Robotic Mobile Fulfillment System Research Articles

An efficient multi-agent approach to order picking and robot scheduling in a robotic mobile fulfillment system

This paper presents a Multi-Agent System (MAS) for optimizing the scheduling and routing of mobile robots and human pickers in a Robotic Mobile Fulfillment System (RMFS). The RMFS is a system designed for e-commerce warehousing where autonomous mobile robots are used to fetch inventory pods, also referred to as racks, from the storage area and transport them to the appropriate picking station where human pickers pick the required number of goods. The system requires the solution of several hard decision problems like: the order-to-picking station assignment and sequencing, the pod selection, and the multi-robot task allocation. The proposed solution approach employs decentralized scheduling mechanisms, i.e. auctions and dispatching rules, to communicate and distribute picking and retrieval tasks among the agents. Various dispatching rules are identified and analyzed over a wide set of problem instances of the RMFS with varying numbers of mobile robots, picking stations, and order sizes. The proposed MAS framework shows promising results and requires only a fraction of the computation time compared to a centralized scheduling algorithm. The MAS also includes both unidirectional and bidirectional lanes. Although additional complexity in collision avoidance is introduced when using bidirectional lanes, it allows for better system performance.

A column generation driven heuristic for order-scheduling and rack-sequencing in robotic mobile fulfillment systems

Optimizing the customer-order picking process in robotic mobile fulfillment systems depends on two closely related decisions: How to schedule the processing of orders on a workbench of limited size and how to sequence the racks that are lifted and transported by automated guided vehicles (robots) to the picking-station to supply the requested items for order-processing? Minimizing the number of these so-called rack-visits at the picking-station is well suited to reduce robot utilization and the makespan of customer-order processing. We present a heuristic solution approach for the order-scheduling and rack-sequencing problem at a single picking-station using column generation to partition the set of customer-orders into batches, while minimizing the number of rack-assignments to batches, which in turn minimizes the rack-visits. The generated batches possess a property that allows to derive an order-processing schedule straightforwardly. Then, we refine the heuristic solution by rearranging the processing of batches and their assigned racks. A comprehensive and comparative computational study demonstrates superior performance of our approach on the vast majority of instances compared to several heuristics from literature, both in terms of solution quality with up to 15% fewer rack-visits and runtime. The average worst-case optimality gap in comparison with valid lower bounds is 10.72% and the solutions are close to optimal if around ten orders can be processed simultaneously on the workbench. It is also shown that the proposed heuristic provides very competitive results for the order-scheduling and rack-sequencing problem at multiple picking-stations.

Assigning orders and pods to picking stations in a multi-level robotic mobile fulfillment system

This paper addresses the operational planning problem of assigning orders and pods (i.e., mobile shelves) to picking stations in a multi-level robotic mobile fulfillment system (RMFS), which deals with two issues: deciding on which picking station handles which order, and from which pods to pick the ordered items, considering the limited storage capacity of the pods. Due to the relatively poor space utilization of single-level RMFS warehouses, such systems are often spread over multiple floors in practice. Therefore, we explicitly consider multi-level warehouse layouts with isolated levels (or zones) where a pod can only be brought to a station if both of them are on the same level. We optimize the problem with regard to a multi-criteria objective function that consists of three workload-oriented objectives: we aim to balance the total workload among all pickers, minimize the total order-consolidation effort for the packers, and the pod movement effort for the mobile robots. After formalizing the planning problem as a multi-objective optimization problem, we provide two mixed-integer linear programming models. Additionally, we propose a matheuristic that reduces the model size to the desired granularity so that realistically sized problem instances can be solved within less than four minutes of computation time. Moreover, we derive some managerial insights, such as the impact of the number of warehouse levels and picking waves on the objective values. We find evidence that running the RMFS warehouse in a multi-level facility can substantially compromise the consolidation effort at packing stations since it leads to a higher number of split orders. Furthermore, splitting the planning horizon into multiple short waves can lead to a higher number of pod-to-station assignments and, thus, to a raised pod-movement workload for mobile robots.

A Novel Scattered Storage Policy Considering Commodity Classification and Correlation in Robotic Mobile Fulfillment Systems

The commodity storage assignment problem (CSAP), which assigns stock-keeping units (SKUs) to a suitable location for matching the customer demand patterns, is crucial for improving the order picking efficiency. In this study, we jointly consider the SKUs classification and correlation, and propose a new scattered storage policy named scattered-correlation storage policy based on the commodity classification (SCSPCC) for mitigating CSAP in the robotic mobile fulfillment systems (RMFS). We call the new problem CSAP-SCSPCC. To address this problem, we construct a mixed-integer programming model, and propose a novel variable neighborhood search with self-adaption and simulated annealing acceptance mechanisms (SA-VNSSA). Besides, a heuristic algorithm is proposed to select the minimum number of shelves to evaluate the optimization effect of SA-VNSSA and SCSPCC in terms of the number of shelf transports. Extensive numerical experiments are conducted on small-, medium-, and large-scale instances, respectively. The results reveal that the proposed model and algorithms are reasonable and effective in solving CSAP-SCSPCC compared with the state-of-the-art methods. Specifically, SA-VNSSA outperforms the three state-of-the-art comparison algorithms [i.e., SA-1 (Muppani and Adil, 2008), SA-Pop (Assadi and Bagheri, 2016), and SA-2 (Zhang et al., 2019)] by more than 4.19% and 3.23% on average in medium- and larger-scale instances, respectively. The comparisons between SCSPCC and CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019) show that the order picking efficiency is improved by our SCSPCC more than 6.31%. It is can be concluded that SCSPCC is efficient and robust to match the SKU storage pattern and customer demand patterns in e-commerce retail. Note to Practitioners—Robotic mobile fulfillment systems (RMFS) have been widely used in the warehouses of Amazon, Jingdong, Cainiao, and so on. Considering practical situations and requirements in commodity storage assignment problems (CSAP) is necessary for improving RMFS order picking efficiency. We proposed a new problem named CSAP-SCSPCC for RMFS. Particularly, SCSPCC is a novel scattered storage policy that can assign best-selling SKUs and general-selling SKUs to a suitable location based on the SKU correlation, respectively. Computational results with small-, medium-, and large-scale instances show that our proposed SA-VNSSA and SCSPCC are effective, robust, and practically applicable compared with two state-of-the-art approaches [i.e., CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019)]. Compared with CDSAP (Mirzaei et al., 2021) and DCP (Zhang et al., 2019), SCSPCC can improve order picking efficiency by more than 6.31%. In summary, the methods proposed in our work can match the SKU storage mode and the customer demand patterns in a giant e-commerce retail warehouse. This research work can contribute to the improvement of RMFS picking efficiency. In the future, it is necessary to study multiple problems in RMFS jointly, including CSAP-SCSPCC, shelves storage assignment problems, and order batching, etc.

A* guiding DQN algorithm for automated guided vehicle pathfinding problem of robotic mobile fulfillment systems

This paper proposes an A* guiding deep Q-network (AG-DQN) algorithm for solving the pathfinding problem of an automated guided vehicle (AGV) in a robotic mobile fulfillment system (RMFS), that is, a parts-to-picker storage system with numerous AGVs replacing manual labor to improve the efficiency of picking work in warehouses. The pathfinding problem in an RMFS has characteristics such as changing scenes, narrow spaces, and significant decision-making time requirements. The A* algorithm and its variants have been widely used to address this problem. In this paper, we propose a reinforcement learning algorithm for a single AGV that uses the A* algorithm to guide the DQN algorithm. This makes the training process faster and requires less decision-making time than the A* algorithm. The trained neural network in the AG-DQN algorithm requires only the layout information of the current system to guide the AGV to complete a series of randomly assigned tasks. We used the AG-DQN algorithm to control the AGV pathfinding and complete tasks at different scales and layouts of the RMFS models, including traditional rectangular layouts and certain special layouts (e.g., fishbone layouts). The results show that the AG-DQN can train the AGV to find the correct shortest path to complete all tasks in less training time than the standard DQN algorithm. In addition, the decision-making time of the AG-DQN is less than that of the A* algorithm. The AG-DQN algorithm saved 49.92% and 71.51% of the decision-making time for the small- and large-scale RMFS models, respectively. Thus, the AG-DQN algorithm offers valuable insights into AGV control in an RMFS.

An automated guided vehicle conflict-free scheduling approach considering assignment rules in a robotic mobile fulfillment system

Automated guided vehicle (AGV), as a material handling equipment, has been widely used in various applications, especially in the automated warehouse of manufacturing. The working efficiency of the warehouse is greatly influenced by the organization of the order picking process. As the picking tasks of AGVs increase in a certain period, conflicts and deadlocks of simultaneously working AGVs occur more frequently, which are also defecting factors of efficiency. In this paper, a multi-AGV scheduling approach considering assignment rules (MASA) is proposed, and a stage conflict avoidance method is designed to generate a conflict-free working route for AGVs in an automated warehouse with multiple workstations. Different from the random assignment rule of workstations in previous studies, the crowding-based assignment rule (CAR) and opening-time-based assignment rule (OTAR) are proposed to adjust the AGV working route and alleviate the congestion of AGV at the workstations. The scheduling model of AGVs is constructed to minimize the total completion time based on a space–time network, and a cooperative optimization algorithm is designed to solve the AGVs conflict-free scheduling problem. Numerical experiments verify the effectiveness and stability of the proposed algorithm. With the same working loads, CAR and OTAR result in a 5.55% and 9.56% decrease in the total completion time than the traditional random assignment rule, and the completion time is reduced by 8.01% and 12.13% in class-based tasks, respectively. MASA has great potential to improve the system efficiency of the warehouse when there are massive picking tasks and workstations.

Robots’ picking efficiency and pickers’ energy expenditure: the item storage assignment policy in robotic mobile fulfillment system

The item storage assignment problem (ISAP) in a robotic mobile fulfillment system (RMFS) is addressed in the paper. Recently, most ISAP studies have concentrated on improving the robots’ picking efficiency while ignoring the fact that RMFS is a human-robot coordinated system. In ISAP, we also need to take human factors into account. This research investigates ISAP by considering both the robots’ picking efficiency and the pickers’ energy expenditure. The bi-objective mixed-integer programming model is developed to maximize the items’ similarities in the pods and minimize the pickers’ energy expenditure. The improved knee point-driven evolution algorithm (IKnEA) is proposed to solve the model. Experimental results demonstrate that IKnEA has superior distribution and convergence speed than NSGA-II and KnEA. IKnEA’s hypervolume (HV) indicator operates better than NSGA-II and KnEA in various warehouse scales. More layers of pods contribute to less travel distance for robots but more energy expenditure for pickers. Five Pareto solutions are presented to assist the decision-makers in selecting the optimal ISAP solutions given various preferences.

Online joint optimization of pick order assignment and pick pod selection in robotic mobile fulfillment systems

Robotic mobile fulfillment systems is a large dynamic system where information such as backlog, inventory, pod location, and robot state will change with time during its operation. In this paper, we model the dynamic system for the online joint optimization of pick order assignment and pick pod selection considering multiple items and then propose a new MIP model to solve the decision-making in the key process. We design a heuristic based on alternate decision and order-driven to solve large-scale problems. We extend an open-source simulation framework to evaluate the results of proposed model and algorithm through different instances in the dynamic system. Experimental results show that the performance of the new MIP model is at least 13% higher than that of the different settings, and the performance of the designed heuristic is at least 57% higher than that of the state-of-the-art sequential method in the literature.

Dynamic Picking and Storage Optimization of Robotic Picking Systems

With the development of e-commerce and the improvement of logistics requirements, more and more ‘parts-to-picker’ picking systems begin to replace the inefficient ‘picker-to-parts’ picking systems in various scenarios. As the mainstream ‘parts-to-picker’ system, the robotic mobile fulfillment system has been attracting much attention in recent years. In addition to the customer's changing requirements, the rapid response of the picking system to the order is particularly important. In the above context, to seek a breakthrough in the picking system's picking efficiency without increasing the cost of additional equipment, the storage allocation of the pods becomes very important. This article focuses on the dynamic storage allocation of robotic mobile fulfillment system, which has positive theoretical and practical significance. By analyzing the pod storage process of the robotic mobile fulfillment system, a dynamic pod storage allocation model suitable for the robotic mobile fulfillment system is established with the goal of minimizing the pod handling distance. Two dynamic pod storage allocation strategies are proposed for the system. By simulating the picking systems of different scales, the effectiveness of the dynamic storage allocation strategy is verified, which has a certain reference to the operation of the robotic mobile fulfillment system in practice.

Rack retrieval and repositioning optimization problem in robotic mobile fulfillment systems

Robotic mobile fulfillment systems provide a new solution for e-commerce retailers to fulfill customers’ orders, wherein racks are moved by mobile robots to workstations so pickers can retrieve the purchased products. While such automated parts-to-picker systems can save on labor costs, they raise new operational challenges. In this paper, we investigate the rack storage and robot assignment to racks problem during order processing. We formulate this problem with the goal of minimizing the makespan of the system. Based on a rolling horizon framework and the simulated annealing method, we develop a matheuristic decomposition approach, which involves the solution of a special axial 3-index assignment problem in each stage to solve the problem. We test the performance of the proposed method for both large-scale cases based on a real-world dataset and small-scale instances generated synthetically. Computational results demonstrate the good performance of the proposed approach.

A Two-Stage Hybrid Heuristic Algorithm for Simultaneous Order and Rack Assignment Problems

The problem of order and rack assignment to picking stations (ORAPS) is a key joint optimization problem in the order picking process of the robotic mobile fulfillment system (RMFS). Given a set of customer orders and a set of mobile racks, the goal of this problem is to simultaneously assign orders and racks to multiple picking stations so that the racks can provide products to meet the demand of orders by the minimal number of visits to all picking stations. In this article, we build a mathematical model for the ORAPS, which considers all orders assignment with the allocable capacity interval of the picking station and the rack product capacity limitation. To solve the ORAPS problem, we propose a two-stage hybrid heuristic algorithm (TS-HHA) consisting of the reducing stage and the assigning stage. In the reducing stage, a scheme of dynamic programming (DP) is introduced to find a critical rack set, which can focus attention on the most promising racks and increase the speed of problem-solving. In the assigning stage, we propose an optimization strategy that combines a constructive heuristic algorithm and adaptive neighborhood search (CH-ANS). It can generate a high-quality simultaneous assignment scheme and further improve its quality effectively. The computational results show that our proposed algorithm performs better than its competitors on both simulation and practical instances of the ORAPS problem. Note to Practitioners—This article investigates the order and rack assignment to picking stations (ORAPS) problem in the robotic mobile fulfillment system (RMFS) originated from a renowned Chinese logistics platform. We build a more comprehensive and reasonable mathematical programming model for this ORAPS problem and propose a two-stage hybrid heuristic algorithm (TS-HHA) strategy, which can simultaneously assign orders and racks to multiple picking stations so that all customer orders can be processed. The experimental results show that our TS-HHA performs at least 35% better than its competitors on all simulation and practical instances set. We believe that this research work can serve as a kind of generic framework for practical ORAPS problems and be very helpful for operating the RMFS efficiently.

Order picking optimization in a robotic mobile fulfillment system

The Robotic Mobile Fulfillment System (RMFS) is a parts-to-picker warehouse system where robots are used to fetch inventory goods from the storage area and transport them to the workstation. To improve the overall performance of the order picking process, this paper investigates an integrated order and robot scheduling optimization problem. We formulate this problem as a mixed-integer non-linear programming (MINLP) model with an objective of minimizing the total cost, containing the transportation cost of robots, the cost of loading and unloading operation, the incentive cost, and the penalty cost. And a Variable Neighborhood Search algorithm is presented to solve the MINLP model. Numerical experiments are conducted on 30 test cases based on real-world data to validate effectiveness of the proposed model and algorithm. Results show that the formulated model contribute to cost saving, and the proposed algorithm not only solves the model within a reasonable CPU time, but also yields near-optimal solutions with about 0.67% relative gap. Moreover, the study find that the reasonable robot configuration has a significant impact on the overall system performance.

Optimization models for scheduling operations in robotic mobile fulfillment systems

In robotic mobile fulfillment systems (RMFS), mobile robots carry inventory shelves autonomously from the storage area to picking stations and back. The scheduling of these robots and the order picking activities can be modeled as a collection of interrelated optimization problems. In this paper, we focus on the following optimization problems: the order allocation to picking stations, order sequencing, the inventory pod selection, and the robot scheduling. We present new mixed-integer programming (MIP) models for these decision problems and extended on existing models from the literature. To improve the models further, we include interstation travel which means that inventory racks can be transported from one picking station straight to another station without returning it to the storage area in between, hence reducing the overall travel distance. Moreover, In previous research on RMFS, only some of these decision problems are integrated. Therefore, we developed an integrated model to study the interdependencies between the decision problems. The models are validated through simulations and different performance metrics are analyzed such as the number of pod visits, the total distance travelled, and the system makespan. Moreover, we introduce a cost metric to facilitate the objective evaluation of the system performance. From the computational experiments we conclude that the integration of the decision problems results in significantly better performing systems compared to sequentially optimizing them. However, this comes at the cost of more computational effort. Furthermore, including interstation visits can further improve the overall system performance.

Stronger mixed-integer programming-formulations for order- and rack-sequencing in robotic mobile fulfillment systems

This paper addresses the order- and rack-sequencing problem at a single picking station in the context of robotic mobile fulfillment systems, a warehouse technology typically applied in large distribution centers. Following the parts-to-picker concept, items are stored on movable racks that are lifted and transported by automated guided vehicles from the storage area to picking stations for order-processing. The order-picking process involves two linked decisions: How to sequence the processing of orders and how to sequence the rack visits to supply the picking station with the requested items. We present a novel mixed-integer linear programming formulation achieving stronger linear programming bounds than a previous formulation. Including preprocessing techniques it quickly solves instances of medium-size to proven optimality for the first time in literature. For large real-world instances, we provide a three-stage heuristic solution procedure suitable in a dynamic environment, while providing competitive solutions within a short run time. Computational experiments on a broad set of benchmark instances and a comparative study with approaches from literature verify our results.

Velocity‐based stowage policy for a semiautomated fulfillment system

AbstractOnline retail fulfillment is increasingly performed by semiautomated fulfillment systems in which inventory is stored in mobile pods that are moved by robotic drives. In this paper, we develop a model that explores the benefits of velocity‐based stowage policies for semiautomated fulfillment systems, also known as robotic mobile fulfillment systems. The stowage policies decide which pods to replenish with the received inventory. Specifically, we model policies that account for the velocity of the units being stowed. By stowing higher (lower) velocity units on higher (lower) velocity pods, we expect to increase the heterogeneity of the pod velocities. Greater heterogeneity in pod velocities can yield a greater reduction in pod travel distance from velocity‐based storage policies for the pod. Reducing pod travel distance decreases the number of robotic drives that are needed for the system to maintain a certain throughput rate. We analyze an M‐class velocity‐based stowage policy. We stow units from each velocity class onto pods dedicated to that velocity class; each class of pods then has its own storage zone, where the zones are ordered based on the distance to the stationary pick and stow stations. We characterize the pod travel distance as a function of the skewness of the demand distribution and the number and size of the classes. We find that with two or three classes we can achieve most of the benefits possible from a velocity‐based stowage and storage policy. For representative demand distributions, we find that a two‐class policy achieves 75% of the maximum possible travel‐time reduction and that a three‐class policy improves this to 90%.

Performance evaluation of a robotic mobile fulfillment system with multiple picking stations under zoning policy

This paper evaluates the performance of a robotic mobile fulfillment system (RMFS) with multiple picking stations under a zoning policy. Focusing on a single picking process performed by robot, we develop an expected travel time model for the robot considering a constant velocity, and evaluate the system performance according to the travel time. Compared with the data of the actual RMF system from FAW Logistics Co., Ltd, the model developed in this study has high accuracy and can be used to rapidly evaluate the performance of RMFS with different layouts. In addition, by the numerical experiments, the effect of the number of horizontal aisles and two zoning policies on system performance is analyzed. The results show the following: (1) With the initial total number of storage locations determined, when picking efficiency and space utilization are taken as indexes for planning and designing, the optimal scheme can be obtained more directly by focusing on the effect of the number of horizontal aisles on the expected travel time. (2) Under the relevant assumptions and system characteristics proposed herein, when the expected travel time is taken as the index, it is always better to store A-class goods in the center of the pod storage area.

An efficient simulation model for layout and mode performance evaluation of robotic mobile fulfillment systems

The robotic mobile fulfillment system (RMFS) is a “goods to man” picking system that employs numerous automatic guided vehicles (AGVs) instead of workers to transport goods from the storage station to the picking station. Using smarter AGVs enhances the performance but also leads to layout problems and the challenge of efficiently estimating the system performance with various layouts. With the same capability of storage, the different row and column of storage position area can lead to different system performance. The number of combinations of rows and columns is huge which makes it time-consuming to compare all the combinations to find the best one, and different path modes (unidirectional path mode and bidirectional path mode) and different number of AGVs further complicate the problem. Quickly modeling and performance estimating of RMFS become the biggest challenge for logistics enterprise. In this paper, according to the modular simulation theory, an efficient model based on discrete-event system simulation is proposed for quickly constructing different RMFS layouts and estimating their performance. The model contains two independent components: a physical component and a control component. The nuclear module for physical component is the track module that forms all tracks and can be temporarily or permanently blocked to satisfy the requirements of both unidirectional and bidirectional path mode models. The main functions of the control component are routing, collision prediction, and collision avoidance. The physical and control components are independent but work together to construct and run a certain model according to the input parameters; thus, an RMFS model can be efficiently built by simply entering parameters. A series of experiments were conducted to evaluate the performance of the RMFS in different layouts with various numbers of AGVs in both the unidirectional and bidirectional path modes. The results indicated that in the same layout, the bidirectional path mode can reach the summit output with fewer AGVs. The experimental results also indicated that in a large-scale RMFS, vertically arranging the storage station on the block island and then horizontally positioning the block island can result in a higher output than other layouts. In addition, the experimental results indicated that the proposed model can quickly construct different RMFS layouts and efficiently determine the best scheme.

Workstation layout strategies in Robotic Mobile Fulfillment Systems considering carbon emissions

A Robotic Mobile Fulfillment System (RMFS) in the e-commerce industry is an automated order picking system for reducing workers’ moving time, aiming to improve the efficiency of the warehouse. Robots are able to carry movable shelves, known as pods, traveling swiftly between the storage area and workstations, including pick and replenishment stations. This paper designs bidirectional robots driving rules and studies warehouse layout planning decision and evaluates system operational performance by modeling a Semi-Open Queueing Network (SOQN). Furthermore, we analyze the impact of different warehouse layout on system throughput time and carbon emission.

Evaluation of the Joint Impact of the Storage Assignment and Order Batching in Mobile-Pod Warehouse Systems

The rapid development of e-commerce has impacted the intelligence level of warehouses. The robotic mobile fulfillment system (RMFS) demonstrated that mobile-pod warehouse systems could process a significant number of order-picking operations within the same amount of time as other systems, saving money and improving operational efficiency. The order-picking mode in such a system is “parts-to-picker,” which is becoming both a prevailing operation mode in industrial warehouses and an encouraging research field during these years. One of the most significant fundamental factors in RMFS is the operational efficiency that is affected by storage assignment and order batching. This paper considers the joint impact of the storage assignment policies and order batching policies on order picking process. Our goal is to minimize the moving times of robots, which reflects the order-picking cost or efficiency. We propose using order similarity and item similarity to batch orders and assign item storage locations, respectively. Both the order batching and item grouping are tackled by a clustering model which is an integer linear program. We also develop a policy evaluation model to measure the order picking cost. We conduct numerical tests on six order batching and storage assignment policy combinations. A comparative analysis and an ANOVA analysis are then performed on the test results to compare the performances of these policy combinations. We find that the Weighted Support Count-based storage allocation combining with the correlation-based order batching achieves the best order-picking performance. Also, the more accurate information about the items and orders we can get from the historical data, the more order-picking workload we can save by exploiting the similarity features.

A bi-level memetic algorithm for the integrated order and vehicle scheduling in a RMFS

A Robotic Mobile Fulfillment System (RMFS) is a parts-to-picker system designed for e-commerce warehousing where robots are used to fetch inventory pods from the storage area and transport them to the appropriate workstation. At these stations human workers pick the required amount of goods from the pods to fulfill the active orders. The RMFS is composed of several hard sub-problems which are typically solved sequentially. To the best of the authors’ knowledge, there exists no algorithm that integrates multiple of these problems and consider the interdependencies between them. This paper focuses on solving the integrated order to workstation and robot scheduling problem and proposes a bi-level memetic algorithm. Computational experiments on a wide range of problem instances show the importance of considering an integrated solution approach over a sequential approach for this complex problem. The experiment clearly show the impact of the existing interdependencies. Moreover, the study shows that the pod selection problem for the order fulfillment has a significant impact on the overall system performance. The inventory pod’s consolidation opportunity and distance from the picking station has to be taken into account.