A novel storage location allocation strategy for intelligent E-commerce warehouse with new products

Industrial internet of things-driven storage location assignment and order picking in a resource synchronization and sharing-based robotic mobile fulfillment system

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 reinforcement learning-based hyper-heuristic for AGV task assignment and route planning in parts-to-picker warehouses

Robotic mobile fulfillment (RMF) systems automate storage and transportation within fulfillment centers while still relying on human pickers. We focus on two key performance metrics in these systems, aiming to minimize overall completion time (OCT) as the primary objective and the number of required robots (NRR) as a secondary objective. We investigate two interrelated operational problems influencing these metrics: (i) Pod selection, which involves choosing the mobile racks for item picking, and (ii) pod scheduling, which entails assigning these racks to pickers and determining the picking sequence. We first explore the pod scheduling problem independently, providing theoretical results. This stand-alone problem is NP-hard with at least two pickers when minimizing OCT and remains NP-hard with even one picker when minimizing NRR. The NRR objective introduces a novel optimization problem structure, contributing to scheduling theory even beyond the RMF context. We demonstrate that a simple but effective scheduling rule is asymptotically optimal for minimizing OCT with multiple pickers. For NRR minimization with a single picker, we derive theoretical performance bounds for two sequencing rules. When incorporating the pod selection problem, we focus on two approaches: (i) Sequential and (ii) integrated pod selection and scheduling. The sequential approach handles pod selection first, then pod scheduling, while the integrated approach addresses both problems simultaneously within a single formulation to minimize OCT. Computational experiments using e-commerce order data reveal that the sequential approach achieves significantly faster and constant computation times, with a mean OCT similar to that of the integrated approach. We also find that our sequencing rules can reduce mean NRR by 13%–28% without affecting OCT, compared to the case where no such rules are used. The sequential approach for OCT minimization and the proposed sequencing rules for NRR minimization are scalable to large systems with any picker count. Our experiments also explore the changes in OCT and NRR values with varying numbers of pickers. These results guide managers in assigning the desired number of pickers for a target OCT and inform managers about the average NRR they can expect for the chosen picker count.

The existing studies on the robotic mobile fulfillment systems (RMFS) have analyzed robot route planning, throughput and layout of RMFS based on single deep layout. As land becomes more expensive and the demand to warehouse is increasing, it's essential to consider multiple deep layout to realize compact storage. This paper applies compact storage strategy to traditional parts-to-picker storage system. We develop a semi-open queuing network model (SOQN) to analyze the RMFS with multiple deep layout. The performance indicators such as system throughput and robot utilization are obtained. Our work can provide valuable suggestions for designing compact RMFS, such as which kind of layout (n × m) of block benefits for throughput, how many robots and workstations are required to reach the given throughput under the limited warehouse area and so on.

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

A robotic mobile fulfillment system (RMFS) performs the order fulfillment process by bringing inventory to workers at pick-pack-and-ship warehouses. In the RMFS, robots lift and carry shelving units, called inventory pods, from storage locations to picking stations where workers pick items off the pods and put them into shipping cartons. The robots then return the pods to the storage area and transport other pods. In this article, we consider an item assignment problem in the RMFS in order to maximize the sum of similarity values of items in each pod. We especially focus on a reoptimization heuristic to address the situation where the similarity values are altered so that a good assignment solution can be obtained quickly with the changed similarity values. A constructive heuristic algorithm for the item assignment problem is developed, and then, a reoptimization heuristic is proposed based on the constructive heuristic algorithm. Then, computational results for several instances of the problem with 10–500 items are presented. We further analyze the case for which an item type can be placed into two pods. Note to Practitioners —This article proposes an efficient heuristic algorithm for assigning items to pods in a robotic mobile fulfillment system (RMFS) so that items ordered together frequently are put into the same pod. Computational results with 10–500 items show that the gaps from upper bounds are very small on average. For cases where the similarity values between items change or their estimation is not accurate due to the fluctuations in demand, a reoptimization heuristic algorithm that alters the original assignment is developed. The experimental results show that the reoptimization algorithm is robust when perturbation levels are approximately 40%–50% of the original similarity values with much less computation times. We believe that this research work can be very helpful for operating the RMFS efficiently.

Context: In the age of rapid e-commerce growth; the Robotic Mobile Fulfillment Systems (RMFS) have become the major trend in warehouse automation. These systems involve the use of self- governed mobile chares to collect shelves as well as orders for deliveries with regard to optimization of task allocation and with reduced expenses. However, in a manner to implement such systems, one needs to find enhanced algorithms pertaining to resource mapping and the planning of movement of robots in sensitive environments. Problem Statement: Despite RMFS have certain challenges especially when it comes to the distribution of tasks and the overall distances that employees have to cover. Objective: The main goal of this paper is to propose a new compound optimization model based on RL-ACO to optimize the RMFS’s task assignment and navigation. Also, the direction of the study is to investigate how such methods can be applied to real-life warehouse automation and how effective such methods can be on a large scale. Methodology: This research introduces a new optimization model for RMFS selection which integrates reinforcement learning with Ant Colony Optimization (ACO). Specifically, a real gym environment was created to perform the order assignment and training in the way of robotic movement. Reinforcement Learning (RL) models were trained with Proximal Policy Optimization (PPO) for improving the dynamic control of robots and ACO was used for computing optimal shelf trajectories. The performance was also measured by policy gradient loss, travelled distance and time taken to complete the tasks. Results: The proposed framework showed potential in enhancing the efficiency of tasks required and the travel distances involved. In each of the RL models used the shortest paths were identified and the best route was determined to have a total distance of 102.91 units. Also, other values such as, value function loss and policy gradient loss showed learning and convergence in iterations. To build a global solution, ACO integration went a step forward in enabling route optimization through effective combinatorial problems solving. Implications: This research offers a practical, generalizable and flexible approach for the improvement of the operations of RMFS and thinking for warehouse automation.

The rapid development of E-commerce has forced warehouse operations to develop towards a robotics-based system named Robotic Mobile Fulfillment System (RMFS), in which shortest path planning and conflict recognition play a vital role in enhancing the operational efficiency under multiple mobile robots movement. Compared to the traditional double-deep layout in Automatic Guided Vehicle (AGV) system, this paper proposes multi-deep based layouts in RMFS, including the modification of Flying-V, Fishbone and Chevron layouts. Under these circumstances, this paper further adopts the shortest path graph attention network in RMFS. This paper considers the Dijkstra algorithm as a baseline and compares it with Biased Cost Pathfinding methods, Anytime Repairing A-star and Flow-Annotation Re-planning methods. The shortest path graph attention network adoption in RMFS should enhance the overall operational efficiency and effectiveness under different layouts scenarios with different path planning methods.

In recent years, the Robotic Mobile Fulfillment System has been established as a new goods-to-person storage system, which particularly addresses the needs of e-commerce. In these systems, a fleet of mobile robots carries inventory pods (mobile racks) between picking stations and storage locations, a task that requires efficient path planning for potentially hundreds of robots. Therefore, this task can be considered an instance of the Multi-Agent Path Finding problem, where the goal is to find collision-free and goal-reaching paths for a set of agents. Previous publications addressing Multi-Agent Path Finding for Robotic Mobile Fulfillment Systems use oversimplified goal-assignment schemes and use-cases. To address these issues, we present an adapted version of the Multi-Agent Path Finding problem that mimics the goal assignment scheme of real-world picking systems and we introduce three industry-inspired use-cases. Finally, using the Rolling Horizon Collision Resolution framework, we apply three state-of-the-art solvers for Multi-Agent Path Finding problems to our use-cases. Our preliminary results indicate that two of the three solvers are suitable for usage in Robotic Mobile Fulfilment systems.

This paper studies a robotic mobile fulfillment system (RMFS) featured by robots transporting movables shelves to order pickers. The RMFS can increase productivity, reduce costs, increase order picking accuracy, and improve operational flexibility. We build queue network models to describe the RMFS with two protocols in sharing robots for pickers, propose the corresponding algorithms, conduct numerical analyses, and evaluate the performance of the RMFS by calculating the throughput time. We then calculate the optimal number and the velocity of robots, and provide the effective design rules for the RMFS.

With high efficiency and good scalability, Robotic Mobile Fulfilment Systems (RMFS) are increasingly applied in various warehouses, especially the e-commerce warehouses with rigid order completion time. RMFS requires less workers and provide more punctual service for customers. The existing literature on RMFS is based on single-deep non-compact layout. As land supply is limited and expensive in urban area, it’s essential to consider compact storage in RMFS. This paper is the first to model and evaluate the multi-deep compact RMFS. We develop a semi-open queueing network (SOQN) model to characterise the multi-deep compact RMFS and solve it by Approximate Mean Value Analysis (AMVA). The obtained approximate analytic solutions of system throughput, robot utilisation, and queue length were verified and assessed through simulations. The numerical experiments investigated the effects of different configuration of the lane depth, number of picking aisles, arrangement of picking stations and the number of robots on performance. Our research can provide useful guidelines for warehouse planners and managers for designing and operating multi-deep compact RMFS.

The robotic mobile fulfillment system (RMFS) is a new automatic warehousing system, a type of green technology, and an emerging trend in the logistics industry. In this study, we take an RMFS as the research object and combine the connected issues of storage location assignment and path planning into one optimization problem from the perspective of collaborative optimization. A sustainable mathematical model for the collaborative optimization of storage location assignment and path planning (COSLAPP) is established, which considers the relationship between the location assignment of goods and rack storage and path planning in an RMFS. On this basis, we propose a location assignment strategy for goods clustering and rack turnover, which utilizes reservation tables, sets AGV operation rules to resolve AGV running conflicts, and improves the A-star(A*) algorithm based on the node load to find the shortest path by which the AGV handling the racks can complete the order picking. Ultimately, simulation studies were performed to ascertain the effectiveness of COSLAPP in the RMFS; the results show that the new approach can significantly improve order picking efficiency, reduce energy consumption, and lessen the operating costs of the warehouse of a distribution center.

The role of energy consumption in robotic mobile fulfillment systems: Performance evaluation and operating policies with dynamic priority

The rapid development and implementation of the Internet of Things (IoT) and Cyber-Physical Systems (CPS) in the engineering and manufacturing field have embraced a virtual identity to ensure nearly real-time adjustment. Warehouses are challenged to reassess its order fulfillment operations while simultaneously being provided with the opportunity to develop its own cloud-based CPS with the aid of IoT devices. Robotic Mobile Fulfillment System (RMFS) is a system controlling mobile robots, mobile storage rack, putaway and picking workstations, charging stations, and wireless communication infrastructure in the context of robotic-assisted warehouse. This paper addresses the value creation utilizing cloud-based CPS in RMFS. By providing an analysis of cloud services and IoT enhancement, theoretical concepts from the literatures are consolidated to solve the research que-stions on how RMFS offering better order fulfillment can gain benefits in terms of operational efficiency and system reliability. The paper also proposes a cloud-based CPS architecture, providing a comprehensive understanding on conflict avoidance strategy in the multi-layers multi-deeps warehouse layout. This research presents six conflict classifications in RMFS and provides a case study in the real-life context. Dock grid conflict is a new type of conflict appearing in multi-deeps RMFS. A scenario analysis with real customer orders is applied to present the collision detection and solution.