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Estimating Performance in a Robotic Mobile Fulfillment System
Abstract
This paper models Robotic Mobile Fulfillment Systems and analyzes their performance. A Robotic Mobile Fulfillment System is an automated, parts-to-picker storage system where robots bring pods with products to a workstation. It is especially suited for e-commerce distribution centers with large assortments of small products, and with strong demand fluctuations. Its most important feature is the ability to automatically sort inventory and to adapt the warehouse layout in a short period of time. Queueing network models are developed for both single-line and multi-line orders, to analytically estimate maximum order throughput, average order cycle time, and robot utilization. These models can be used to quickly evaluate different warehouse layouts, or robot zoning strategies. Two main contributions are that the models include accurate driving behavior of robots and multi-line orders. The results show that: 1) the analytical models accurately estimate robot utilization, workstation utilization, and order cycle time 2) maximum order throughput is quite insensitive to the length-to-width ratio of the storage area and 3) maximum order throughput is affected by the location of the workstations around the storage area.
... The exponential growth observed among e-commerce companies, coupled with rising labor costs and the necessity for careful handling of goods, has spurred a demand for Robotic Mobile Fulfillment Systems (RMFS) [1]- [5]. In the context of a typical RMFS, robots directly transport shelves, referred to as 'pods', containing products between workstations and the shelf storage area [6]. However, directly transporting shelves imposes limitations on shelf height and weight, leading to reduced space utilization. ...
... Although the research in [8] analyzes the MTSR system through a SOQN, it does not investigate the influence of orders with varying numbers of product lines on the system performance. This imposes some new challenges: 1) The probability density function of the number of lines within an order can follow a general distribution, rather than being limited to only the geometric distribution [6], [16]; 2) Due to the limited buffer positions on a robot, the robot might need to make several trips to fulfill an order if the number of lines within it exceeds the buffer positions; 3) The service time at the workstation where goods are handled and the traveling time while retrieving and storing totes should depend on the number of assigned totes to the robots in a trip. Thus, our contributions in this paper can be summarized as follows: ...
... Multi-line orders in RMFS are studied in [6] and [16]. These orders are presumed to follow a geometric distribution in terms of the number of lines. ...
As warehouses are emphasizing space utilization and the ability to handle multi-line orders, multi-tote storage and retrieval (MTSR) autonomous mobile robot systems, where robots directly retrieve totes from high shelves, are becoming increasingly popular. This paper presents a novel shared-token, multi-class, semi-open queueing network model to account for multi-line orders with general distribution forms in MTSR systems. The numerical results obtained from solving the SOQN model are validated against discrete-event simulation, with most key performance metrics demonstrating high accuracy. In our experimental setting, results indicate a 12.5% reduction in the minimum number of robots needed to satisfy a specific order arrival rate using the closest retrieval sequence policy compared with the random policy. Increasing the number of tote buffer positions on a robot can greatly reduce the number of robots required in the warehouse.
... Moreover, they explained the foundations of the Kiva solution and the AI techniques they included in such a system. In the second most relevant article, Lamballais et al. studied different layouts, seeking to identify the best distribution [38]. The authors found two significant results. ...
... Additionally, if we delve deeper into the top 10 authors, we find that two of them, Roy, D. and Lamballais, T., have coinciding works. In fact, they have three works in common [38], [41], [45]. China seems to be the most prolific country, as it provides 50% of the manuscripts. ...
... Actually, this is something relatively common since we also found other papers with collaborations between institutions from different countries. For example, the Netherlands has collaborated with India [38] and with Germany [37]. ...
The Robotic Mobile Fulfillment System (RMFS) is a method for handling products, in which a Line Follower Robot (LFR) transports products to a human workstation for packing. In this systematic review, we delve into the current state of RMFS research using data sourced from Scopus. After a comprehensive search, we found 264 manuscripts, which we filtered to 76 relevant articles. Our analysis covers several variables, from basic metadata to manuscript impact and specific conditions the authors consider. We discovered that there needs to be more focus on the pod allocation problem, despite its potential, with the majority of the emphasis on LFR displacement. We created a detailed diagram that outlines the essential elements and subproblems associated with RMFS. As the interest in RMFS continues growing, our study provides crucial insights and direction for future research efforts.
... The new high-capacity batteries (e.g.lithium-ion) enable longer operational time and provide more power for the calculations needed for autonomous navigation and operations. They also allow the AMRs to be smaller (this also holds for the newest AGVs) and thus to be deployed in narrow-aisle areas, or even directly underneath multiple loads stored closely in deep lanes ( Lamballais et al. , 2017 ). With these technological improvements, the importance of battery management has declined somewhat, although it may still be relevant in 24/7 operations ( Zou et al. , 2018 ). ...
... Different warehouse layouts, zone and service point configurations for order-picking robots are analyzed using MILP and numerical analysis with the goal of minimizing the time needed to deliver all items from a pick list to the packing station. Lamballais et al. (2017) and Roy et al. (2019) use queuing network models and simulation to analyze zone assignment strategies in RMF systems to improve system throughput, average order cycle time, and robot utilization. To analyze the preferred number of service points in such systems, Lamballais et al. (2020) use a semiopen queueing network with simulation to determine the optimal number of pods, and picking and replenishment stations. ...
Autonomous mobile robots (AMR) are currently being introduced in many intralogistics operations, like manufacturing, warehouses, cross-docks, terminals, and hospitals. Their advanced hardware and control software enable decentralized decision-making processes and thereby autonomous operations in dynamic environments. These developments have influenced traditional planning and control methods. This study describes first the AMR attributes in intralogistics. Secondly, it explains the technological advances and their impact on AMR planning and control. Next, it introduces crucial planning and control decisions for AMRs in intralogistics and presents an emerging planning and control framework. Finally, it highlights current AMR challenges that need to be overcome and outlines future application areas.
... The picker processes only one order at a time. Fig. 3 shows the storage area which consists of multiple aisles and cross-aisles (adapted from Roodbergen et al. (2008) and Lamballais et al. (2017)). Such a layout can be found in many warehouses, including online grocery fulfillment centers. ...
... Table 5 presents the delivery parameters. The parameters of the order picking process are based on the parameters of the e-commerce fulfillment center presented by Lamballais et al. (2017). Table 5 summarizes the order pick parameters. ...
... This autonomy makes AMRs particularly suitable for modern warehouses, where layout changes and space optimization are frequent. The ability of AMRs to independently handle tasks such as picking, transportation, and inventory management, while seamlessly integrating into existing workflows, positions them as a versatile and future-proof choice for warehouse automation [44,45]. Moreover, the rise of e-commerce and the corresponding need for rapid, flexible, and scalable warehouse operations further underscore the relevance of AMRs in meeting these evolving demands [42]. ...
This paper introduces an innovative approach to augmenting the capabilities of autonomous mobile robots (AMRs) in dynamic, multi-agent warehouse environments. We propose an integrated system that combines Multi-Agent Deep Deterministic Policy Gradient (MADDPG) a cutting-edge multi-agent reinforcement learning (MARL) algorithm with Curriculum Learning (CL). This hybrid method is specifically tailored to meet the complex demands of warehouse operations. Our approach is distinguished by the development of decentralized policies within a centralized training framework, progressively incorporating complexity through a carefully structured curriculum. This strategy enables autonomous agents to master sophisticated navigation and coordination tactics, adapting adeptly to the variable requirements of real-time warehouse scenarios. Through comprehensive experiments, we demonstrate significant enhancements in autonomous decision-making and operational efficiency, thereby validating the effectiveness of merging MARL with CL in intricate, multi-agent contexts. Our findings align with recent advancements in MARL applications for robotics 1 and underscore the potential of curriculum-based learning strategies in complex environments.
... A SOQN to evaluate the performance of a robotic mobile fulfilment system was proposed in [1], and the arriving of the order follows the general distribution, while [6] establish integrated methods which combine the queuing network and Markov decision process to optimize the operational process. In [5], the authors proposed a SOQN to analyze the system performance, including maximum order throughput, average order cycle time and robot utilisation and so on. An OQN is established [10] to different operational policies applied in the system. ...
This review delves into the application of queuing networks within warehousing systems, highlighting their significance in optimizing operations amidst inherent stochasticity. The paper systematically evaluates existing literature, focusing on methodologies such as open, closed, and semi-open queuing networks. It underscores the necessity for developing more sophisticated analytical algorithms to handle general service time distributions and complex correlations. The review advocates for the integration of realistic distributions and empirical validation to enhance model accuracy. Ultimately, it posits that queuing network models are pivotal for advancing warehousing efficiency and innovation, emphasizing their transformative potential in the logistics sector.
... Accelerating to full speed takes very little time. Moreover, this assumption is common in the literature on robotic warehousing systems (Fragapane et al., 2021;Lamballais et al., 2017;Roy et al., 2012;Roy et al., 2020). 5. Sufficient robots are used in the system. The number of robots is determined at the design phase to meet peak demand, which guarantees their availability in daily operations. ...
Autonomous mobile robots are increasingly used for order picking, order delivery, and parcel sorting. This article studies a robotic sorting system that uses robots to transport parcels from loading stations to drop‐off points. While this system provides more flexible throughput capacity than conventional sorting systems, its performance is significantly affected by the robot travel distance and robot congestion. We study the problem of assigning parcel destinations to drop‐off points to minimize the throughput time, trading off travel distance and congestion. First, an open queuing network (OQN) with finite capacity queues is constructed to estimate the congested throughput time. A decomposition method based on the analysis of the tandem queuing network of each aisle is developed to solve the OQN. Second, using the obtained throughput time as an objective and the destination assignments as decisions, we formulate an optimization model and solve the problem using an adaptive large neighborhood search (ALNS) algorithm. We validate the accuracy of the OQN by simulation and verify the efficiency of the ALNS algorithm by comparing it with Gurobi, a tabu search algorithm, several heuristic assignment rules, and the rule used by our case company, that assigns high demands close to loading stations. The results show that the ALNS solution provides a relatively low throughput time by dispersing destinations with high demands over drop‐off points. In addition, we investigate the effects of different system layouts and travel path topologies. We also show that the ALNS assignment rule produces substantially lower operational costs than the heuristic assignment rules for a given required throughput capacity.
... For example, the latest lithium-ion high-capacity batteries allow longer operating times and offer more power for the computations required for autonomous operations and navigation. Also, they enable the robots to be smaller (this also applies to the newest AGVs), enabling their deployment in places with narrow aisles or even immediately underneath loads packed closely together in deep lanes [78]. ...
The just-in-time concept, mass customization, omnichannel distribution, and the rising global population have all fueled the logistics sector. Consequently, using automation inside the warehouses to make them more dynamic and sustainable for the future is one of the crucial components to adapt to this quick shift. Giants in the industry and technology are becoming more interested in the “smart warehouse” system, built with innovative warehousing technologies, as an achievable solution for the development of warehouses in the future. To contextualize the past and provide light on prospective future directions, a study of current articles in the literature is important. This study evaluates works published in the previous 32 years related to flexible automation in warehouses to create a framework that future academics might use to guide them in establishing an original conceptual model that might be implemented at warehouses. One hundred eleven selected, examined, and categorized publications were published between 1990 and 2022 to establish a significant foundation for earlier research. Results indicated that combining automated machinery, collecting data technologies, and management systems are essential to creating a flexible automated warehouse. Finally, based on the examined literature, a flexible automated warehouse framework is suggested to direct future research in this field.
... Order picking is a labor-intensive and time-consuming process in warehouses (De Koster, Le-Duc, and Jan Roodbergen 2007;Lamballais, Roy, and Koster 2017), which retrieves certain items for customer orders. The massive orders and tight schedules brought by the flourishing E-commerce now claim for a more efficient order-picking solution (Batt and Gallino 2019). ...
... RMF systems are similar to MTSR robotic systems, as the pods contain multiple products (albeit typically not all the products needed for a single order). Lamballais, Roy, and De Koster (2017) develop a QN model to calculate the throughput of multiline orders. They find that tote throughput capacity is insensitive to the length-to-width ratio of the storage area but is affected by the position of the workstations at the perimeter of the storage area. ...
Multi-tote storage and retrieval (MTSR) autonomous mobile robots can carry multiple product totes, store and retrieve them from different shelf rack tiers, and transport them to a workstation where the products are picked to fulfill customer orders. In each robot trip, totes retrieved during the previous trip must be stored. This leads to a mixed storage and retrieval route. We analyze this mixed storage and retrieval route problem and derive the optimal travel route for a multiblock warehouse by a layered graph algorithm, based on storage first-retrieval second and mixed storage and retrieval policies. We also propose an effective heuristic routing policy, the closest retrieval (CR) sequence policy, based on a local shortest path. Numerical results show that the CR policy leads to shorter travel times than the well-known S-shape policy, whereas the gap with the optimal mixed storage and retrieval policy in practical scenarios is small. Based on the CR policy, we model the stochastic behavior of the system using a semiopen queuing network (SOQN). This model can accurately estimate average tote throughput time and system throughput capacity as a function of the number of robots in the system. We use the SOQN and corresponding closed queuing network models to optimize the total annual cost as a function of the warehouse shape, the number of robots, and tote buffer positions on the robots for a given average tote throughput time and throughput capacity. Compared with robots that retrieve a single tote per trip, an MTSR system with at least five buffer positions can achieve lower operational costs while meeting given average tote throughput time and tote throughput capacity constraints.
Funding: This work was supported by National Natural Science Foundation of China [Grant 72372088] and the Shenzhen Science and Technology Program [Grant GJHZ20220913143003006].
Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0397 .
... There are some publications related to the layout design in the RMFS, such as the dimension of the storage area [9], the shape of the forward area [1], the number of pods, the ratio of stations, the placement of stations [8,9] and the number of robots [7,10,15]. The most common types of stations are picking stations, where human pickers pick items from pods, and replenishment stations, where items are stored on pods. ...
In the era of digital commerce, the surge in online shopping and the expectation for rapid delivery have placed unprecedented demands on warehouse operations. The traditional method of order fulfilment , where human order pickers traverse large storage areas to pick items, has become a bottleneck, consuming valuable time and resources. Robotic Mobile Fulfilment Systems (RMFS) offer a solution by using robots to transport storage racks directly to human-operated picking stations , eliminating the need for pickers to travel. This paper introduces 'combi-stations'-a novel type of station that enables both item picking and replenishment, as opposed to traditional separate stations. We analyse the efficiency of combi-stations using queueing theory and demonstrate their potential to streamline warehouse operations. Our results suggest that combi-stations can reduce the number of robots required for stability and significantly reduce order turnover time, indicating a promising direction for future warehouse automation.
... Nigam et al. (2014) developed a throughput calculation approach using a CQN and a class-based storage strategy. In contrast, Lamballais et al. (2017) built an SOQN to determine robot utilisation. They asserted that the influence of the location of the working station on the throughput is way higher than the length-to-wide ratio of the storage area. ...
The number of robotic compact storage and retrieval systems (RCS/RS) is one of the fastest growing compared to other storage systems. Over a thousand systems with a large spectrum of design and order speed are already in operation. Nonetheless, there is hardly any information on throughput or optimal system design. This paper presents an analytical approach for the performance calculation of an RCS/RS, which operates with several robots serving one I/O shaft. One robot’s cycle time is calculated by assuming a uniform distribution of container stacks and a probabilistic storage height. Based on this, the interaction of the robots at the I/O shaft is considered using an open queueing model with limited capacity. After validating the analytical approach using a discrete event simulation model of an RCS/RS, an extensive parameter variation is done. The easy and fast solvability with standard calculation programs and applicability are just two benefits.
... Instead of being stored in a fixed location like a traditional warehouse, each Stock Keeping Unit (SKU) can be scattered and put on numerous mobile racks, each rack holding varying amounts of different categories of SKUs [4,5]. The MRPS significantly improves the flexibility of SKUs storage and efficiently addresses the difficulty of e-commerce order picking by allowing for the relocation of racks in the warehouse [6]. As a result, it is highly desired by numerous ecommerce warehousing enterprises. ...
The flexible movement of racks in the mobile-rack picking system (MRPS) significantly improves the picking efficiency of e-commerce orders with the characteristics of “one order multi–items” and creates a challenging problem of how to place racks in the warehouse. This is because the placement of each rack in the MRPS directly influences the distance that racks need to be moved during order picking, which in turn affects the order picking efficiency. To handle the rack location optimization problem (RLOP), this work introduces a novel idea and methodology, taking into account the heat degree and the relevance degree of racks, to enhance the efficiency of rack placements in the MRPS. Specifically, a two-stage solution strategy is implemented. In stage 1, an integer programming model (Model 1) is developed to determine the heat and relevance degree of racks, and it can be solved quickly by the Gurobi. Stage 2 entails developing a bi-objective integer programming model (Model 2) with the objective to minimize the travel distances of robots in both heavy load and no-load conditions, using the rack heat and relevance degree as inputs. In light of the challenge of decision coupling and the vast solution space in stage 2, we innovatively propose two lower bounds by slacking off the distance between storage locations. A matheuristic algorithm based on Benders decomposition (MABBD) is designed, which utilizes Benders-related rules to reconstruct Model 2, introduces an enhanced cut and an improved optimal cut with RLOP characteristics, and designs the warm start strategy and the master variable fixed strategy. Given the substantial size of real-life problems, the Memetic algorithm (MA) is specifically devised to address them. Instances of varying sizes are also employed to validate the science and efficacy of the model and algorithm.
... Hence, we are confident that our proposed number of instances is enough to elucidate some patterns. Finally, for all experiments, we utilized the same layout distribution for the warehouse as shown in Fig. 2. The reason is that the location of workstations and replenishment stations affect performance [33]. In our warehouse, we arranged the workstations on the right and the replenishment stations on the left, following the scheme proposed by Merschformann et al. [30]. ...
... One solution for increasing productivity and reducing labour costs is to automate the order picking process. Various automated order picking systems, such as automated guided vehicles (AGVs), rail guided vehicles (RGV), and other types of robots, have been investigated (Sun et al., 2021;Luo et al., 2023;Lamballais et al., 2017;Xin et al., 2014). In this paper, we focus on improving the performance of a robotic put wall, an automated OPS that processes a high volume of orders in a limited space using a fleet of RGVs (see Figure 1). ...
In the current epoch, robotics offers numerous practical and commercial occasions. Warehouse order picking is a key task, especially for large-scale distribution companies. The Robotic Mobile Fulfillment System (RMFS) is an robotic order-picking arrangement constructed on robotic handling, currently designed for e-commerce warehouses. This article aims to understand the current realities of RMFS, how customers perceive this new technology, and the prospective challenges of integrating robots into Amazon's warehousing areas. The aim is to identify opportunities and threats in the use of Robotic Mobile Fulfillment Systems. This research paper will explore the potential benefits and risks of implementing RMFS in the future. The potential advantages of RMFS include increased operational efficiency, cost savings, and improved customer satisfaction. However, there can be risks involved such as cyber security, data privacy, and cost overruns. The paper will use a combination of prospective studies and existing research to assess the potential gains of RMFS and the associated risks.
Rack moving mobile robots provide a new solution for e-commerce retailers to fulfill customers’ orders. In these systems, racks are moved by robots to workstations where pickers retrieve the purchased products from the racks and put them into totes corresponding to the customers’ orders. Such modern parts-to-picker warehouse operations are leading to the order picking problem that is different from those in traditional picker-to-parts order picking systems. This chapter focuses on the order- and rack-sequencing decision challenges with multiple picking stations in such systems. We first formulate the problem as a mixed-integer program. We then propose a two-stage heuristic solution procedure that provides competitive solutions within a short run time. Computational experiments on small cases and large-scale real-world cases demonstrate significant performance improvement with our algorithm. The results in this chapter provide comprehensive insights into modeling and solution approaches for solving the order- and rack-sequencing decision problem in rack-moving mobile robot systems.
Amazon is a world leader in e-commerce and robotization. Its robotic mobile fulfillment systems (RMFS) aim to speed up product delivery by using robots to move shelves. In terms of speed, precision, and profitability, the RMFS solution seems to guarantee economic and commercial success. Nevertheless, the integration of robotization is accompanied by an increase in workload, daily tasks, and accident risks. The aim of the present study is to provide an in-depth analysis of Amazon RFMS employees' perceptions of current robotization. Through a netnographic study of the comments and opinions of Amazon employees working in robotized warehouses, the empirical study will gather argumentative thinking around the effect of digital transformation in RFMS in terms of employability, working conditions, and quality of work life. The results of the study show a consolidation of working conditions accompanied by stress and a risk of job loss. Certain risks and doubts are also present, notably around the possibility of work-related accidents and the man-machine interactions.
In the ever-changing corporate world of today, there has been a notable surge in the use of digital marketing tactics. The goal of this study is to thoroughly examine and comprehend the elements that have led to a notable rise in the use of digital marketing by companies in a variety of industries. In order to obtain insights, the article uses a mixed-methods strategy that combines qualitative and quantitative procedures. The paper explores the historical development of marketing tactics through a thorough literature analysis, highlighting the critical role that digital platforms have had in changing the marketing paradigm. It also looks at the different digital marketing platforms and techniques that have become more popular recently. Surveys and data analysis are used in the quantitative phase to find trends and patterns in the adoption of digital marketing strategies among
A visit to Ahmedabad's historical sites showcases a rich cultural and architectural heritage, but visitors often face multifaceted challenges that affect their overall experience. One prominent problem is inadequate signage and information dissemination, which leads to confusion and difficulty in navigating large historical sites. The lack of clear instructions prevents visitors the ability to appreciate the historical significance of each structure and reduce the educational value of the visit. In addition, the preservation and maintenance of historical sites creates significant problems. Inadequate maintenance can lead to structural damage, restricted access and safety hazards. Visitors may find parts of these websites inaccessible, impairing their research and understanding. The absence of modern amenities such as clean toilets and recreational facilities also hinders the overall comfort of visitors, affecting their willingness to spend long periods of time exploring these cultural assets. Additionally, traffic problems impair the visitor experience. Limited connectivity and parking facilities can discourage would-be explorers and prevent them from fully immersing themselves in the historical wonders that Ahmedabad has to offer. These common concerns highlight the need for comprehensive solutions, including improved signage, increased site maintenance and improved infrastructure, to provide a seamless and enriching experience for visitors to Ahmedabad's historic sites. Addressing these challenges will not only increase the tourism potential of these destinations, but will also contribute to the preservation and appreciation of the city and its rich historical heritage.
The multinational companies engaged in logistics and one of its fields is export and import shipping activities. Determination of priority stockpile locations in air import activities has a role in efforts to improve the quality of facilities to minimize the cost, distance, and time of company activities. This research is expected to help provide information about the right location to be chosen by the company by considering factors and criteria that affect the company's shipping activities, the criteria used are Time, Quality, Location, Service. The method used is the Analytical Hierarchy Process (AHP) method using a quantitative approach with data collection techniques based on observations, interviews, and questionnaires. The results of the study based on the level of importance of the most dominant criteria in determining the location of priority stockpiling is the Time criterion, and then the second priority is service, then in the third priority is quality, finally in the fourth priority there is Location. From these results show that the Time criterion is very dominant in determining the location of priority stockpiling.
This research is on supply chain efficiency of e-commerce industry. this research is on specific company for get idea about all e-commerce platform. Amazon company is one of the largest ecommerce companies in the world with extensive market capitalization. We are going to know that how amazon company maintain its supply and orders with their demands. There are mainly four key elements for efficient supply chain is integration, operation, purchasing and distribution. With supply chain efficiency, amazon will pick up inventory from manufacturing facilities around the world, ship it across borders, handle customs clearance and ground transportation, store inventory in bulk, manage replenishment across amazon and other sales channels, and driver directly to customer. All without seller having to worry about managing their supply chain. The new solution allows sellers to spend more time building great products, delighting customers, and growing their business, while amazon handles the logistics, improves delivery speed and reduce costs for sellers
Robotic mobile fulfillment systems (RMFS) have received substantial attention in recent years both from academia and practice, not least of all because of their widespread adoption by Amazon. In such a system, human pickers and mobile robots collaborate by relieving the human worker of the unproductive walking time through the aisles. While this system has proven efficient, it has received criticism for its poor space utilization. Unlike other automated storage solutions, which often use high-racks, RMFS pods are only about as tall as a person. To alleviate these issues, some warehouses spread out the rack storage and picking stations over multiple floors, which, however, introduces additional issues into the operational planning problems involved with successfully running RMFS. We discuss these issues in this chapter.
This chapter enriches readers’ understanding regarding the significance of Autonomous Mobile Robots (AMRs) within the context of the warehousing and distribution industry. It offers insights into the evolving growth trends of AMRs, the current state of academic research on this technology, and real-world applications of AMRs in the logistics sector. Additionally, the chapter thoroughly discusses the challenges associated with AMR adoption and proposes a mid-range theory to facilitate their integration within the facilities. In sum, this chapter provides valuable insights for academics and practitioners alike who are eager to explore the adoption of AMRs within the warehousing and distribution industry.
With the development of robotics and Internet of Things, robot-assisted goods-to-person order picking systems become popular in smart warehouses. Order picking in such systems is a human-robot collaborative process, where robots carry pods to a picking station with human pickers who pick the demanded goods from them to fulfill orders. In it, pod selection, robot scheduling, and manual picking are highly coupled and together influence the efficiency of order picking. Their joint optimization is the key to enhancing operational efficiency but rarely studied in existing work. In order to fill such a research gap and meet high market demand, this work focuses on a novel human-robot collaborative order picking optimization problem. A mixed integer program is formulated to model it and provide an exact solution method for small-scale instances. To provide large-scale problems with efficient solutions in practical application scenarios, we propose an adaptive large-neighborhood-based tabu search algorithm. Specifically, an adaptive large neighborhood search method is designed and embedded into a tabu search algorithm with two tabu mechanisms. Experimental results indicate that the presented algorithm has significant advantages in solving the newly proposed problem. It substantially outperforms: 1) the independent use of adaptive large neighborhood search or tabu search, 2) Gurobi subject to an hour execution time, and 3) several competitive benchmark and newest well-performing algorithms. Its high performance implies its great potential in solving practical order picking optimization problems for Internet-of-Things-enabled robot-assisted smart warehouses.
E-commerce companies are increasingly facing serious challenges in their supply chains during order fulfillment, as warehouses must simultaneously handle regular and priority (termed as prime) customer orders (COs). Motivated by the concerns of parts-to-picker warehouse operators’ need for efficient order picking systems accommodating both prime and regular COs, this study presents a queuing game based analytical model to analyze the COs priority's impact on order fulfillment strategy and order picking system performance. Our model's objective function involves maximizing the benefits gained from completing order picking with incoming mobile robots and the costs associated with waiting in the picking system. Our analyses demonstrate a threshold equilibrium for the incoming robots carrying prime COs beyond which queue joining becomes non-beneficial. Besides, the likelihood ratio of efficient and inefficient order pickers is decreasing in proportion of prime COs in the system and increasing in the waiting area capacity of the picking station. Therein, unlike the current practices in e-commerce warehouses where static queueing of robots and fixed picking rate of order pickers are prevalent, our findings suggest that warehouse managers should implement dynamic queue joining strategies for robots carrying prime COs. This approach entails modifying robots’ strategies based on the proportion of prime COs and the capacity of the waiting area to achieve an optimal queue joining strategy and improve the overall system throughput. Practically, this study equips e-commerce supply chain practitioners with insights for designing flexible picking systems that handle peak-season demand fluctuations.
The order-picking process has a significant impact on both the overall logistics costs as well as the customer service level, and as a result, is characterized as one of the highest priority warehouse activities for productivity and performance improvements. Although various existing studies focus on the design of order-picking systems as well as on a series of decision-making problems, there is still a need for a holistic framework that will map and categorize the parameters (e.g. total/forward area design, order-picking equipment, storage assignment policies, level of picking locations, order picking technologies, resource planning characteristics, order picking operational policies, routing strategies, product characteristics, order profile, etc.) that should be considered by researchers and practitioners for the design, control, and evaluation of order picking systems. To this end, the aim of this paper is to provide a state-of-the-art classification and review of parameters by adopting the Systematic Literature Review (SLR) approach. Overall, 389 articles were reviewed, and the identified parameters are classified into three categories: order-picking system design, order-picking system control, and order-picking system evaluation. Furthermore, this literature review aims to present managerial implications that directly affect the successful design of order-picking systems and identify a future research agenda.
Shuttle-based compact systems are new automated multideep unit-load storage systems with lifts that can potentially achieve both low operational cost and large volume flexibility. In this paper, we develop novel queuing network models to estimate the performance of both single-tier and multitier shuttle-based compact systems. Each tier is modeled as a multiclass semi-open queuing network, whereas the vertical transfer is modeled using an open queue. For a multitier system, the models corresponding to tiers and vertical transfer are linked together using the first and second moment information of the queue departure processes. The models can handle both specialized and generic shuttles and both continuous and discrete lifts. The accuracy of the models is validated through both simulation and a real case. Errors are acceptable for conceptualizing initial designs. Numerical studies provide new design insights. Results show that the best way to minimize expected throughput time in single-tier systems is to have a depth/width ratio around 1.25. Moreover, specialized shuttles are recommended for multitier systems because the higher cost of generic shuttles is not balanced by savings in reduced throughput time and equipment needs.
Abstract Warehousing has been traditionally viewed as a non-value-adding activity but in recent years a number of new developments have meant that supply chain logistics have become critical to profitability. This paper focuses specifically on order-picking which is a key factor affecting warehouse performance. Order picking is the operation of retrieving goods from specified storage locations based on customer orders. Today’s warehouses face challenges for greater responsiveness to customer orders that require more flexibility than conventional strategies can offer. Hence, dynamic order-picking strategies that allow for changes of pick-lists during a pick cycle have attracted attention recently. In this paper we introduce an interventionist routing algorithm for optimising the dynamic order-picking routes. The algorithm is tested using a set of simulations based on an industrial case example. The results indicate that under a range of conditions, the proposed interventionist routing algorithm can outperform both static and heuristic dynamic order-picking routing algorithms.
In today’s competitive environment with increasingly faster deliveries and smaller order sizes, material handling providers are progressively developing new solutions. A more recent development in automated material-handling technology for unit load storage and retrieval is the autonomous vehicle storage and retrieval system (AVS/RS). The paper investigates the main design trade-offs for this new solution using simulation, and proposes a comprehensive design framework. Using data from a recently implemented AVS/RS, the application of the proposed framework is presented and the key design differences between the two types of AVS/RS configuration (i.e. tier-captive versus tier-to-tier) are identified.
Autonomous vehicle-based storage and retrieval system (AVS/RS) offers considerable flexibility with respect to throughput capacity in the transfer of unit loads in high density storage areas. AVS/RS relies on autonomous vehicles to provide horizontal movement within a tier and uses lifts to provide vertical movement between tiers. In these systems, vehicle blocking delays in the aisles and cross-aisles could significantly impact system throughput and transaction cycle times. In this research, protocols are developed to address vehicle blocking, and a semi-open queueing network model is proposed to analyze system performance and evaluate design trade-offs. A decomposition-based method is used to solve the queueing network and quantify the effect of blocking. This model is adopted to analyze the effect of varying tier configuration parameters such as number of storage locations, depth/width ratio, number of vehicles, and vehicle utilization on blocking delays. These insights are useful for design conceptualization using AVS/RS.
Shuttle-based compact systems are new automated multi-deep unit-load storage systems with lifts that promise both low operational cost and large volume flexibility. In this paper, we develop novel queuing network models to estimate the performance of both single-tier and multi-tier shuttle-based compact systems. Each tier is modeled as a multi-class semiopen queuing network, whereas the vertical transfer is modeled using an open queue. For a multi-tier system, the models corresponding to tiers and vertical transfer are linked together using the first and second moment information of the queue departure processes. The models can handle both specialized and generic shuttles, and both continuous and discrete lifts. The accuracy of the models is validated through both simulation and a real case. Errors are acceptable for conceptualizing initial designs. Numerical studies provide new design insights. Results show that the best way to minimize expected throughput time in single-tier systems is to have a depth/width ratio around 1.25. Moreover, specialized shuttles are recommended for multi-tier systems because the higher cost of generic shuttles is not balanced by savings in reduced throughput time and equipment needs.
Autonomous vehicles-based storage and retrieval systems (AVS/RS) have been shown to offer greater flexibility to improve cycle time and throughput capacity in the transfer of unit loads in high-density storage areas of warehouses. In these systems, loads are transferred by autonomous vehicles that move horizontally along aisles and cross-aisles within a tier, while lifts support vertical movement of loads between tiers. For analytical tractability, prior studies on performance evaluation of AVS/RS have ignored the blocking effects of vehicles and lifts while estimating throughput capacity and cycle times. To evaluate the effects of blocking on system performance, this paper first develops protocols to address vehicle blocking that are subsequently incorporated in a detailed simulation model. The blocking effects are studied for a variety of tier configurations that differ in depth/width ratio, number of locations, number of available vehicles, and transaction arrival rates. Results suggest that blocking delays could account for 2%-20% of the transaction cycle times. [Received 17 October 2013; Revised 28 August 2014; Revised 23 December 2014; Accepted 2 April 2015]
Technological innovations in warehouse automation systems, such as Autonomous Vehicle based Storage and Retrieval System (AVS/RS), are geared towards achieving greater operational efficiency and flexibility that would be necessary in warehouses of the future. AVS/RS relies on autonomous vehicles and lifts for horizontal and vertical transfer of unit-loads respectively. To implement a new technology such as AVS/RS, the choice of a design variable setting, interactions among the design variables, and the design trade-offs need to be well understood. In particular, design decisions such as the choice of vehicle dwell-point and location of cross-aisles could significantly affect the performance of an AVS/RS. Hence, we investigate the effect of these design decisions using customized analytical models based on multi-class semi-open queuing network theory. Numerical studies suggest that the average percentage reduction in storage and retrieval transactions with appropriate choice of dwell-point is about 8 percent and 4 percent respectively. While end of aisle location of the cross-aisle is commonly used in practice, our model suggests that there exists a better cross-aisle location within a tier (about 15 percent from end of aisle); however, the cycle time benefits by choosing the optimal cross-aisle location in comparison to the end of aisle cross-aisle location is marginal. Detailed simulations also indicate that the analytical model yields fairly accurate results.
This paper presents stochastic models for multi-tier warehouse systems that handle unit load transactions using autonomous vehicle-based technology. In this system, self-powered autonomous vehicles carry out unit-load transactions by moving along rails within a tier and using lifts or conveyors for vertical movement between tiers. We develop semi-open queuing network models to evaluate congestion effects in processing storage and retrieval transactions in this system. The queuing network is evaluated using a decomposition-based approach. The queuing model provides design insights on the effect of vehicle interference and vertical transfer mechanism on various system performance measures of interest. Insights from such studies can be especially useful during the conceptualization stage of warehouses that use autonomous vehicle technology.
We develop discrete time models for the throughput time distribution of orders arriving to a one-block warehouse. The models accommodate single- or multi-line orders, and we show how to use them to determine the optimal batch size, given a desired probability of on-time order fulfillment. Experiments suggest that the optimal batch size is slightly higher than one would choose if minimizing average throughput time.
Distribution centers have recently adopted Autonomous Vehicle-based Storage and Retrieval Systems (AVS/RSs) as a potential alternative to traditional automated storage and retrieval systems for processing unit-load operations. The autonomy of the vehicles in an AVS/RS provides a level of hardware sophistication, which can lead to the improvements in operation efficiency and flexibility that will be necessary in distribution centers of the future. However, in order to exploit the potential benefits of the technology, an AVS/RS must be designed using a detailed understanding of the underlying dynamics and performance trade-offs. Design decisions such as the configuration of aisles and columns, allocation of resources to zones, and vehicle assignment rules can have a significant impact on the performance of AVS/RSs. In this research, the performance impact of these design decisions is investigated using an analytical model. The system is modeled as a multi-class semi-open queuing network with class switching and a decomposition-based approach is developed to evaluate the system performance and obtain insights. Numerical studies provide various insights that could be useful in the design conceptualization of AVS/RSs.
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.
Automated storage and retrieval systems (AS/RSs) and autonomous vehicle storage and retrieval systems (AVS/RSs) are two competing technologies for the handling, storage and retrieval of unit-loads in the reserve section of an automated warehouse. In this paper, we model variants of the two systems as an open queuing network (OQN) and use an existing tool for the analysis of OQNs, called the manufacturing system performance analyser (MPA), for analysing the performance of the AS/RS and AVS/RS. Experimental results are provided to show that MPA is a better choice than simulation to quickly evaluate alternate configurations of the two systems. We use MPA to answer a series of design questions for AS/RS and AVS/RS design conceptualisation.
We propose a new method for the performance evaluation of Open Queueing Networks with a Population Constraint (represented by a set of tokens). The method is based on the application of Approximate Mean Value Analysis (AMVA) algorithms. We present procedures for single class networks and for multiple class networks, subject to either a common constraint (shared tokens) or to classbased constraints (dedicated tokens). In fact, the new method is a unified framework into which all procedures for the different types of networks fit. We show how the new method relates to wellknown methods and present some numerical results to indicate its accuracy.
Computationally efficient design conceptualization models are proposed for automated unit load storage and retrieval systems based on autonomous vehicle technology. Vehicle and lift travel times and the probability distribution for 12 service scenarios occurring under realistic operating assumptions are formulated and used to generate expected transaction service times. Additional measures of system performance including transaction waiting time and vehicle utilization are formulated for systems using random storage and point-of-service-completion dwell point rules. The models provide a practical means of predicting key aspects of system performance based on five design variables that drive the majority of system costs. They are illustrated in the context of a conceptualization study adapted from an actual system installation.
A network queuing approach is used to estimate performance measures for Autonomous Vehicle Storage and Retrieval (AVS/R) Systems using opportunistic interleaving. The model exploits the distribution of cycle types and random storage assumptions to estimate the proportion of single and dual command cycles in a system. Comparable in accuracy and computational efficiency to procedures based on standard queuing models, the proposed technique provides the additional advantage of flexibility for modeling the interfaces between a storage system and the overall material flow system in a facility. The procedure is demonstrated for a range of problem scenarios.
The task of coordinating hundreds of mobile robots in one of Kiva System's warehouses presents many challenging multiagent resource allocation problems. The resources include things like inventory, open orders, small shelving units, and the robots themselves. The types of resources can be classified by whether they are consumable, recycled, or scheduled. Further, the global optimization problem can be broken down into more manageable sub-problems, some of which map to (hard) versions of well known computational problems, but with a dynamic, temporal twist. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.
The Kiva warehouse-management system creates a new paradigm for pick-pack-and-ship warehouses that signiftcantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor or two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation or a Kiva system was deployed in the summer of 2006. Copyright © 2008, Association for the Advancement of Artificial Intelligence. All rights reserved.
Is Kiva systems a good fit for your distribution center? an unbiased distribution consultant evaluation
- M Wulfraat
Wulfraat, M. (2012). Is Kiva systems a good fit for your distribution center? an unbiased
distribution consultant evaluation. http://www.mwpvl.com/html/kiva_systems.html.
Mobile fulfillment systems: Model and design insights
- D Roy
- S Nigam
- I J B F Adan
- M B M De Koster
- J Resing
Roy, D., Nigam, S., Adan, I. J. B. F., de Koster, M. B. M., and Resing, J. (2014). Mobile
fulfillment systems: Model and design insights. Working paper.
Amazon unveils its eighth generation fulfillment center
- Business Wire
Business Wire (2015). Amazon unveils its eighth generation fulfillment center. www.
businesswire.com/multimedia/home/20141130005031/en.