and retrieval system

AbstractWith recent Industry 4.0 developments, companies tend to automate their industries. Warehousing companies also take part in this trend. A shuttle‐based storage and retrieval system (SBS/RS) is an automated storage and retrieval system technology experiencing recent drastic market growth. This technology is mostly utilized in large distribution centers processing mini‐loads. With the recent increase in e‐commerce practices, fast delivery requirements with low volume orders have increased. SBS/RS provides ultrahigh‐speed load handling due to having an excess amount of shuttles in the system. However, not only the physical design of an automated warehousing technology but also the design of operational system policies would help with fast handling targets. In this work, in an effort to increase the performance of an SBS/RS, we apply a machine learning (ML) (i.e.,Q‐learning) approach on a newly proposed tier‐to‐tier SBS/RS design, redesigned from a traditional tier‐captive SBS/RS. The novelty of this paper is twofold: First, we propose a novel SBS/RS design where shuttles can travel between tiers in the system; second, due to the complexity of operation of shuttles in that newly proposed design, we implement an ML‐based algorithm for transaction selection in that system. The ML‐based solution is compared with traditional scheduling approaches: first‐in‐first‐out and shortest process time (i.e., travel) scheduling rules. The results indicate that in most cases, theQ‐learning approach performs better than the two static scheduling approaches.

A joint optimisation of multi-item order batching and retrieving problem for low-carbon shuttle-based storage and retrieval system

Shuttle-based storage and retrieval system (SBS/RS) is a smart warehouse system evolved from automated storage and retrieval system. It is used for the storage and order picking of products in e-commerce warehouses because of its flexibility and low-carbon feature. This paper investigates the multi-item order batching and retrieving problem for SBS/RS, in which orders containing different items are batched and processed by the system. A mixed-integer linear programming model is proposed to jointly optimise the batching and retrieving assignment, and a tabu search algorithm along with an efficient algorithm for generating retrieving assignments are developed for problem-solving. Numerical experiments are then conducted to validate the efficiency of the proposed algorithm as well as the carbon emission reduction potential of the developed model.

Travel time models for tier-to-tier SBS/RS with different storage assignment policies and shuttle dispatching rules

This paper explores both random and class-based storage policies and three shuttle dispatching rules, random, distance-based, and demand-rate based, for a tier-to-tier shuttle-based storage and retrieval system (SBS/RS). Modeling the system as a discrete-time Markov Chain, this study derived the shuttle distribution under each policy combination, and further developed the expected travel time models for both single-command (SC) and dual-command (DC) operations. The developed models can help a warehouse designer or manager to comprehensively make four decisions for SBS/RS, whether SC or DC operations should be adopted, the most appropriate storage policy, the best shuttle dispatching rule, and the number of shuttles. The travel time models can evaluate each possible policy combination quickly through multiple criteria, such as the capacity, customer responsiveness, and costs of shuttles. Numerical experiments showed significant impacts of the policy combination on the expected travel time. Class-based storage, if implementable, is always better than random storage under each combination of shuttle dispatching rule and operational mode (i.e., SC or DC). Under random storage and SC operations, the distance-based shuttle dispatching rule is always better than the random rule, and their difference is concave in the number of shuttles. When classed-based storage is adopted and the number of shuttles is small, the demand-rate-based rule is the best under SC operations. Its benefit becomes more obvious when demand rates become more heterogeneous until some point but decreases as the system has more shuttles. Under DC Operations, depending on equipment speeds, the distance-based shuttle dispatching rule can be worse than the random rule under random storage. Under class-based storage and DC operations, the demand-rate-based rule is still the best when demand rates are heterogeneous. The differences between SC and DC operations are more significant when demand rates are more homogenous. When a system wants to be more responsive to customers, it should be run under SC operations. If the overall capacity is of interest, DC operations are much better.

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.

Energy consumption model for shuttle-based Storage and Retrieval Systems

Automatic warehouses need to balance speed, cost, flexibility, and energy consumption to support the responsiveness, efficiency, and sustainability of modern supply chains. This paper explores environmentally friendly velocity and acceleration configurations of the Shuttle-Based Storage and Retrieval Systems (SBS/RS) under throughput requirement. This study develops an energy consumption model for the SBS/RS with cross shuttles operating on a dual-command-cycle basis and estimates the maximum throughput, travel time and energy consumption. Numerical experiments explore the optimal velocity profiles with the least energy consumption under different throughput requirements. The results show that the lift velocity and acceleration are the leading factors influencing energy consumption. Increasing the accelerations of aisle and cross shuttles can moderately reduce travel times and improve storage efficiency, without hurting energy consumption. Besides, this paper also identifies the relationship between throughput capacity and energy consumption. Overall, this study provides insights on the SBS/RS configurations under given throughput requirements and environmental performance and builds a green decision-making basis for automatic warehouse designers and managers.

An approach for performance evaluation of SBS/RS with shuttle vehicles serving multiple tiers of multiple-deep storage rack

This paper presents a method for determining the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier-captive, single-aisle shuttles serving various numbers of tiers of multiple-deep storage. The use of this approach takes place in the design process of SBS/RS. The proposed approach considers the real operating characteristics of the shuttle and lifts. The basis of this calculation method is a continuous-time, open-queueing system with limited capacity. The cycle times of the lifts and shuttles, determined by a spatial value approach, can be used directly in the presented method with an assumed uniform distribution of storage locations and a probability-based model of storage depth. This approach is validated by a comparison with a discrete-event simulation. Finally, an example based on a system provided by a European material handling provider is presented to outline how this calculation model can be used for designing SBS/RS that fulfill predefined requirements. The result of this example is a decrease in the needed ground space with an increasing number of tiers served by each shuttle and with increasing storage depth.

Cost and performance comparison for tier‐captive and tier‐to‐tier SBS/RS warehouse configurations

AbstractThis paper compares two shuttle‐based storage and retrieval system (SBS/RS) configurations developed on travel policy of shuttles in the systems. Specifically, we compare two system configurations of SBS/RS: SBS/RS with tier‐captive shuttles and SBS/RS with tier‐to‐tier shuttles. In a tier‐captive SBS/RS warehouse design, there is a dedicated shuttle in each tier that cannot travel between tiers. Due to a large number of shuttles in such a configuration, lifts are always bottlenecks and the utilizations of shuttles are very low. In this study, we propose an alternative SBS/RS design with tier‐to‐tier shuttles in which shuttles can travel between tiers by using a separate lifting mechanism dedicated for them. The advantage of this design would be reduction in investment cost due to the decreased number as well as higher utilizations of shuttles in the system. The two configurations were compared on the following performance metrics: initial investment costs, throughput rates per hour, and average energy consumption per transaction when there is a regeneration mechanism in the system. In a tier‐to‐tier SBS/RS design, allowing shuttles to travel between tiers increases operational complexity of the system. Thus, we simulate the two system designs and compare their performance metrics by conducting several rack design experiments based on the number of aisles, tiers, and bays. The results of this study suggest that there may be a better configuration in terms of total investment cost and throughput rate in a newly proposed tier‐to‐tier SBS/RS design that could be considered as an alternative to a tier‐captive SBS/RS design.

An open queuing network-based tool for performance estimations in a shuttle-based storage and retrieval system

Recently, growth in e-commerce has resulted in substantially increased investment in robotic technologies in warehouses, especially for storage and retrieval devices. A shuttle-based storage and retrieval system (SBS/RS) is one of the robotic automated storage and retrieval technologies mostly used for a miniload material handling system in e-commerce warehouses. This new technology has been developed as an alternative to the traditional miniload crane-based automated storage and retrieval system. Because it has an autonomous order picker robot in each tier of the racking system, the process rate for this new technology is very high. To evaluate several design options for an SBS/RS warehouse quickly, we have developed analytical models for estimating important performance outputs of these systems. Specifically, this study presents an open queuing network-based software tool that can quickly estimate some important performance metrics for a predefined SBS/RS warehouse design. Once the user enters the required input data values in the tool (e.g., the arrival rate of transactions in the system, warehouse metrics, number of tiers, aisles, and bays, and velocity profiles of devices), the performance metric estimates can be obtained in terms of the mean cycle time of a transaction, the mean energy consumption and energy regeneration per transaction, the mean waiting time of a transaction, the mean number of transactions waiting in a server (lift/shuttle) queue, and the mean utilization of servers. The software tool developed can be downloaded for free from the Web site http//:www.sbsrscalculator.com.

Optimization of Storage Location Assignment in Tier-To-Tier Shuttle-Based Storage and Retrieval Systems Based on Mixed Storage

To improve the efficiency of tier-to-tier shuttle-based storage and retrieval system (SBS/RS), the optimization problem of the location allocation based on the mixed storage of goods is proposed. Considering the effect of warehouse operation scheduling and batch outbound allocation on the location allocation, an optimization model with the shortest outbound time of all outbound orders in a certain historical period is established. The optimization model consists of two stages: location allocation and job scheduling. A two-layer genetic algorithm is designed to solve the model. The first layer is used to solve the location allocation, and the coding method is group coding; the second layer is used for job scheduling, and the coding mode is real number coding. When the population is initialized during the location allocation phase, the BFD algorithm is used to improve the convergence velocity of the algorithm. Taking the actual data of a tier-to-tier SBS/RS of an aviation food company as an example, the established model and design algorithm were verified, and the different batch intervals of each cargo space for storing different types of goods and outbound were analyzed. The optimization effects of the algorithm are compared, and the effects of considering the job scheduling and not considering the job scheduling on the location allocation are compared. The results show that based on the cargo allocation strategy of cargo mixed storage, the outbound efficiency can be improved by about 20%. Considering job scheduling, the efficiency of warehousing is improved by about 5% compared with the optimization of warehouse allocation without job scheduling, where the efficiency of delivery is increased by about 6%.

Analytical model to estimate the performance of shuttle-based storage and retrieval systems with class-based storage policy

This paper presents a method to determine the performance of shuttle-based storage and retrieval systems (SBS/RS) with tier-captive single-aisle shuttles and class-based storage policy. The use of this approach takes place both in the design process of SBS/RS and in the redesign process of SBS/RS. With this approach, it is possible to evaluate performance improvement by applying a class-based storage policy. Another beneficial scope of application of this approach is the evaluation of the placement of different classes throughout the rack to achieve the maximum throughput. The basis of this calculation method was a continuous-time open-queueing model with limited capacity. The cycle times of lifts and shuttles, as determined by a spatial value approach combined with a probability-based approach to mention the storage policy, could be directly used in the presented method with their time distributions. The data used herein were provided by a European material handling provider. An example was presented to outline how this calculation model can be used for optimizing the already existing SBS/RS via the application of a class-based storage policy. Through the example, depending on the configuration of the policy, applying the class-based storage to an existing SBS/RS would increase the throughput by up to twice the throughput without class-based storage policy.