The increasing demands of e-commerce and challenges in modern warehousing have emphasized the need for advanced robotic solutions in intralogistics, particularly for order picking tasks. This study introduces a novel approach to integrating robotic bin -picking systems with adaptive detection times and multi-gripper coordination, aiming to enhance the efficiency of automated order preparation processes. Through controlled laboratory experiments with an UR5e robotic manipulator and three distinct robotic grippers (two-finger, soft, and vacuum) empirical data on detection times and success rates were collected under varying conditions. This data was utilized to generate a synthetic dataset that simulated realistic operational scenarios involving conveyor-based transportation of SKUs from automated storage and retrieval systems (AS/RS) and separate conveyors for order totes. A simulation model, developed in AnyLogic, evaluated two operational strategies. minimizing detection times versus minimizing gripper exchanges. Results indicate that while the scenario minimizing gripper exchanges reduced the total number of gripper swaps by 26.5%, the improvement in average cycle time was modest, with a 2.9% reduction. These findings highlight the marginal time difference between the two strategies, suggesting that system priorities such as mechanical wear or operational robustness could guide strategy selection. Copyright (c) 2025 The Authors.

Thanks to rapid technological developments in robotics, various automation technologies are being applied in warehouses today. Order picking, as a key process in warehouse operations, has drawn attention in academia and practice for decades. In addition to many studies dedicated to manual and fully automated order picking, efforts have also been made to study semi-automated warehouses in which humans and robots collaborate. However, these studies mostly focused on system efficiency and ignored ergonomic aspects. Order picking was confirmed as a labor-intensive process in an environment in which workers are at a high risk of developing health problems. Therefore, this study addresses the investigation of physical human working conditions in both manual and robot-assisted order picking systems via real-life laboratory experiments and simulation modeling. We used a motion capture system to assess human working postures when working with and without robot assistance. In addition, we estimated the daily workload by applying the energy expenditure concept. Using simulation experiments, we were able to extend the results to various practical scenarios with different design variables, for example warehouse layouts, order sizes, and human-robot team configuration. Our preliminary results reveal that human-robot collaboration can reduce human workload. Posture evaluation also shows a slight improvement. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Technological developments in the global supply chain have changed processes in warehousing. This reflects in short response time in handling the orders, which has a consequence on high automation degree in warehousing. An important part of automated warehouses is presented by shuttle-based storage and retrieval systems (SBS/RS), which are used in practice when demand for the throughput capacity is high. In this paper, analytical travel time model for the computation of cycle times for double-deep SBS/RS is presented. The advantage of the double-deep SBS/RS is that fewer aisles are needed, which results in a more efficient use of floor space. The proposed model considers the real operating characteristics of the elevators lifting table and the shuttle carrier with the condition of rearranging blocking totes to the nearest free storage location during the retrieval process of the shuttle carrier. Assuming uniform distributed storage locations and the probability theory, the expressions for the single and dual-command cycle of the elevators lifting table and the shuttle carrier have been determined. The proposed model enables the calculation of the expected cycle time for single- and dual-command cycles, from which the performance of the double-deep SBS/RS can be evaluated. The analysis show that regarding examined type of the double-deep SBS/RS, the results of the proposed analytical travel time model demonstrate good performances for evaluating double-deep SBS/RS.

To meet the rising demands of global trade and e-commerce, efficient warehousing relies on integrated and cooperative material handling systems. This paper investigates the extension of Vertical Lift Module (VLM) storage capability with a Buffer System and assesses the impact of this integration on performance. We developed an analytical model to calculate the expected dual command cycles, forming the basis for evaluating the VLM - Buffer integrated storage system's performance. Our research emphasises minimising unnecessary swaps between the VLM and the Buffer System to enhance throughput performance. We introduce the Look Ahead Strategy (LAS) to minimise inter-system swaps and develop a Binary Integer Program (BIP) to benchmark its performance. The results indicate that LAS performs on par with BIP, due to its ability to consider product popularity during the final selection of the outbound swapping tote. Through a comprehensive analysis of the analytical model with an empirical correction, utilising Pareto-based order sequences, the results show deviations of less than 1% on average, affirming the analytical model's accuracy. Our research provides insights on using the VLM-Buffer integrated storage system, emphasising efficient tote swapping policies like LAS for enhanced warehouse operations, and allows managers to assess system performance through scenario-based analyses.

As industry trend continues to accelerate sellers to begin transitioning the sale of their products exclusively through e-commerce platforms, companies must remain vigilant and recognize the requirement for their products to be safely stored and quickly retrieved. This research presents a comprehensive model and simulation study of a Vertical Lift Module (VLM) with an integrated shuttle-based storage and retrieval system (SBS/RS) or buffer system. This work evaluates a proposed solution to the ever-increasing emergent storage and retrieval challenges faced by warehouses worldwide. The VLM system was modeled using AnyLogic software to evaluate system capacity, travel distance, velocity profiles, and other user-defined operational constraints. The VLM performance is modeled under various conditions and compared to the performance of a traditional stand-alone VLM in terms of throughput and cycle-time to identify potential VLM-Buffer system integration drawbacks or limitations.

This paper studies a multiple-deep automated vehicles storage and retrieval system (AVS/RS) rack following a Depth-First storage and a Depth-First relocation strategy. We propose an analytical model based on a novel approach that utilises the Markov chain stochastic steady-state model. To verify the analytical model, a numerical simulation is developed. We also derive an empirical model using first- and second-order polynomial functions that are accurately fitted with regression equations and examined with MAPE and RMSE prediction accuracy measurements from a large-scale simulation study. The empirical model enables a straightforward calculation of the expected number of location movements of shuttle carriers and the attached satellite vehicles from which the AVS/RS throughput performance can be calculated. We present threefold and sixfold deep AVS/RS case study scenarios with an equal number of storage locations and estimate the cycle times. The evaluation of the case study results reveals that the analytical and empirical models achieve less than 2% error in the case of a dual command cycle time prediction compared to the simulation results. This proves that our approach allows an accurate estimation of multiple-depth AVS/RS throughput performance.

Warehouses and distribution centres are based on integrated material handling solutions. Most of the research papers in intralogistics are only concerned with one system to analyse and optimise the addressed system's performance, such as maximum throughput performance, minimum total cost, maximum energy efficiency, etc. This paper presents a concept to justify the proposed VLM and SBS/RS integrated system and a binary integer programming (BIP) model that minimises totes exchange between the two storage systems. The results show that the minimal number of exchanges between VLM and SBS/RS was achieved in a case study with a longer order list (T = 1600) and a highly dependent SKU's Pareto relationship (2050). Copyright (C) 2022 The Authors.

This paper studies multiple-deep automated vehicle storage and retrieval systems (AVS/RSs) known for their high throughput performance and flexibility. Compared to a single-deep system, multiple-deep AVS/RS has a better space area utilisation. However, a relocation cycle occurs, reducing the throughput performance whenever another stock keeping unit (SKU) blocks a retrieving SKU. The SKU retrieval sequence is undetermined, meaning that the arrangement is unknown, and all SKUs have an equal probability of retrieval. In addition to the shuttle carrier, a satellite vehicle is attached to the shuttle carrier and is used to access storage locations in multiple depths. A discrete event simulation of multiple-deep AVS/RS with a tier captive shuttle carrier was developed. We focused on the dual-command cycle time assessment of nine different storage and relocation assignment strategy combinations in the simulation model. The results of a simulation study for (i) random, (ii) depth-first and (iii) nearest neighbour storage and relocation assignment strategy combinations are examined and benchmarked for five different AVS/RS case study configurations with the same number of storage locations. The results display that the fivefold- and sixfold-deep AVS/RSs outperform systems with fewer depths by utilising depth-first storage and nearest neighbour relocation assignment strategies.

The market provides increasingly diverse products. Additionally, it is expected from the retailers to deliver goods in progressively shorter periods. Both actualities influence the merchandise businesses to desire a fast delivery system with high throughput capacity. Efficient intralogistics operations, including well-organized warehouses, contribute a major part to achieving success in retail market. One of the possible solutions to the stated problem is an automated storage and retrieval system (AS/RS). Shuttle-based storage and retrieval system is a special version of AS/RS where order picking is accomplished in each tier by a shuttle vehicle. The shuttle delivers totes to the lift, which acts as I/O point of the automated warehouse system. While the needs of companies differ from one another and the boundaries are predefined with a given layout, there are numerous different warehouse configurations to consider. To obtain a vast majority and analyse their performances, a discrete-event simulation of an SBS/RS was developed. In this paper we present the concepts of an SBS/RS and how they were implemented to a discrete-event simulation, focused on the storage single command cycle (SCC). Furthermore, this paper presents a study case upon which additional analysis was made, by changing kinematic parameters and fill grade factor of the warehouse.

A steel plant company producing steel bars has large assortment of the end products, with similar appearance and attributes. The steel bars are stored on the floor in a stacking frame. For the order picking of steel bars, an overhead crane is used for reshuffling all the necessary steel bars to get access to the required product. While the production schedule allows for anticipating the storage occupancy, a stochastic transport arrival prevents optimal product stacking for efficient order-picking operation. Due to this, any order-picking sequence may require reshuffling of the stacked material, which increases working cost, order-picking times, and complicates material tracking. This paper presents a method for minimizing the order-picking times by overhead crane movements through proper reshuffling of the steel bars. Similar research was done on container yard pre-marshalling and reshuffling problem, while the presented approach handles with the special situation in the steel plant. Various optimization approaches including linear programming, simulated annealing, taboo search, branch and bound and genetic algorithms were used by researchers to solve comparable problems. The proposed method for solving the specific problem of reshuffling steel bars uses genetic algorithms to find a feasible solution in real-time. The proposed solution reduces intralogistics cost and increases order-picking efficiency.

Production environments worldwide transform themselves in order to take the best advantage of the Industry 4.0 guidelines. Automation, data exchange, cyber-physical systems, the IoT, cloud and cognitive computing represent a step in the unknown to these companies, associated with high risks and also the need to restructure their culture. If the execution route is not clearly defined and understandable to all levels of employees, the renovation is too long. The maturity models can be used for the assessment of current Industry 4.0 maturity level, but the practical use of scores and assessed level often requires the involvement of consulting firms. Companies can avoid the involvement of consulting companies with the use of complementary tools. In this paper, we propose a new methodology that combines the Industry 4.0 maturity model and discrete-event simulation tools in the case of steel production company with the possibility of generalization. The combination of these tools in the first step helps the company to assess its current level of maturity for Industry 4.0, and in the second step helps to consider about strengths and weaknesses of possible scenarios for transition to a higher level of maturity.

Our research objective is to lower intralogistics costs by minimizing the number of shuffling operations in a steel plant company commercial warehouse. The process of dispatching products consists of retrieving set of steel bar (SSB) from a floor stored stack or a special stacking frame by an overhead crane. To retrieve a targeted merchandise all SSB above targeted must be reshuffled. Proper assignment of storage locations is a key logistics problem for efficient order picking. We are comparing two heuristics, that do not require information of dispatching sequence of any stored products. We simulated the problem at hand with both methods. Our objective is to count the number of reshuffles using each heuristic on randomly generated examples and decide which is better in the long run. Our problem has similarities with storage assignment of steel plates or steel coils for minimization of reshuffling operations. The problem is also comparable to storage assignment of containers in a container yard. In our case we are dealing with a special stacking configuration of products, that demands different approach. We want to demonstrate which heuristic should be used in companies that lack necessary storage information infrastructure.