Balancing Picking and Outbound Loading in Forklift-Operated Storage and Retrieval Systems

The Rise of Shuttle-Based Storage and Retrieval Systems

Warehouses are essential elements of almost every Supply Chain (SC) and have a significant impact on its performance. However, existing research on warehouse operations mainly aims at maximizing operational performance, neglecting their effect on downstream nodes. Shuttle-Based Storage and Retrieval Systems (SBS/RS) are an increasingly adopted type of Automated Storage and Retrieval System (AS/RS) that can provide very high throughput to the outbound loading area due to the simultaneous usage of multiple shuttles.

In this context, logistics hubs play a central role in efficiently coordinating material flows within the SC. Indeed, they are increasingly utilized to serve customers directly or via warehouses across an extensive geographical area. Logistics hubs serve both multiple customers and other smaller warehouses characterized by minor throughput that usually rely on human operators due to the economic convenience compared to automation, particularly in contexts with lower volumes. However, in such smaller and manual warehouses, the material handling activities are more time-consuming, more prone to human errors or inefficiencies, and therefore require higher operational costs.

Consequently, logistics hubs are called to provide a cost-effective picking that ensures timely delivery to customers, as well as streamlining deliveries to manual warehouses to support human operators in the receiving and stocking processes. Indeed, optimizing the outbound loading, i.e., the process of loading items onto transportation vehicles, is recognized to reduce the overall costs in the SC.

The efficiency of outbound loading depends on the sequence in which items arrive at the outbound loading area, namely the shipping area. Such a sequence is, in turn, determined by several factors, including the scheduling of picking operations and the warehouse technology. Systems with a high degree of parallelization of picking activities, such as the increasingly adopted SBS/RS, are indeed known to require well-structured scheduling of their concurrent activities. They have a heightened risk of encountering an inefficient sequence of Unit Loads (ULs) at the outbound loading area, as shown in Figure 1.

To face this issue, fully-automated sortation systems are commonly employed to effectively separate and/or merge items directed towards trucks. However, these systems may not be able to avoid bottlenecks at the shipping area when there is a very large number of ULs to be handled. Furthermore, the implementation of a large sortation system entails a significant capital investment in the loop conveyor infrastructure and requires a considerable allocation of physical space.

Given the lack of empirical evidence on this topic and with the high complexity of such a system, this study is based on a Digital Twin (DT) developed in a real case study to balance picking and outbound loading efficiency in an SBS/RS. The DT is leveraged to define and test different picking rules and evaluate their impact on both picking and outbound efficiency, with the aim of supporting the warehouse manager on how best to balance these performances when a decision is required.

Picking and Outbound Loading Efficiency in SBS/RS

High-throughput warehouses commonly rely on Automated Storage and Retrieval Systems (AS/RS) to efficiently conduct material handling activities. Among them, Shuttle-Based Storage and Retrieval Systems (SBS/RS) represent a sub-type of Automated Vehicle Storage and Retrieval Systems (AVS/RS) in which every tier has its dedicated shuttle, thus implying higher throughput and investment costs compared to the tier-to-tier solution.

Recent reviews have highlighted that the most debated research stream in SBS/RS literature regards the design, evaluation, and optimization of the picking process, i.e., studying the efficiency of automated warehouses in terms of local performance such as cycle time and/or energy consumption. However, the literature overlooks warehouse operations related to shipping, such as outbound loading, while keeping focused on picking and stocking.

In traditional warehouse configurations, outbound loading takes as input the exact sequence in which ULs are retrieved by human or robotic pickers. Thus, the sequencing of operations determines the sequence of outbound loading. Several studies have addressed this issue, optimizing the sequence of retrieval orders to minimize the overall cycle time. However, these works do not consider the impact of the upstream picking process on the efficiency of the outbound loading.

Modern warehouse configurations typically include both an automated warehouse and a sortation system. In this context, research has investigated the synchronization between picking and sorting activities, highlighting the importance of integrating these two processes to minimize the overall sum of time and cost of operations. However, these studies lack considerations on the performance of outbound loading and/or details on the picking (i.e., what, how much, where and when ULs are picked from).

Furthermore, their analyses focused on traditional warehouse typologies, specifically manual and AS/RS, thus they may not be directly applicable to modern SBS/RS characterized by a high degree of concurrent operations. In such contexts, the way ULs are retrieved exerts a significant impact on the subsequent material flow at the sorting stage.

To fill this gap, this research leverages a Digital Twin (DT) to define effective picking strategies that dynamically balance picking and outbound loading efficiency in an SBS/RS. The developed DT is able to virtually represent warehouse operations, estimate performance, test strategies in acceptable times, and potentially communicate recommended actions to the real system actuator while being fed with real data.

Methodology: A Digital Twin Approach

The methodology proposed in this work relies upon the adoption of a Discrete Event Simulation (DES)-based Digital Twin (DT) due to the complexity and stochastic nature of the real warehouse investigated in this research. Indeed, it can capture non-stationary variations and accurately estimate travel time in concurrent storage/retrieval tasks, which relies on the stochastic order of stocking and picking operations, as well as how they are assigned to the vehicles.

The design and implementation of the DT has gone through the following phases:

1. Research Setting: To properly define the research setting, the first phase involved analyzing the logistics hub context, checking the suitability of the SBS/RS for the research questions, identifying all elements required in the DT, and understanding the unique characteristics of the material handling processes carried out in the warehouse.

2. DT Building: In the second phase, real data related to warehouse geometry, machine kinematics, storage and retrieval process design, and orders were collected to build the DT structure. Stocking and picking processes were modeled, establishing the relationships among customers (i.e., ULs) and servers (e.g., shuttles, lifts, buffers, etc.), and distinguishing between sequential and non-sequential picking orders.

3. DT Validation: The DT was validated using real data from the company, ensuring the results produced by the digital model were reliable before proceeding with further experiments. The refinement of the DT was carried out until the gap between simulated and real performance was acceptable, with an average cycle time error of ≤ 5%.

4. Strategies Design: The DT was used to define effective picking strategies to dynamically balance picking and outbound loading efficiency. In doing so, different workloads and application easiness were considered, rejecting solutions that would require a significant amount of time to implement.

5. Use Phase: DT was tested in a controlled environment to assess the effectiveness of the selected strategies during the real company operations. Specifically, the DT was fed with real data and launched at the end of every working day to compare the recommended picking rule for the next day with the current one chosen by the company. Additional insights regarding different workloads were also derived to better contextualize the choice of the picking rule.

The DT is based on the object-oriented programming paradigm, which simplifies the building and debugging of the entire digital model. The final choice was to adopt MATLAB® due to its high flexibility and user-friendly interface. The DT is structured into 6 modules, each with specific functions, ranging from reading geometric and kinematic parameters to running the DES simulation and writing the output.

The validation phase led to achieve an average cycle time error less than 5% and meeting daily throughput, thus the developed DT is considered a reliable copy of the real-world system.

Case Study: Balancing Picking and Outbound Loading in a Logistics Hub

This research examines the SBS/RS used in the logistics hub of a leader distributor company in the tire industry. This sector is suitable with respect to the identified research questions since tires may arrive at the sorting area in an unordered sequence, thus affecting outbound loading efficiency. Indeed, to optimize space and volume, tires are frequently stored in stacked configurations and during the picking process, the ULs change their dimensionality from a set of tire stacks to single tires.

The examined SBS/RS has 6 aisles, each one with 6 tiers, each containing up to 45 channels. The overall number of lifts and shuttles is respectively 6 and 36. The UL, both stocking and picking, is defined as a group of tires named “train”. Each train consists of 1 up to 3 stacks of tires, and each stack can contain up to 5 tires. Consequently, a train can contain from 1 to 15 tires.

There are two types of picking ULs in this setting: sequential and non-sequential. Sequential ULs with the same ‘label-shipment’ pair are desired to be loaded onto the truck one after the other. Non-sequential ULs have no label.

The picking process of a non-sequential UL begins with the command to the shuttle to reach the channel of the desired train. Then the satellite grabs it, one stack at a time, and loads it onto the shuttle. After that, the shuttle reaches the outbound buffer and releases the train. From there, the train is moved onto the lift, which reaches the outbound conveyor belt tier and releases the train. Each stack of the train goes through the unstacker machine and gets unstacked, with each single tire reaching the outbound conveyor belt to the sorting system.

For a sequential UL the process is the same except for the peculiar control logic that enables the UL to move from the outbound buffer to the lift. A sequential UL is allowed to proceed if it belongs to the same ‘label-shipment’ pair in progress or if there are no ‘in progress’ ULs for that shipment.

Balancing Picking and Outbound Loading Efficiency through Digital Twin

The developed DT is employed to define and assess the effects of three picking rules on both picking and outbound loading efficiency across various workloads. These picking rules were defined based on the choice of the completion point, i.e., the point in the picking process where a sequential UL is considered as completed:

1. CP1: Arrival on Outbound Buffer 2 (OB2)
2. CP2: Arrival onto the outbound conveyor belt
3. CP3: Arrival at the sorting area

The workloads were defined by the number of sequential picking orders, ranging from 0 to 100% of the total picking demand.

The experiments conducted on the DT allowed us to observe that as the number of sequential ULs increases, both cycle time and overlap also increase due to the more complex scheduling of picking orders. However, how much they increase varies depending on the choice of the completion point:

• Low Sequential ULs (0-10%): Both cycle time and overlap are low, regardless of the completion point.
• Medium Sequential ULs (20-30%): Cycle time and overlaps grow differently according to their completion point: the earlier it is (CP1), the lower is the cycle time and the higher is the overlap, and vice-versa.
• High Sequential ULs (>30%): The impact of completion point on cycle time and overlap becomes even more pronounced.

The DT also allowed to quantify the impact of each completion point on cycle time and overlap, as shown in Table 3. This performance was then converted into costs to allow managers to fairly compare the choice of the completion point, according to their preferences on cycle time and overlap.

For the specific current scenario of the company, with 30% of sequential ULs and equal preferences on cycle time and overlap, the DT verified that the current completion point CP1 is the best choice. However, the DT also showed that if the manager prefers overlap over cycle time, then the optimal choice would be CP3, while if the preference is the opposite, then CP2 should be selected.

Insights and Implications

This research provides several managerial insights and practical guidance:

1. Balancing Picking and Outbound Loading Efficiency: The developed DT can support warehouse managers in dynamically choosing the most suitable picking strategy to balance picking cycle time and outbound loading efficiency, based on the current workload and their preferences. This is particularly relevant for logistics hubs serving both direct customers and smaller manual warehouses, where the timeliness of delivery and the correct sequence of ULs at the sorting area are both crucial.

2. Optimizing Downstream Nodes’ Operations: By ensuring an optimal sequence of ULs at the sorting area, the logistics hub can improve the productivity and reduce the operational costs of subsequent nodes in the supply chain, such as the receiving and stocking processes in smaller manual warehouses.

3. Sizing the Sorting System: The number of overlaps approximates the workload at the sorting area and shipping location, helping in the design stage of the sorting system, particularly to size the capacity of the sorter and to optimize its layout.

4. Managing the Workload of Truck Operators: Knowing the expected overlap can also be used to manage the workload of truck operators, preventing increased errors and stress due to the augmented workload.

5. Insights for Technology Suppliers: By understanding the expected cycle time and overlap, suppliers can optimize warehouse design to meet the throughput and outbound loading efficiency demands of their customers more effectively.

Additionally, while this study is tailored to the mentioned company, the presented approach can be applicable to other warehouses leveraging on large and highly parallelized AS/RS to retrieve ULs, adopting different picking process policies, and employing a closed-loop sorting system for accurate dispatching. This includes systems such as SBS/RS with conveyor belts, particularly in contexts with high volumes and diversity of customers, such as e-commerce, food and beverage, and paper tissue industries.

Conclusion and Future Research

This paper introduced a novel approach using Digital Twin to support managers in the balancing of picking and outbound loading efficiency in an SBS/RS. The proposed methodology was applied to a real case-study of a logistics hub, whose picking process differs according to the next node typology.

The key contributions of this research are:

1. Quantitatively measuring the impact of different picking rules and workloads on both picking and outbound loading efficiency in an SBS/RS.
2. Providing warehouse managers with a practical tool to properly balance and control these performances.

The findings demonstrate a trade-off relationship between picking and outbound loading efficiency when different picking rules are applied. Moreover, the developed Digital Twin shows promising results in supporting operational decisions by managers.

Future research directions include using the developed DT for an extended period and including different demand loads, as well as considering the receiving and stocking operations of subsequent nodes in the supply chain to more precisely quantify the benefits. Additionally, further studies in different contexts are encouraged to confirm the validity of the DT as a supporting tool for balancing cycle time and overlap, and to characterize the relationship between picking and outbound loading efficiency in SBS/RS.