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Order picking in warehouses is a labour- and time-intensive logistical process that significantly impacts the efficiency of supply chains. Although technical progress facilitates the automation of specific order picking tasks, human workers remain the primary actors of order picking. Owing to high operating costs associated with manual order picking, its design and management have been increasingly researched for decades. Because manual order picking systems are socio-technical systems, human factors and workers' interaction with technology are essential for operational success. As innovative technologies become increasingly utilised, such as augmented reality or exoskeletons, warehouse managers need to consider the effects of supportive and substitutive technologies on operational outcomes. However, the potentials and obstacles of using technologies in manual order picking require further investigations. Therefore, this study analyses literature content on supportive and substitutive technologies in manual warehouse order picking and investigates the existing state of research in this field. Text mining is employed to enhance the insights regarding the content analysis. Additionally, future research opportunities on the integration of supportive and substitutive technologies are proposed for manual order picking improvement and development of sustainable and human-centered logistics systems, according to the Industry 5.0 vision. [ABSTRACT FROM AUTHOR]

Theoretical background: The literature analysis focuses on the importance of spatial design of warehouses, especially the retrieval zone. Research shows that warehouse layout has a key impact on operational efficiency, especially in manual order picking. The authors discuss various approaches that are mainly aimed at minimizing the average distance traveled by warehouse employees when picking orders. Other studies also include warehouse workers’ movements and operations planning. The authors note that existing research focuses mainly on operational efficiency, ignoring social and environmental aspects. Hence, there is a need to develop a methodology that will take into account these additional aspects of designing a sustainable warehouse. Purpose of the article: The article in the context of scientific research aims to present the methodology of designing a spatial warehouse system in terms of sustainable development. In a practical context, a tool for assessing the effectiveness of sustainable design was developed. Research methods: A case study was chosen as the research method. Observation and interviews with employees were used. These methods allowed for a better understanding of the analyzed issue and occurring phenomena. In addition, the methods used allowed to verify the developed method of spatial design of the warehouse in terms of sustainable development. Main findings: By achieving scientific and practical goals, the article can contribute to promoting sustainable design in business and architectural practice and contribute to building a more sustainable and effective work environment. [ABSTRACT FROM AUTHOR]

In the e-commerce era, efficient order fulfillment processes in distribution centers have become a key success factor. One novel technology to streamline these processes is robot-assisted order picking. In these systems, human order pickers are supported by autonomous mobile robots (AMRs), which carry bins for collecting picking orders, autonomously move through the warehouse, and wait in front of a shelf containing a requested stock keeping unit (SKU). Once a picker has approached a waiting AMR and placed the requested SKU into the respective bin, AMR and picker may separate and move toward other picking positions. In this way, pickers continuously move between different waiting AMRs without having to return to the depot. This paper treats the coordination of multiple AMRs and multiple pickers to minimize the makespan. We present a heuristic method for the deterministic case that can handle the requirements of large e-commerce fulfillment centers and successfully solves instances with more than one thousand picking positions. Based on the obtained solutions, the performance of our picking system is compared with the traditional warehouse setup without AMR support and to another work policy using fixed pairings of picker and AMR per order. We find that largely improved makespans can be expected. In addition, we analyze the effects of stochastic picking times, speed differences between AMRs and pickers, and a zoning strategy. The ripple effect caused by stochastic picking times, in which a single delay may cascade through a tightly synchronized schedule and deteriorate picking performance, can be effectively mitigated by separating the workforce into smaller subgroups. Another important finding is that pickers and AMR should have approximately the same travel speed because slower AMRs deteriorate system performance. Finally, zoning slightly decreases the flexibility of the system and should be used if dictated by organizational reasons. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1207. [ABSTRACT FROM AUTHOR]

This paper reexamines the order picking process in a warehouse facing the challenges that e-commerce brings about and which are characterised by a very large number of small sized orders and returns. Implementing effective storage assignment strategies combined with efficient batching, in this context, is very fundamental to keep the warehouse's responsiveness and order completion times up to the standards. This paper investigates a new storage assignment strategy, initially implemented by a large shoes and footwear wholesaler, to enhance the performance of the order picking process in its warehouses. The impact of this new storage assignment strategy on the performance of the order picking process is analysed via simulation. The performance of the system, measured in terms of total picking time as well as order lead time while taking congestion into account, is compared to the cases where conventional storage assignment strategies are implemented. A full factorial design is set up and the simulation output is statistically analysed. The results of this analysis are reported and thoroughly discussed. Attending to the results, the proposed strategy presents a remarkable potential to shorten total picking travel distances and order completion time and consequently customer satisfaction. [ABSTRACT FROM AUTHOR]

The design of manual-order-picking warehouses is a combination of interdependent decisions with enormous possible varieties in design components. The strong interrelationship between these components, in addition to the dynamic and interconnected stochastic nature of the problem; necessitate the utilisation of a simultaneous simulation-based approach. This study proposes a concurrent simulation-based design of experiments approach for the design of manual-order-picking warehouses. The proposed approach can investigate all possible warehousing design combinations with their stochastic nature and interactions; hence, widening the search for performance improvement. The examined design components include warehouse throughput, size, layout, operational policies and manpower/carts. Furthermore, the presented approach captures the probabilistic nature of all the key warehouse functions of receiving, unloading, put away, storage, preparation and picking and shipping; and evaluates the performance of the studied designs using the cycle time for a stock keeping unit in the warehouse. Statistical analysis of the simulation results showed several interesting findings; horizontal layout was preferable over all other types of layouts and small size warehouses perform better than other large sizes. The study has also recommended using high throughput for traditional layout-small size warehouses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

To improve picking performance, many warehouses apply order batching and/or zoning in their picking areas. The former policy collects multiple customer orders jointly on a picker tour to increase picking density, and the latter partitions the picking area into smaller zones to enable a parallel order processing. Both picking policies require an additional consolidation stage, where bins filled with partial orders arriving from multiple zones are sorted according to customer orders. To connect both stages, a conveyor system is applied on which the picked products, each being a piece of a specific Stock Keeping Unit (SKU), move from the picking area toward the consolidation stage. If multiple pieces of the product sequence, approaching the consolidation area on the conveyor, refer to the same SKU, these products are interchangeable among customer orders, and our product-to-order assignment problem arises: Given a product sequence where each product refers to some SKU, we assign products to customer orders, such that demands are fulfilled and order-related objectives, e.g., the sum of completion times, are optimized. We investigate different objectives for this very basic optimization task and show that some problem versions are solvable in polynomial time, whereas others turn out to be NP-hard. Furthermore, we provide exact and heuristic solution approaches. By applying these algorithms in a comprehensive simulation study, we show that our product-to-order assignment problem can be an impactful lever to improve consolidation performance. [ABSTRACT FROM AUTHOR]

Warehouse order picking activities are among the ones that impact the most the bottom lines of warehouses. They are known to often account for more than half of the total warehousing costs. New practices and innovations generate new challenges for managers and open new research avenues. Many practical constraints arising in real-life have often been neglected in the scientific literature. We introduce, model and solve a rich order picking problem under weight, fragility and category constraints, motivated by our observation of a real-life application arising in the grocery retail industry. This difficult warehousing problem combines complex picking and routing decisions under the objective ofminimising the distance travelled. We first provide a full description of the warehouse design which enables us to algebraically compute the distances between all pairs of products. We then propose two distinct mathematical models to formulate the problem. We develop five heuristic methods, including extensions of the classical largest gap,mid-point, S-shape and combined heuristics. The fifth one is an implementation of the powerful adaptive large neighbourhood search algorithm specifically designed for the problem at hand. We then implement a branch-and-cut algorithm and cutting planes to solve the two formulations. The performance of the proposed solution methods is assessed on a newly generated and realistic test bed containing up to 100 pickups and 7 aisles. We compare the bounds provided by the two formulations. Our in-depth analysis shows which formulation tends to perform better. Extensive computational experiments confirm the efficiency of the ALNS metaheuristic and derive some important insights for managing order picking in this kind of warehouses. [ABSTRACT FROM AUTHOR]

Humans are at the heart of crucial processes in warehouses. Besides the common economic goal of minimising cycle times, we therefore add in this paper the human well-being goal of minimising workers' discomfort in the context of order picking. We propose amethodology for identifying the most suitable storage location solutions with respect to both goals. The first step in our methodology is to build data-driven empirical models for estimating cycle times and workers' discomfort. The second step of the methodology entails the use of these empirically grounded models to formulate a bi-objective assignment problem for assigning products to storage locations. The developedmethodology is subsequently tested on two actual warehouses. The results of these practical tests show that clear trade-offs exist and that optimising only for discomfort can be costly in terms of cycle time. Based on the results, we provide practical guidelines for taking storage assignment decisions that simultaneously address discomfort and travel distance considerations. [ABSTRACT FROM AUTHOR]

We consider a semi-open queueing network (SOQN), where one resource from a resource pool is needed to serve a customer. If on arrival of a customer some resource is available, the resource is forwarded to an inner network to complete the customer's order. If no resource is available, the new customer waits in an external queue until one becomes available ("backordering"). When a resource exits the inner network, it is returned to the resource pool. We develop a new solution approach. In a first step we modify the system such that new arrivals are lost if the resource pool is empty ("lost customers"). We adjust the arrival rate of the modified system such that the throughputs in all nodes of the inner network are pairwise identical to those in the original network. Using queueing theoretical methods, in a second step we reduce this inner network to a two-station system including the resource pool. For this two-station systems, we invert the first step and obtain a standard SOQN which can be solved analytically. We apply our results to storage and delivering systems with robotic mobile fulfilment systems (RMFSs). Instead of sending pickers to the storage area to search for the ordered items and pick them, robots carry shelves with ordered items from the storage area to picking stations. We model the RMFS as an SOQN to determine the minimal number of robots. [ABSTRACT FROM AUTHOR]

To reduce unproductive picker walking in traditional picker-to-parts warehousing systems, automated guided vehicles (AGVs) are used to support human order pickers. In an AGV-assisted order-picking system, each human order picker is accompanied by an AGV during the order-picking process. AGVs receive the picked items and, once a picking order is complete, autonomously bring the collected items to the shipping area. Meanwhile, a new AGV is requested to meet the picker at the first storage position of the next picking order. Thus, the picker does not have to return to a central depot and continuously picks order after order. This paper addresses both the routing of an AGV-assisted picker through a single-block, parallel-aisle warehouse and the sequencing of incoming orders. We present an exact polynomial time routing algorithm for the case of a given order sequence, which is an extension of the algorithm of Ratliff and Rosenthal [Ratliff HD, Rosenthal AS (1983) Order-picking in a rectangular warehouse: A solvable case of the traveling salesman problem. Oper. Res. 1(3):507–521], and a heuristic for the case in which order sequencing is part of the problem. In addition, we investigate the use of highly effective traveling salesman problem (TSP) solvers that can be applied after a transformation of both problem types into a standard TSP. The numerical studies address the performance of these methods and study the impact of AGV usage on picker travel: by using AGVs to avoid returns to the depot and by sequencing in (near-) optimal fashion, picker walking can be reduced by about 20% compared with a traditional setting. Sharing AGVs among the picker workforce enables a pooling effect so that, in larger warehouses, only about 1.5 AGVs per picker are required to avoid picker waiting. Summary of Contribution: New technologies, such as automatic guided vehicles (AGVs) are currently considered as options to increase the efficiency of the order-picking process in warehouses, which is responsible for a large part of operational warehousing costs. In addition, picker-routing decisions are more and more often based on algorithmic decision support because of their relevance for decreasing unproductive picker walking time. This paper addresses both aspects and investigates routing algorithms for AGV-assisted order picking in parallel-aisle warehouses. We present a dynamic programming routine with polynomial runtime to solve the problem variant in which the sequence of picking orders is fixed. For the variant in which this sequence is a decision, we show that the problem becomes NP-hard, and we propose a greedy heuristic and investigate the use of state-of-the-art exact and heuristic traveling salesman problem solution methods to address the problem. The numerical studies demonstrate the effectiveness of the algorithms and indicate that AGV assistance promises strong improvements in the order-fulfillment process. Because of the practical relevance of AGV-assisted order picking and the presented algorithmic contributions, we believe that the paper is relevant for practitioners and researchers alike. [ABSTRACT FROM AUTHOR]

The order batching problem is a combinatorial optimization problem that arises in the warehouse order picking process. In the order batching problem, the aim is to find groups of orders and picking routes of these groups to minimize distance travelled by the order picker. This problem is encountered especially in manual order picking systems where the capacity of picking vehicle is limited. Solving the order batching problem becomes more important when the size of the problem (e.g. number of storage locations, number of aisles, number of customer orders, etc.) is large. The content of the batch and picking route affect the retrieval-time of the orders. Therefore, an effective batching and routing approach is essential in reducing the time needed to collect ordered items. The main objective of this study is to develop fast and effective metaheuristic approaches to solve the order batching problem. For this purpose, two genetic algorithm based metaheuristic approaches are proposed. The numerical test of the proposed algorithms is performed with generated data sets. The proposed methods are thought to be useful to solve real-life problems in different warehouse configurations. Accordingly, a real case study is conducted in the distribution center of a well-known retailer in Turkey. The case study includes the storage assignment process of incoming products. The results demonstrate that developed algorithms are practical and useful in real-life problems. [ABSTRACT FROM AUTHOR]

The efficiency of warehouses is vital to e-commerce. Fast order processing at the warehouses ensures timely deliveries and improves customer satisfaction. However, monitoring, analyzing, and manipulating order processing in the warehouses in real time are challenging for traditional methods due to the sheer volume of incoming orders, the fuzzy definition of delayed order patterns, and the complex decision-making of order handling priorities. In this paper, we adopt a data-driven approach and propose OrderMonitor, a visual analytics system that assists warehouse managers in analyzing and improving order processing efficiency in real time based on streaming warehouse event data. Specifically, the order processing pipeline is visualized with a novel pipeline design based on the sedimentation metaphor to facilitate real-time order monitoring and suggest potentially abnormal orders. We also design a novel visualization that depicts order timelines based on the Gantt charts and Marey's graphs. Such a visualization helps the managers gain insights into the performance of order processing and find major blockers for delayed orders. Furthermore, an evaluating view is provided to assist users in inspecting order details and assigning priorities to improve the processing performance. The effectiveness of OrderMonitor is evaluated with two case studies on a real-world warehouse dataset. [ABSTRACT FROM AUTHOR]

Order pickers and individual differences between them could have a substantial impact on picking performance, but are largely ignored in studies on order picking. This paper explores the role of individual differences in picking performance with various picking tools (pick by voice, RF-terminal picking and pick to light) and methods (parallel, zone and dynamic zone picking). A unique realistic field experiment with 101 participants (academic students, vocational students and professional pickers) is employed to investigate the influence of individual differences, especially the Big Five personality traits, on picking performance in terms of productivity and quality. The results suggest that (PbV) performs better than RF-terminal picking, and that Neuroticism, Extraversion, Conscientiousness and the age of the picker play a significant role in predicting picking performance with voice and RF-terminals. Furthermore, achieving higher productivity appears to be possible without sacrificing quality. Managers can increase picking performance by incorporating the insights in assigning the right pickers to work with a particular picking tool or method, leading to increased picking performance and reduced warehousing costs. [ABSTRACT FROM PUBLISHER]

Featured Application: The results of the proposed research could be a useful tool in the selection of products classification while planning products allocation in a warehouse. Proposed model, as a developed software, can support the decision and decrease the risk of the inappropriate choice of product location planning. Based on the proposed research and the application of the proposed model Pickup Simulo, we can expect the reduction in the total warehouse costs to be between 10 and 16 per cent. The research results are, therefore, of significant importance for warehouse management. In this paper, a new model supporting decisions about product allocation in an order-picking shelf warehouse is presented. Industry 4.0 pays attention to inciting the processes, self-analysis and self-optimization of the short response time to market changes, and the maximum use of related data. Methods for solving the product allocation problem (PAP) are not enough to meet the requirements of Industry 4.0. The authors present a new approach for solving PAP. The novelty introduced in the model is based on correlated data—products parameters, clients' orders and warehouse layout. The proposed model contains elements of intelligence. The model, after product classification and allocation, analyzes its effectiveness by a simulation of the order-picking process. The application of artificial neural networks (ANN) as a part of the computing model enables the analysis of large data sets in a short time. The presented study has proved the proposed model, both for practical and scientific purposes. Relying on the research results, the total warehouse cost could be reduced by 10 to 16 per cent. With the use of the proposed model, it is possible to predict the effect of future actions before their execution. The model can be implemented in most conventional warehouses to raise the throughput performance of the order-picking process. [ABSTRACT FROM AUTHOR]

Supply Chain Management is based on formulating models and methodologies to improve and optimize operations and internal processes, in order to increase efficiency and response capacity and simultaneously reduce costs. In Supply Chain Management, activities carried out in warehouses and distribution centers are critical to ensure an optimum service level and to obtain significant savings in total logistics costs. Slotting and picking are two fundamental activities in the efficient warehouses operation and management. For this reason, we made in this article an exhaustive bibliographic review on models and methodologies used for optimizing slotting and picking, between 2000 and 2018, identifying indirect applications in other types of industries and research opportunities and trends, considering the factors that influence the flow of materials and stochastic components in inventory planning: dynamic accommodation, route analysis, cluster location methodologies and warehouse division according to the type of products. [ABSTRACT FROM AUTHOR]

Customer order response time and system throughput capacity are key performance measures in warehouses. They depend strongly on the storage strategies deployed. One popular strategy is to split inventory into a bulk storage and a pick stock, or Forward-Reserve (FR) storage. Managers often use a rule of thumb: when the ratio m of average picks per replenishment is larger than a certain factor, it is beneficial to split inventory. However, research that systematically quantifies the benefits is lacking. We quantify the benefits analytically by developing response travel time models for FR storage in an Automated Storage/Retrieval system combined with order picking. We compare performance of FR storage with turnover class-based storage, and find when it pays off. Our findings illustrate that, in FR storage systems where forward and reserve stocks are stored in the same rack, FR storage usually pays off, as long as m is sufficiently larger than 1. The response time savings can go up to 50% when m is larger than 10. We validate these results using real data from a wholesale distributor. [ABSTRACT FROM AUTHOR]

Order picking has often been considered as one of the most labor- and time-intensive tasks in warehouse operations. Among the various planning problems that have to be solved in manual picker-to-parts systems, the routing of the order picker usually accounts for the highest share of the total warehouse operating cost. To minimize the cost of order picking, researchers have developed various routing procedures that guide the order picker through the warehouse to complete given customer orders. For some warehouse layouts such as the chevron warehouse, an optimal routing algorithm has not yet been proposed. This article, therefore, contributes to filling this research gap by developing an optimal order picker routing policy for the chevron warehouse. The optimal routing algorithm proposed in this article is based on the concept of graph theory and utilizes a dynamic programming procedure. In addition, we propose various simple routing heuristics for the chevron warehouse. In computational experiments, the average order picking tour lengths resulting from optimal routing and from the simple heuristics are compared. Moreover, we compare the performance of the chevron warehouse to the conventional two-block warehouse under various conditions using the tour lengths obtained by the optimal algorithms. [ABSTRACT FROM AUTHOR]

Put‐to‐light order picking systems invert the basic logic of conventional picker‐to‐parts systems. Instead of successively visiting the storage positions of the stock keeping units (SKUs) when collecting picking orders, an order picker accompanies successive bins each containing multiple items of a specific SKU along a lane of subsequent orders. Whenever the picker passes an order requiring the current SKU, which is indicated by a light signal, she puts the requested number of items into the bin associated with the order. Such an order picking system is well‐suited if the assortment is not overly large and all orders demand similar SKUs, so that it is mainly applied in distribution centers of brick‐and‐mortar retail chains. This paper evaluates four different setups of put‐to‐light systems, which, during operations, require the solution of different storage assignment and SKU sequencing problems. We formulate these problems, prove computational complexity, and suggest suited solution algorithms. By applying these algorithms in a comprehensive computational study, we benchmark the impact of the four different setups on picking performance. In this way, warehouse managers receive decision support on how to set up their put‐to‐light systems. [ABSTRACT FROM AUTHOR]

The order picking process involves a series of activities in response to customer needs, such as the selection or programming of orders (batches), and the selection of different items from their storage location to shipment. These activities are accomplished by a routing policy that determines the picker sequence for retrieving the items from the storage location. Therefore, the order picking problem has been plenty investigated; however, in previous research, the proposed models were based on demand fulfilling, putting aside factors such as the product weight--which is an important criterion--at the time of establishing routes. In this article, a mathematical model is proposed; it takes into account the product's weight derived from a case study. This model is relevant, as no similar work was found in the literature that improves the order picking by making simultaneous decisions on the storage location assignment and the picker-routing problem, considering precedence constraints based on the product weight and the characteristics of the case study, as the only location for each product in a warehouse with a general layout. [ABSTRACT FROM AUTHOR]

Trolley line picking is a special warehousing system particularly suited to fulfill high-volume demands for heavy stock keeping units (SKUs). In such a system, unit loads of SKUs are positioned along a given path passed by automated trolleys, i.e., carriers hanging from a monorail or automated guided vehicles. Once a trolley reaches a requested SKU, it automatically stops and announces the requested items on a display. This is the signal for an accompanying human picker to put the demanded items onto the trolley. In this way, picking continues until, at the end of the path, the current picking order is complete and the trolley moves onward to the shipping area. Meanwhile, the picker rushes back to meet the subsequent trolley associated with the next picking order. The picking performance of the trolley line system is mainly influenced by the picker's unproductive walking from SKU to SKU during order processing and back to the next trolley when switching to the next order. In this paper, we investigate how the storage assignment of SKUs along the path and the order sequence influence picking performance. Specifically, we explore the positive effect of duplicating SKUs and storing them at multiple positions along the path. We formulate the interdependent storage assignment and order sequencing problems and introduce a decomposition heuristic. Our computational study investigates the solution performance of this procedure and shows that SKU duplication can considerably improve picking performance. [ABSTRACT FROM AUTHOR]

In this study we consider a pick-and-sort order picking system, in which batches of orders are picked simultaneously from different (work) zones by a group of order pickers. After picking, the orders are transported by a conveyor to a next station for consolidation and packing. Packing can only occur when an order has been picked completely. For a given number of workers, each assigned to a single zone, a larger number of zones reduces pick time (since travel time reduces), but increases waiting time for completion at the packing stations, because more partial batches needing assembly arrive at the packing stations. Our aim is to determine the optimal number of zones such that the total (picking and packing) time to complete a batch is minimised. We solve this problem by optimally assigning items to pick routes in each zone. We illustrate the method with data taken from a distribution centre of one of the largest online retailers in The Netherlands. [ABSTRACT FROM AUTHOR]

Increasing productivity and reducing labour cost in order picking processes are two major concerns for most warehouse managers. Particularly picker-to-parts order picking methods lead to low productivity as order pickers spend much of their time travelling along the aisles. To enhance order picking process performance, an increasing number of warehouses adopt the concept of dynamic storage where only those products needed for the current order batch are dynamically stored in the pick area, thereby reducing travel time. Other products are stored in a reserve area. We analyse the stability condition for a dynamic storage system with online order arrivals and develop a mathematical model to derive the maximum throughput a DSS can achieve and the minimum number of worker hours needed to obtain this throughput, for order picking systems with a single pick station. We discuss two applications of dynamic storage in order picking systems with multiple pick stations in series. In combination with simulation modelling, we are able to demonstrate that dynamic storage can increase throughput and reduce labour cost significantly. [ABSTRACT FROM AUTHOR]

This paper presents an optimisation model and its effective solution technique using beam search heuristic for floor-storage warehousing systems. For a floor-storage system, storage can be accessed from the top of stacks only. The objective is to minimise the number of re-handling operations by optimally determining the storage location and by grouping products for each customer that fit a given sequence for receiving and retrieving operations. An integer programming model is formulated and an approximate solution technique based on the beam search method is proposed to solve the problem by incorporating effective heuristics to reduce the search space using future receiving and retrieving requests. The effectiveness of the proposed method is demonstrated for industrial warehousing problems in a steel plant with 58 storage areas involving more than 3000 retrieving operations. The proposed solution method is shown to be more efficient than the traditional branch-and-bound method for solving integer programming problems. [ABSTRACT FROM AUTHOR]

This paper investigates the order-batching problem both as a theoretical problem, including its special variants, and as a practical problem, with possible heuristic solution procedures. It shows that certain special cases or variants of the order batching problem are solvable, while other cases of the problem are NP-hard. It also develops a new Mixed-Integer Programming (MIP) model to obtain near-exact solutions to the problem. The MIP provides good lower and upper bounds to the problem, which prove useful in the investigation of the heuristic solution procedures. The paper identifies and modifies slightly the order batching heuristic, which shows the strongest results in the numerical experiments. Although good solutions are obtained with this particular heuristic, the results indicate that it is computationally intensive. Furthermore, solutions obtained through the MIP model suggest there is still room to obtain even better solutions to the problem via heuristic procedures, although the challenge is to do so without further increasing the computational burden. [ABSTRACT FROM AUTHOR]

In this paper, we consider a newly designed compact three-dimensional automated storage and retrieval system (AS/RS). The system consists of an automated crane taking care of the pallets' movements in the horizontal and vertical direction. A gravity or powered conveying mechanism takes care of the pallets' depth movement in the rack. Our research objective is to analyze the system performance and optimally dimension the system. For single-command cycles, the crane's expected retrieval travel time is the same for gravity and powered conveyors; we give a closed-form expression. From the expected travel time, we calculate the optimal ratio between three dimensions that minimizes the travel time for a random storage strategy. In addition, we derive an approximate travel time expression for dual command cycles for the system with powered and gravity conveyors, respectively, and use it to optimize the system dimensions. Finally, we illustrate the findings of the study by a practical example. [ABSTRACT FROM AUTHOR]

Robotic handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of automated and robotic handling systems, such as shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and operations research modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Because of unique system features (such as autonomous control, flexible layout, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotic warehouse systems will form the next category of warehouses. All vital warehouse design, planning, and control logic, such as methods to design layout, storage and order-picking system selection, storage slotting, order batching, picker routing, and picker to order assignment, will have to be revisited for new robotized warehouses. [ABSTRACT FROM AUTHOR]

E-commerce fulfillment competition evolves around cheap, speedy, and time-definite delivery. Milkrun order picking systems have proven to be very successful in providing handling speed for a large, but highly variable, number of orders. In this system, an order picker picks orders that arrive in real-time during the picking process; by dynamically changing the stops on the picker's current picking route. The advantage of milkrun picking is that it reduces order picking set-up time and worker travel time compared with conventional batch picking systems. This article is the first to study order throughput times of multi-line orders in a milkrun picking system. We model this system as a cyclic polling system with simultaneous batch arrivals, and determine the mean order throughput time for three picking strategies: exhaustive, locally-gated, and globally-gated. These results allow us to study the effect of different product allocations in an optimization framework. We show that the picking strategy that achieves the shortest order throughput times depends on the ratio between pick times and travel times. In addition, for a real-world application, we show that milkrun order picking significantly reduces the order throughput time compared with conventional batch picking. [ABSTRACT FROM AUTHOR]

Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses which are important components of the logistics systems draw attention in accordance with cost reducing efforts. Warehouse management is becoming more critical, as storage and distribution of the ordered items to customers is a cost-intensive process for the companies, and this process affects the customer service level directly. In warehouse order picking systems, order batching problem is encountered when several orders are demanded, and this paper reveals recent developments and particular trends for the order batching problem. This study aims to give a literature review on order batching operations in the framework of order picking process. It also displays current position and importance of the problem among warehouse operations, and presents future research directions based on key findings. The paper examines several research papers and existing reviews about order batching and order picking. Papers are selected after a systematic approach with predetermined search strategy, classified and presented with important characteristics under main headings. [ABSTRACT FROM AUTHOR]

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Sequential zone picking systems are popular conveyor-based picker-to-parts order picking systems that divide the order picking area in work zones. When designing a zone picking system, it is important to know whether the throughput capability of the system can meet customer demand. However, the performance and maximum throughput capability of a zone picking system is largely determined by congestion and blocking that occur at the various conveyor merges in the system. In this paper we develop an analytical model to study the impact of conveyor merges in sequential zone picking systems. Because of finite buffers, blocking, recirculation, and merging, the resulting queueing model does not have a product-form stationary queue-length distribution which makes exact analysis practically infeasible. Therefore, we develop an approximate solution by using an aggregation technique and matrix-geometric methods to study the throughput capability of the system. The model is suitable to support rapid design of complex zone picking systems, in terms of number and length of zones, input and output buffer capacities, and storage allocation of products to zones to meet prespecified performance targets. Comparison of the approximation results to simulation show that for a wide range of parameters the mean relative error in the system throughput is typically less than 5%. The model accurately predicts the loss in throughput due to congestion and blocking at the merges, and can be used to allocate input and output buffer spaces to maximize the throughput capability of the system. [ABSTRACT FROM AUTHOR]

In this study, the authors have investigated warehouse processes to identify critical ones that are wasteful. The aim of this research study was to improve the efficiency of warehouse processes by reducing travel time and cost in replenishment and order picking. To achieve this objective, the authors have proposed a mathematical model and discrete event simulation study. For the simulation model, the Dijkstra algorithm has been selected to schedule forklifts driving and picking vehicles routes in internal transport. According to the extensive simulation analysis approximately 67% of waste could be reduced in warehouses. Of course, this number depends significantly on a warehouse layout, operations and material handling equipment used in warehouses. [ABSTRACT FROM AUTHOR]

Paper presents analysis of the order picking system efficiency under congestion situations. Analysed picking system is manual and organized as picker-to-part system with very narrow aisles. About 150 different variants of the system was taken into consideration. Each of them is characterized by different storage policies (random, class-based and volume-based), number of order pickers and picking strategies (single picking and batch picking). Main attention was paid to picker blocking phenomenon and its impact on order picking efficiency. Authors explain where, when and why such situations may happen. Additionally, they point out how the number of pickers, storage policy and picking strategy impact pickers blocking and picking process efficiency. Due to complexity of presented issue, dynamics and volatility of picking processes, simulation method was implemented. [ABSTRACT FROM AUTHOR]

The aim of this research is to investigate the state of the art in the adoption of order picking systems (OPS) and provide a broad empirical analysis built on a cross-section of 40 Italian distribution or factory warehouses. The analysis is based on multiple case studies. Semi-structured interviews were conducted with management at leading companies operating in different industries, and results are discussed with reference to the literature. The study provides an insight into the types of OPS adopted at distribution or factory warehouses. Results show the OPS type implemented, their automation level, and the main information and communication technology systems in place to support picking activities. The findings offer interesting views on how and where the different types of OPS may be appropriate based on contextual requirements.The paper fulfils an identified need for in-depth case studies on OPS focusing on their adoption level and implementation process (i.e. motivations and barriers to adoption) in warehouses and suggests areas for future research. [ABSTRACT FROM AUTHOR]

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A recent trend in the layout design of unit-load warehouses is the application of layouts without conventional parallel pick aisles and straight middle aisles. Two examples for such designs are flying-V and fishbone designs for single- and dual-command operations. In this study, it is shown that the multi-item order picking problem can be solved in polynomial time for both fishbone and flying-V layouts. These two designs are compared with the traditional parallel-aisle design under the case of multi-item pick lists. Simple heuristics are proposed for fishbone layouts that are inspired by those put forward for parallel-aisle warehouses and it is experimentally shown that a modification of the aisle-by-aisle heuristic produces good results compared with other modified S-shape and largest gap heuristics when items have uniform demand. Computational experiments are performed in order to compare the performances of fishbone and traditional layouts under optimal routing and it is shown that a fishbone design can obtain improvements of around 20% over parallel-aisle design in single-command operations but can perform as high as around 30% worse than an equivalent parallel-aisle layout as the size of the pick list increases. The sensitivity of the results to varying demand skewness levels when volume-based storage is applied is tested and it is shown that unlike the single- and dual-command cases, a fishbone design performs better compared to a traditional design under highly skewed demand as opposed to uniform demand. [ABSTRACT FROM AUTHOR]

This note develops analytical picker blocking models to simply and accurately assess picker blocking in parallel-aisle order picking systems when multiple picks occur at a pick point. The Markov chain--based models characterize the two bounding walking speeds for modeling picker movement: unit walk time and instantaneous walk time. The unit walk time model has a state-space transition matrix that is reduced by a factor of 16 for both narrow-aisle and wide-aisle systems. Additionally, the model improves upon the existing literature by providing a closed-form expression for the narrow-aisle system with instantaneous walk time. Experimental results are provided to demonstrate how picker blocking is influenced by pick density in a variety of scenarios under varying assumptions regarding the maximum number of picks at a pick point. These results broaden those previously presented in the literature, as well as demonstrate the improved efficiency of the proposed model.1 [ABSTRACT FROM PUBLISHER]

Purpose – The purpose of this paper is to study the prevalence of human learning in the order picking process in an experimental study. Further, it aims to compare alternative learning curves from the literature and to assess which learning curves are most suitable to describe learning in order picking. Design/methodology/approach – An experimental study was conducted at a manufacturer of household products. Empirical data was collected in the order picking process, and six learning curves were fitted to the data in a regression analysis. Findings – It is shown that learning occurs in order picking, and that the learning curves of Wright, De Jong and Dar-El et al. and the three-parameter hyperbolic model are suitable to approximate the learning effect. The Stanford B model and the time constant model led to unrealistic results. Practical implications – The results imply that human learning should be considered in planning the order picking process, for example in designing the layout of the warehouse or in setting up work schedules. Originality/value – The paper is the first to study learning effects in order picking systems, and one of the few papers that use empirical data from an industrial application to study learning effects. [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]