Autonomous Vehicle Storage Research Articles

Scheduling Optimization of Compound Operations in Autonomous Vehicle Storage and Retrieval System

The increasing demand for storing various types of goods has led to a raise in the need for storage capacity in warehousing systems. Autonomous vehicle storage and retrieval systems (AVS/RSs) offer high flexibility by allowing different configurations to meet different storage requirements. The system mainly completes operations through elevators and multiple rail-guided vehicles (RGVs). This paper focuses on the scheduling optimization of compound operations in the AVS/RS to improve system performance. Compound operations involve the coordinated execution of both single-command and double-command operations. A mathematical model with compound operations was proposed and effectively decomposed into a horizontal component for RGVs and a vertical counterpart for the elevator, which can represent the operations of one elevator cooperating with multiple RGVs. The goal of this model was to minimize the makespan for compound operations and to determine the optimal operation sequence and path for RGVs. An improved discrete particle swarm optimization (DPSO) algorithm called AGDPSO was proposed to solve the model. The algorithm combines DPSO and a genetic algorithm in an adaptive manner to prevent the algorithm from falling into local optima and relying solely on the initial solution. Through rigorous optimization, optimal parameters for the algorithm were identified. When assessing the performance of our improved algorithm against various counterparts, considering different task durations and racking configurations, our results showed that AGDPSO outperformed the alternatives, proving its effectiveness in enhancing system efficiency for the model. The findings of this study not only contribute to the optimization of AVS/RS but also offer valuable insights for designing more efficient warehouses. By streamlining scheduling, improving operations, and leveraging advanced optimization techniques, we can create a more robust and effective storage and retrieval system.

Blockage‐free storage assignment and storage/retrieval scheduling in autonomous vehicle storage and retrieval systems

AbstractAutonomous vehicle storage and retrieval systems have greatly increased in popularity in the last decade. In such a system, at each tier multiple roaming vehicles transport totes between the storage locations and the lifts. However, this may lead to vehicle interference. We study in which order and by which vehicle the storage and retrieval requests should be executed to minimize the makespan, without vehicle interference. The optimal storage locations for incoming totes are also determined. A blocking mitigation protocol is proposed to address vehicle interference. We propose a two‐phase matheuristic, where in the first phase, the tier is divided into zones, with each zone assigned its own vehicle. The second phase focuses on reassigning requests between adjacent vehicles to obtain improved solutions. The models proposed in both phases are solved to optimality in polynomial time and pseudo‐polynomial time, respectively. Computational experiments show that the matheuristic produces high‐quality solutions within a few seconds, even for large‐sized instances, making it suitable for real‐time decisions. Compared to methods commonly used in practice, our matheuristic can reduce the makespan by up to 15%. Our results show that making integrated decisions that combine storage assignment and request scheduling, is more beneficial than sequential optimization in terms of throughput performance, space utilization and overall system cost. We also find that increasing the number of vehicles has a diminishing return effect on the makespan. Another finding is that the system with a large number of short storage aisles leads to higher throughput capacity than that with a small number of long storage aisles.

Retrieval sequencing in autonomous vehicle storage and retrieval systems

ABSTRACT Autonomous vehicle storage and retrieval systems (AVS/RSs) are widely used in e-commerce warehouses due to their high throughput and flexibility. In such systems, storage and retrieval transactions are performed by lifts and vehicles. This paper focuses on the sequencing retrievals problem in an AVS/RS, which is an important problem for daily operations. We formulate this sequencing problem as a mixed-integer program to determine a retrieval sequence for the lift and the vehicles, one that minimises the makespan. A dynamic programming approach is proposed to solve the sequencing problem to optimality. However, the solution time of the dynamic programming method is exponentially increasing in the number of retrieval requests. To be more practical, we present a beam search heuristic that can solve large-sized instances in reasonable time. Computational experiments verify that near-optimal solutions can be found by the beam search heuristic. Compared to commonly used heuristics and straightforward heuristics, the beam search decreases the makespan by up to 15%. Finally, we analyse how vehicle modes impact the makespan, showing evidence that a small makespan can be achieved when considering a realistic mode of vehicles.

Hybrid model for the design of a deep-lane multisatellite AVS/RS

The autonomous vehicle storage and retrieval system (AVS/RS) significantly improves the responsiveness and throughput of the traditional automated storage and retrieval system (AS/RS) in regard to handling unit loads. The AVS/RS consists of multiple tiers connected to an elevator system and is equipped with at least two autonomous vehicles, that is, a shuttle and satellite. Other necessary equipment are the lifts and input/output buffer areas. This paper aims to present and apply an original hybrid analytical-simulative model for the design of a deep-lane and multisatellite AVS-RS by evaluating and controlling the system performance. This AVS-RS is equipped with multiple free and non-free satellites for each tier. As an original contribution, this study reviews the literature on AVS/RS according to the introduction of multiple features categorized into five homogeneous groups: (1) rack configuration, (2) vehicle kinematics and configuration, (3) dispatching rules, (4) modeling approach, and (5) validation. Two of the most critical issues in existing research studies are the random arrival time of storage and retrieval transactions and the random storage policy. The proposed modeling approach is data-driven and based on realistic assumptions, filling the gap between the literature and real applications. This hybrid model is applied to a case study of the beverage industry according to a what-if comparative and competitive multiscenario analysis. This data-driven assessment supports the decision-making process on the number of satellites for each tier, while simultaneously controlling the service and waiting times, system throughput, and vehicle utilization. The analysis based on the maximum system throughput estimation demonstrates that introducing more than two satellites does not increase the productivity of the system.

Storage efficiency in a deep-lane AVS/RS

Autonomous Vehicle Storage and Retrieval Systems (AVS/RS) are largely widespread in modern companies. Most literature focuses on system performance estimation, such as throughput and cycle time to perform a storage or retrieval transaction. Little attention has been paid to analysing the impact of storage policies and dispatching rules on space efficiency, i.e., storage capacity exploitation. This paper aims to present and apply an original set of indicators to measure the impact of storage policies and dispatching rules on space efficiency, supporting the decision-making process on the design, management, and control of an AVS/RS. The proposed indicators are applied to a real AVS/RS deep-lane from the food and beverage industry.

Research on Autonomous Vehicle Storage and Retrieval System Cargo Location Optimization in E-commerce Automated Warehouse

Cargo allocation optimization is important to improve the efficiency of e-commerce automated warehouse’s autonomous vehicle storage and retrieval system (AVS/RS) operations. Considering the electrical business logistics characteristic of small batch, batches, variety characteristics, this paper aiming at maximizing the highest efficiency and shelf stability for warehousing efficiency, we established an initial cargo location model, designed an adaptive multi-objective genetic algorithm. MATLAB software programming was used to implement the model solution, and an example verification was performed with an automation warehouse named Jing-dong in China. The comparison between the initial cargo location model and the random storage strategy shows that the model can significantly improve the overall warehousing efficiency and shelf stability of AVS/RS.

Energy Evaluation of Deep-Lane Autonomous Vehicle Storage and Retrieval System

With the rise of a consciousness in warehousing sustainability, an increasing number of autonomous vehicle storage and retrieval systems (AVS/RS) is diffusing among automated warehouses. Moreover, manufacturers are offering the option of equipping machines with energy recovery systems. This study analyzed a deep-lane AVS/RS provided with an energy recovery system in order to make an energy evaluation for such a system. A simulator able to emulate the operation of the warehouse has been developed, including a travel-time and an energy model to consider the real operating characteristics of lifts, shuttles and satellites. Referring to a single command cycle with a basic storing and picking algorithm for multiple-depth channels, energy balance and recovery measurements have been presented and compared to those of a traditional crane-based system. Results show significant savings in energy consumption with the use of a deep-lane AVS/RS.

Analytical models for cycle time and throughput evaluation of multi-shuttle deep-lane AVS/RS

The increased need for just-in-time delivery of finite goods has pulled the development of novel automation solutions to manage warehouse activities. Among the available technologies, autonomous vehicle storage and retrieval systems (AVS/RS) rely on light vehicles able to travel independently and to perform different tasks at the same time, thus exhibiting enhanced flexibility and increased throughput level. Nonetheless, techniques to evaluate the performance of such systems still exhibit some gaps and are mainly focused on simple configurations. This paper aims to extend the state of the art by introducing novel analytical models capable to assess the performance of a tier-to-tier, multi-shuttle AVS/RS feeding a deep-lane rack. The proposed approach enables to evaluate the expected cycle time and throughput by (i) enabling the possibility to consider the real criteria adopted to store and retrieve items, and (ii) taking into account the ability of the vehicles to simultaneously perform different tasks. The model is validated against simulations performed on different rack layouts, on AVS/RS with different fleet compositions, for different types of cycle. The developed model aims to support both the design and the deployment phases of AVS/RS by enabling quick and accurate performance estimation in a wide variety of scenarios.

An analytical model to estimate AVS/RS energy consumption

Automation systems to support warehouse activities are a topic largely debated. Critical decisions on the technology to be adopted and the basic system attributes must be taken in the design phase; to support designers, several methodologies have been defined to evaluate performance indicators such as throughput, cycle time and equipment utilization. Conversely, smaller attention has been paid so far in the estimation of the energy necessary to perform the scheduled activities. Nonetheless, this topic is gaining popularity as the impact of renewable energy sources is increasing and the adoption of complex energy grids is gaining popularity. This paper is aimed at proposing an analytical model to evaluate the energy consumption of Autonomous Vehicle Storage and Retrieval Systems (AVS/RS), which exhibit higher flexibility and lower energy consumption compared to the traditional AS/RS. The proposed model is able to estimate the average energy consumption by accounting for realistic criteria adopted to store and retrieve unit loads. The proposed approach is validated by comparing the analytical estimation to the output of a simulation model.

Aisle changing shuttle carriers in autonomous vehicle storage and retrieval systems

The objective of this study is to propose analytical travel time models for aisle changing shuttle carriers, which are capable of travelling in the horizontal and in the cross-aisle directions. The expressions for the single and dual command travel times have been determined assuming uniform distributed storage locations and the probability theory. A simulation model has been applied for the performance analysis of the proposed analytical models. The proposed models enable the calculation of the expected travel times for single and dual command cycles of the aisle changing shuttle carriers, from which the performance can be evaluated.

Flexible reconfiguration of AVS/RS operations for improved integration with manufacturing processes

The improvements in connectivity and data availability enable to fully integrate all the components of a production system. Manufacturing processes are frequently reconfigured over time, due to changes in lot sizes, process parameters and product customization. Despite this, warehousing operations are often disregarded: usually, automation systems for warehouses are set-up during the installation and their management is hardly ever reviewed. As a consequence, the manufacturing process is adapted to the capabilities of the warehousing system, rather than the other way round. To overcome this issue, this paper aims to propose a method capable to support an easy reconfiguration of warehousing operations based on the current state of the manufacturing process. The method is applied to an Autonomous Vehicle Storage and Retrieval Systems (AVS/RS), one of the most recent and promising automation technologies for warehouses. The proposed approach is based on both discrete-event simulation and analytical techniques and is applied to a real case of an Italian company.

Analytical models for the evaluation of deep-lane autonomous vehicle storage and retrieval system performance

In the last decades, huge efforts have been made to develop innovative solutions supporting the automation of logistics activities. Among the available technologies, autonomous vehicle storage and retrieval systems (AVS/RS) are very promising: they use light vehicles able to independently travel on different axes, and thus perform different tasks at the same time. Despite the increasing diffusion of such systems, there is still a lack of performance evaluation techniques. Furthermore, there are no standards available to provide performance evaluation criteria. In this paper, novel analytical models to assess the performance of AVS/RS pertaining to deep-lane racks. These models extend the state of the art by (i) taking into account the real criteria related to the storage and retrieval of items, (ii) considering the capability of the shuttle and the satellite to simultaneously perform different tasks, and (iii) evaluating the standard deviation of the cycle time, beside its average value. The presented models are validated through simulations performed on different warehouse layouts, in different deployment scenarios. The ultimate aim of such models is to support warehouse equipment designers in evaluating the performance of their systems, considering a multiplicity of realistic scenarios.

Design of AVS/RS under group constraint

Distribution centers, which are essential to our society, are becoming increasingly important. Although the efficiency of an AVS/RS (Autonomous vehicle storage and retrieval system) makes it a promising system, there is still much to be studied regarding its effective use and design. This paper proposes a method of designing AVS/RSs through a quantitative consideration of group constraint by utilizing probability theory. The proposed algorithm also deals with the problem of designing the sizes of several buffers, and considers the effect of load fluctuations, which are important for real-world operation, by use of queuing network theory. Effectiveness of the proposed algorithm is shown with simulations of various number of groups, those of various safety factors for fluctuations, and those of various evasion rates for buffer overflow. The design results are shown to be much different from those which do not consider those factors. The results prove that the proposed method provides an accurate quantitative assessment of the AVS/RS’s constraints.

A discrete-time queueing network approach to performance evaluation of autonomous vehicle storage and retrieval systems

In this paper, we present a method for performance evaluation of autonomous vehicle storage and retrieval systems (AVS/RSs) with tier-captive single-aisle vehicles. A discrete-time open queueing network approach is applied. The data obtained from the evaluation of the lift and vehicle movements can be used directly as input for the general discrete service time distributions of the queueing network. Furthermore, the approach allows for the computation of the retrieval transaction time distribution as well as of the distribution of the number of transactions waiting to be stored. Consequently, not only expected values and variances but also quantiles of the performance measures can be obtained. Comparison to discrete-event simulation quantifies approximation errors resulting from the decomposition approach in the discrete-time domain. Moreover, the errors obtained by the discrete-time approach are compared to the errors obtained using a continuous-time open queueing network approach. Finally, it will be outlined how the model can be used for designing AVS/RSs according to given system requirements, such as storage capacity, throughput, height and length of the system as well as the 95% quantile of the retrieval transaction time.

Modeling parallel movement of lifts and vehicles in tier-captive vehicle-based warehousing systems

This paper models and analyzes tier-captive autonomous vehicle storage and retrieval systems. While previous models assume sequential commissioning of the lift and vehicles, we propose a parallel processing policy for the system, under which an arrival transaction can request the lift and the vehicle simultaneously. To investigate the performance of this policy, we formulate a fork-join queueing network in which an arrival transaction will be split into a horizontal movement task served by the vehicle and a vertical movement task served by the lift. We develop an approximation method based on decomposition of the fork-join queueing network to estimate the system performance. We build simulation models to validate the effectiveness of analytical models. The results show that the fork-join queueing network is accurate in estimating the system performance under the parallel processing policy. Numerical experiments and a real case are carried out to compare the system response time of retrieval transactions under parallel and sequential processing policies. The results show that, in systems with less than 10 tiers, the parallel processing policy outperforms the sequential processing policy by at least 5.51 percent. The advantage of parallel processing policy is decreasing with the rack height and the aisle length. In systems with more than 10 tiers and a length to height ratio larger than 7, we can find a critical retrieval transaction arrival rate, below which the parallel processing policy outperforms the sequential processing policy.

Travel time models for deep-lane unit-load autonomous vehicle storage and retrieval system (AVS/RS)

Autonomous vehicle storage and retrieval systems use vehicles that move horizontally along rails within the storage racks, while vertical movements are provided by lifts. The solution proposed in this paper addresses a particular system configuration that works with multiple deep storage lanes that are widely used in the food and beverage industry, characterised by large volumes of products of limited variety. The generic deep lane is single item, i.e. one stock keeping unit, and single batch, i.e. one production lot, thereby affecting the performance of the system in terms of storage capacity utilisation and throughput. Determining the number and depth of the lanes is crucial to aid the design and control of such a storage system. The aim of this paper was to support the design of AVS/RSs though a set of original analytic models for the determination of the travelled distance and time for single-command and dual-command cycles given alternative layout configurations. The models are validated by simulation and exemplified with a real-warehousing case study. The paper presents useful guidelines for the configuration of the system layout including the determination of the optimal shape ratio and the length of the lanes.

Incorporating the environmental dimension in the assessment of automated warehouses

In today’s competitive context, the paradigm of sustainable development is becoming more and more significant, also in warehousing. Managers are progressively considering not only purely economic aspects but also environmental concerns. Despite such consciousness, the selection of automated warehousing solutions has been mainly based on operational and economic performance in both practice and theory so far, whereas energy consumption and environmental performance have not been adequately taken into account. To fill this lack, a model is proposed to evaluate the energy consumption and environmental impact of automated warehousing solutions. The model has been used to investigate whether and how the selection of automated solutions changes depending on the dimensions involved in the analysis (i.e. only economic, only environmental or both). The analysis has been performed considering autonomous vehicle storage and retrieval systems (AVS/RSs) and its natural alternative, i.e. automated storage and retrieval systems (AS/RSs). Results confirm the importance of considering both dimensions in the assessment of automated warehouses, as depending on the scenario, the technology selection shifts from AS/RS to AVS/RS when considering not only the economic but also the environmental impact. Additionally, this study provides new insights on the suitability areas of AVS/RSs.

The AVS/RS Scheduling Optimization Based on Improved AFSA

This paper addresses the problem of the autonomous vehicle storage and retrieval system (AVS/RS) scheduling optimization. AVS/RS relies on rail guide vehicle (RGV) to provide horizontal movement within a tier and uses lifts to provide vertical movement between tiers. Firstly, the process of RGVs’ compound operation is analyzed, and the corresponding mathematical model is established. Then, an improved artificial fish swarm algorithm (IAFSA) is proposed to solve the model. According to the characteristics of the storage and retrieval operation in the system, an encoding and decoding method is designed, which contains RGV task allocation and elevator selection information. The tabu list and the optimal strategy are introduced into this algorithm, coupled with memory action and communication action to avoid the algorithm to trap in local optimal solution. Meanwhile, the adaptive step and visual are used to increase the late convergence of this algorithm. Finally, simulations based on the concrete living example of AVS/RS in a provincial verification center are given.The results obtained by the proposed algorithm are compared with another two optimization algorithm. Analysis shows that the proposed algorithm has the characteristics of fast convergence and the best solution, so as to improve the practicality and robustness of the algorithm.

Modeling and evaluating the AVS/RS with tier-to-tier vehicles using a semi-open queueing network

The Autonomous Vehicle Storage and Retrieval System (AVS/RS) with tier-to-tier vehicles is modeled as a semi-open queueing network (SOQN). Different storage/retrieval requests in the AVS/RS are modeled as different classes of customers in the SOQN model. Analyzing multiple configurations of an SOQN via computer simulation is time-consuming. Therefore, this article uses some analytical methods to evaluate the performances of a multi-class, multi-stage SOQN with general service time and interarrival time distributions. Two synchronization policies are also compared and the results show that an AVS/RS with virtual synchronization has a better performance than that with physical synchronization.

Matrix-geometric solution for semi-open queuing network model of autonomous vehicle storage and retrieval system

In this paper, we model the autonomous vehicle storage and retrieval system (AVS/RS) as a semi-open queuing network (SOQN) and apply a matrix-geometric method (MGM) for analyzing it. An AVS/RS is an automated material handling system for the high-rise pallet storage area of a warehouse and allows pallets to be stored and retrieved quickly and efficiently from their storage locations. It is an alternative to the traditional crane-based AS/RS (automated storage and retrieval system). A combination of lifts and autonomous vehicles store pallets into and retrieve them out of their respective rack storage locations. The crane based AS/RS typically utilizes aisle-captive, mast-mounted cranes that can access any storage location in an aisle via horizontal movement of the mast and vertical movement of the crane on the mast.In an SOQN, it is assumed that an arriving job or customer is paired with another device and the two visit all the stations that must process the job in the appropriate sequence. After all operations are completed on the job, it exits the system, but the device returns back to a device pool and awaits the next customer. Sometimes a job may have to wait for a device to arrive at the pool or a device may have to wait for a job to arrive. Although closed queuing networks (CQNs) and open queuing networks (OQNs) model systems that require pairing of an incoming job with a device, unlike the SOQN, they ignore the time that a device waits for a job or the time that a job waits for a device.In the context of an AVS/RS, the jobs correspond to storage/retrieval (S/R) transaction requests and the autonomous vehicles (AVs) correspond to the devices. Because an AV may sometimes have to wait for an S/R transaction or vice versa, we model the AVS/RS as an SOQN. We build the queuing network by deriving general travel times of pre-defined servers. We model the AVS/RS system as a single-class, multiple-server, SOQN. Then, we solve the network using the MGM and obtain its key performance measures. We apply the MGM technique for solving the SOQN model to a warehouse in France that uses AVS/RS.