Order Picking Research Articles

Human-Centric Order Picking: Performance Prediction and Robot Assignment at a Robotic Fulfilment Center

Human-Centric Order Picking: Performance Prediction and Robot Assignment at a Robotic Fulfilment Center

Efficient Order Batching Optimization Using Seed Heuristics and the Metropolis Algorithm

Order Picking in warehouses is often optimized using a method known as Order Batching, which means that one vehicle can be assigned to pick a batch of several orders at a time. There exists a rich body of research on Order Batching Problem (OBP) optimization, but one area which demands more attention is computational efficiency, especially for optimization scenarios where warehouses have unconventional layouts and vehicle capacity configurations. Due to the NP-hard nature of the OBP, computational cost for optimally solving large instances is often prohibitive. In this paper, we compare the performance of two approximate optimizers designed for maximum computational efficiency. The first optimizer, Single Batch Iterated (SBI), is based on a Seed Algorithm, and the second, Metropolis Batch Sampling (MBS), is based on a Metropolis algorithm. Trade-offs in memory and CPU-usage and generalizability of both algorithms is analyzed and discussed. Existing benchmark datasets are used to evaluate the optimizers on various scenarios. On smaller instances, we find that both optimizers come within a few percentage points of optimality at minimal CPU-time. For larger instances, we find that solution improvement continues throughout the allotted time but at a rate which is difficult to justify in many operational scenarios. SBI generally outperforms MBS and this is mainly attributed to the large search space and the latter’s failure to efficiently cover it. The relevance of the results within Industry 4.0 era warehouse operations is discussed.

Warehouse Order Picking Differences in Bulgarian Enterprises

Warehousing is an important logistics activity related to the management of material flows in the supply chain. To ensure this movement in the warehouses, it is necessary to perform a number of integrated warehouse processes. The order picking process is particularly the most significant, reflecting the whole logistics system. The purpose of the study is to analyze warehousing practices in this field and to define main conclusions. The work presents the results of a survey conducted among Bulgarian trade and manufacturing enterprises, and some of the order picking practices are studied. Based on the data, a number of conclusions are drawn about the practices, especially regarding the application of information systems and technologies. The main focus of the work is the differences between the trade and manufacturing enterprises, as well as their sizes. The main conclusions are related to the correct understanding of the importance of order picking processes and, at the same time, to the insufficient utilization of their potential.

Fuzzy Multicriteria Decision-Making Model Based on Z Numbers for the Evaluation of Information Technology for Order Picking in Warehouses

Order-picking process management is one of the most demanding tasks within the operations of a warehouse system. It is especially evident in companies that have a high intensity of product flows, so the question of increasing the productivity of order picking arises. In this paper, a novel integrated fuzzy MCDM (Multicriteria Decision-Making) model was developed for the evaluation and selection of information technologies for order picking in a warehouse system, which is one of the most important novelties and contributions of the paper. Barcode, pick-to-light, pick-to-voice, and pick-to-vision technologies were evaluated based on IMF SWARA (improved fuzzy stepwise weight assessment ratio analysis) and fuzzy EDAS (evaluation based on distance from average solution) based on Z numbers. IMF SWARA-Z was applied to determine the importance of four criteria while the information technologies for order picking were evaluated with the fuzzy EDAS-Z method. The averaging of the estimates of the critera and alternatives was performed using the fuzzy Dombi aggregator. The results show that in this particular case under these research conditions, pick-to-vision is the best order-picking technology. Subsequently, validation tests were carried out, and they included the simulation of criteria weights and the impact of the reverse rank matrix.

An Innovative Layout Design and Storage Assignment Method for Manual Order Picking with Respect to Ergonomic Criteria

Background: This paper aims to improve the overall performance of manual warehouse Order Picking (OP) processes by proposing an innovative method for designing a picking area layout, and introducing a storage assignment strategy with respect to ergonomics and workers’ physical fatigue. Methods: The proposed method categorizes the available picking slots based on size and ABC analysis. Ιt takes into consideration a set of ergonomic constraints pertinent to the rack heights and travel distance restrictions for each slot type, leading to the assignment of a location to each slot type based on its individual characteristics. In doing so, the proposed method introduces an innovative ‘flame-shape’ aisle layout. Finally, the products are assigned to their optimal locations, targeting OP time minimization, balanced workload allocation, and ergonomics optimization through a ranking system measuring the ‘difficulty’ of retrieving the products based on their weight, popularity, and slot location. Results: The proposed method led to a productivity rise of 14.9% along with a significant decrease of the ‘difficulty’ index, by 31%. Conclusions: The results prove that a prominent performance improvement can be achieved when both travel distance and manual workload minimization are targeted for determining the picking area layout and storage design.

Dynamic Order Picking Method for Multi-UAV System in Intelligent Warehouse

For the logistics environment, multi-UAV algorithms have been studied for the purpose of order picking in warehouses. However, modern order picking adopts static order picking methods that struggle to cope with increasing volumes of goods because the algorithms receive orders for a certain period of time and pick only those orders. In this paper, by using the modified interventionist method and dynamic path planning, we aim to assign orders received in real-time to multi-UAVs in the warehouse, and to determine the order picking sequence and path of each UAV. The halting and correcting strategy is proposed to assign orders to UAVs in consideration of the similarity between the UAV’s picking list and the orders. A UAV starts picking orders by using the ant colony optimization algorithm for the orders initially assigned. For additional orders, the UAV modifies the picking sequence and UAV’s path in real time by using the k-opt-based algorithm. We evaluated the proposed method by changing the parameters in a simulation of a general warehouse layout. The results show that the proposed method not only reduces completion time compared to the previous algorithm but also reduces UAV’s travel distance and the collapsed time.

Solving an order batching, picker assignment, batch sequencing and picker routing problem via information integration

Modern supply chain systems require an effective scheduling scheme based on industrial information integration. Warehouses are a key part of the supply chain systems. Its efficiency can be significantly improved by making full use of the multi-dimensional information in an order-picking process. This work presents an integrated order batching, picker assignment, batch sequencing, and picker routing problem in a warehouse. Its solution is difficult due to the coupling of various information such as diversity of orders, orderliness of picking positions, and complexity of a warehouse environment. In order to obtain a high-quality solution in a reasonable time, this work proposes a hybrid iterated local search algorithm embedded with heuristic rules. In it, multiple effective perturbation moves with an evolutionary mechanism are designed to allow it to escape from a local optimal region. A perturbation strategy based on its convergence speed is presented to balance exploration and exploitation. An acceptance criterion based on the Metropolis criterion is used to improve its global search ability. Besides, a routing strategy based on Manhattan distance is proposed to evaluate the performance of the proposed method. Finally, various simulation experiments and its comparisons with several existing methods are performed to validate its advantages. The impact of information integration on system performance is also investigated.

Improved formulations of the joint order batching and picker routing problem

Order picking is the process of retrieving ordered products from storage locations in warehouses. In picker-to-parts order picking systems, two or more customer orders may be grouped and assigned to a single picker. Then routing decisions regarding the visiting sequence of items during a picking tour must be made. Won and Olafsson (2005) found that solving the integrated problem of batching and routing enables warehouse managers to organize order picking operations more efficiently compared with solving the two problems separately and sequentially. We therefore investigate the mathematical programming formulation of this integrated problem. We present several improved formulations of the problem based on the findings of Valle, Beasley, and Salles da Cunha (2017), that can significantly improve computational results. More specifically, we reconstruct the connectivity constraints and generate new cutting planes in our branch-and-cut framework. We also discuss some problem properties by studying the structure of a graphical representation of the warehouse, and we present two types of additional constraints. We also consider the no-reversal case of this problem. We present efficient formulations by building different auxiliary graphs. Finally, we present computational results for publicly available test problems for single-block and multiple-block warehouse configurations.

AMR-Assisted Order Picking: Models for Picker-to-Parts Systems in a Two-Blocks Warehouse

Manual order picking, the process of retrieving stock keeping units from their storage location to fulfil customer orders, is one of the most labour-intensive and costly activity in modern supply chains. To improve the outcome of order picking systems, automated and robotized components are increasingly introduced creating hybrid order picking systems where humans and machines jointly work together. This study focuses on the application of a hybrid picker-to-parts order picking system, in which human operators collaborate with Automated Mobile Robots (AMRs). In this paper a warehouse with a two-blocks layout is investigated. The main contributions are new mathematical models for the optimization of picking operations and synchronizations. Two alternative implementations for an AMR system are considered. In the first one handover locations, where pickers load AMRs are shared between pairs of opposite sub-aisles, while in the second they are not. It is shown that solving the mathematical models proposed by the meaning of black-box solvers provides a viable algorithmic optimization approach that can be used in practice to derive efficient operational plannings. The experimental study presented, based on a real warehouse and real orders, finally allows to evaluate and strategically compare the two alternative implementations considered for the AMR system.

An Asymmetric Polling-Based Optimization Model in a Dynamic Order Picking System

The timeliness of order deliveries seriously impacts customers’ evaluation of logistics services and, hence, has increasingly received attention. Due to the diverse and large quantities of orders under the background of electronic commerce, how to pick orders efficiently while also adapting these features has become one of the most challenging problems for distribution centers. However, previous studies have not accounted for the differences in the stochastic characteristics of order generation, which may lead to asymmetric optimization problems. With this in mind, a new asymmetric polling-based model that assumes the varying stochastic characteristics to analyze such order picking systems is put forward. In addition, two important indicators of the system, mean queue length (MQL) and mean waiting time (MWT), are derived by using probability-generating functions and the embedded Markov chain. Then, simulation experiments and a comparison of the numerical and theoretical results are conducted, showing the usefulness and practicalities of the proposed model. Finally, the paper discusses the characteristics of the novel order picking system and the influence of the MQL and MWT on it.

Modelling the Inner Warehouse Shortest Route Planning using Dynamic Programming Block

Fulfilling the customer requirement has always been of utmost concern to logistics service companies, namely those providing warehouse and transportation services. In the warehouse, inner transportation problem affects its performance. Order picker problem is one of the problems that involves the transportation problem within the warehouse. The problem can be handled properly by having proper storage assignment, proper tasking allocation and optimal routing for inner warehouse vehicles' movement. This study proposed a modified Dynamic Programming model to determine the shortest route for the order pickers in completing and fulfilling the customers' orders. The model shows stable solutions for numerous orders.

Energy minimizing order picker forklift routing problem

The material handling systems used in warehouses involve important operations such as the usage of order picker forklifts. These order picker forklifts provide efficient utilization of the storage space by their ability in moving in narrow aisles and picking items from high level racks. Routing the order picker forklifts to pick ordered items belongs to the operational decision level and is done in high frequency. Therefore, finding an energy-efficient route for an order picker forklift can yield significant savings in the energy consumption in warehouses and the resulting CO2 emission. In this paper, we introduce and study the energy minimizing order picker forklift routing problem (EMFRP) which aims to find an energy-efficient route for an order picker forklift to pick a given list of items. To our knowledge, this is the first study that considers the order picker forklifts in the context of the order picking problem. We calculate the forklift’s energy consumption in both horizontal and vertical moves considering the effects of friction forces, the acceleration and deceleration of the forklift, and its load. A mixed integer programming formulation and a dynamic programming approach are developed to solve small size instances of the EMFRP exactly. To solve larger instances, we provide tour construction and tour improvement heuristics and integrate them into a single solution approach. Computational results show that the proposed heuristic approach finds high quality solutions. Moreover, it is observed that significant energy savings can be achieved by solving the EMFRP instead of the classical distance minimization problem.

The impact of monetary incentives and regulatory focus on worker productivity and learning in order picking

PurposeIn manual order picking systems, temporary workers are often employed to handle demand peaks. While this increases flexibility, it may hamper productivity, as they are usually unfamiliar with the processes and may have little experience. It is important for managers to understand how quickly inexperienced workers arrive at full productivity and which factors support workers in improving their productivity. This paper aims to investigate how learning improves the performance of order pickers, and how their regulatory focus (RF) and monetary incentives, as management actions, influence learning.Design/methodology/approachData was collected in two case studies in controlled field-lab experiments and statistically analysed. This allowed evaluating the validity of hypotheses through an ANOVA, the calculation of correlation coefficients and the application of regression models.FindingsA monetary incentive based on total order picking time and pick errors has a positive influence on order picking time, but not on pick quality. The incentive influences initial productivity, but not the learning rate. A dominant promotion-oriented RF increases the effect of the incentive on initial productivity, but it does not impact worker learning.Practical implicationsThis study contributes to behavioral and human-focused order picking management and supports managers in setting up work plans and developing incentive systems for learning and productivity enhancement, considering worker RF.Originality/valueThis work is among the few to empirically investigate the effect of monetary incentives on learning in interaction with RF. It is the first study to investigate these concepts in an order picking scenario.

Integrated Online Order Picking and Vehicle Routing of Food Cold Chain with Demand Surge

This paper focuses on the effect of demand surge on the food cold chain, where orders arrive online. The demand surge has successively affected the order batching, batch sequencing, and route planning, compared to regular demand. This research studies the integrated optimization of food cold chain order picking and vehicle routing of online orders, where mixed integer programming model is formulated to minimize time-consuming and cost. We firstly use K-means++ algorithm to cluster all customers, and then an online batch processing algorithm is designed in each region. Finally, a genetic algorithm is used to complete the joint optimization of the picking and delivery. We use X enterprise’s e-commerce platform as a case to collect actual operating data to verify the effectiveness of the model and algorithm. And comparing the analysis results between phased optimization and integrated optimization, reasonable suggestions are put forward for management decisions.

In pursuit of humanised order picking planning: methodological review, literature classification and input from practice

At the core of every high-performing warehouse is an efficient order picking (OP) system. To attain such a system, policy choices should be carefully aligned with subjects responsible for the actual picking within the established system. Despite recent advancements in automating the picking process due to Industry 4.0, human operators will continue to play a crucial role in the future of warehousing. However, unlike robots, human operators have specific skills, conduct, and perceptions, which are only partly accounted for in current planning models. This review adopts a multimethod approach to identify and analyse how these phenomena are currently integrated into OP planning problems. In addition, we assess the relevance and adequacy of human factors modelling in academic literature with practice-based insights gathered via semi-structured interviews. This leads to five major human factors integration constructs and dedicated recommendations on how to refine them. We then take the analysis one step further and make suggestions on how to integrate these constructs with leading research methodologies in the context of Industry 5.0. The results highlight the prevalent need to increasingly account for psychosocial phenomena and their impact on operational performance. Future research opportunities provide a substantiated foundation to assist in human-centric work design.

Integrated Order Picking and Multi-Skilled Picker Scheduling in Omni-Channel Retail Stores

Utilizing local brick-and-mortar stores for same-day order fulfillment is becoming prominent in omni-channel retailing. Efficient in-store order picking is critical to providing timely value-added omni-channel delivery services. Despite numerous studies on order picking in traditional logistics warehouses and distribution centers, there is scant research focusing on in-store order fulfillment with the multi-skilled workforce in omni-channel retail stores. We studied the integrated Order Picking and Heterogeneous Picker Scheduling Problem (OPPSP-Het) in omni-channel retail stores. We characterized the OPPSP-Het in a mixed-integer linear optimization model with the objective of the minimization of total tardiness of all customer orders. A hybrid heuristic combining the genetic algorithm and variable neighborhood descent was designed to obtain effective solutions. Extensive experiments were conducted to validate the performance of the proposed approach relative to existing algorithms in recent literature. We further numerically showed the effects of order size and heterogeneous workforce on order fulfillment performance. We lastly emphasized the importance of workforce flexibility as a cost-effective approach to improving in-store order fulfillment performance.

The min–max order picking problem in synchronised dynamic zone-picking systems

ABSTRACT In both manual and automated warehouses, a combination of efficient zoning and picker routing plays an important role in improving travel time, congestion, and system throughput. This paper considers the order picker routing problem in a dynamic and synchronised zoning environment, where the items corresponding to each customer order are picked simultaneously in multiple zones, and zones may change between different orders. The objective is to minimise the maximum time of completing the picking activities in any zone. Using a min–max type of objective not only minimises the makespan of an order picking wave, but it also helps balance the workload of the order pickers more effectively. We present a mathematical model for the optimal solution of this problem, as well as a dynamic programming approach to find the optimal solution for the case where a zone is a set of adjacent aisles. Computational experiments on randomly generated instances show that the dynamic programming approach is able to find optimal solutions in negligible computational times.

A variable neighborhood search approach to solve the order batching problem with heterogeneous pick devices

In this paper, we discuss the order batching problem in manual order picking systems. In such systems, order pickers move through a warehouse to collect items that are requested by customers. Customer orders have to be grouped into picking orders of limited size to ensure that the total length of the picker tours to collect all items is minimized. Motivated by real-world restrictions, we assume that the pick devices are heterogeneous, i.e., it is not necessarily possible to transport each customer order with each device. Since the order batching problem is NP-hard, we propose both serial and parallel versions of a variable neighborhood search (VNS) scheme to tackle large-sized problem instances in a reasonable amount of time. Moreover, the VNS scheme is hybridized with a set partitioning problem formulation. Based on computational experiments, we show that the proposed VNS scheme is a very fast approach that is able to provide solutions for benchmark instances with homogeneous pick devices that are of comparable quality or even partly superior to the best performing approaches from the literature. New benchmark instances for heterogeneous pick devices are proposed. High-quality solutions are obtained for these instances. We also consider large-scale problem instances from a real-world e-commerce warehouse. The VNS scheme outperforms the heuristic applied for order batching in this warehouse. Moreover, we demonstrate that a warehouse setup with heterogeneous pick devices is highly beneficial with respect to operating cost savings.

Fast and Faultless? Quantity and Quality Feedback in Order Picking

Many operations are characterized by a trade‐off between speed and quality. This calls for an exploration of effective strategies to balance the two metrics. This study investigates the separate and joint effect of quantity and quality feedback on both performance metrics in order picking. We collected data from two real‐effort experiments with in total 212 participants, who conducted an order picking task in an experimental warehouse. The results of the main experiment show that giving only quantity feedback improves productivity performance, without compromising on quality. No significant effects of quality feedback were identified. Furthermore, combining quantity and quality feedback does not create a synergistic or a weakening effect on both performance dimensions. The second experiment was used as a robustness check in a different setting, and its results show that quantity feedback not only positively impacts productivity, but can also improve quality. We further decompose the aggregate productivity improvement into the separate impact on pickers who perceive different relative positions. We find that the top‐ranked and middle‐ranked workers in their group accelerate less relative to themselves than the bottom‐ranked workers do, and the acceleration gap between the top‐ranked and the bottom‐ranked pickers is smaller when quantity feedback is present. Our findings show that managers can achieve both fast and faultless performance by carefully designing the performance feedback to their employees.

Optimal Picking Policies in E-Commerce Warehouses

In e-commerce warehouses, online retailers increase their efficiency by using a mixed-shelves (or scattered storage) concept, where unit loads are purposefully broken down into single items, which are individually stored in multiple locations. Irrespective of the stock keeping units a customer jointly orders, this storage strategy increases the likelihood that somewhere in the warehouse the items of the requested stock keeping units will be in close vicinity, which may significantly reduce an order picker’s unproductive walking time. This paper optimizes picker routing through such mixed-shelves warehouses. Specifically, we introduce a generic exact algorithmic framework that covers a multitude of picking policies, independently of the underlying picking zone layout, and is suitable for real-time applications. This framework embeds a bidirectional layered graph algorithm that provides the best known performance for the simple picking problem with a single depot and no further attributes. We compare three different real-world e-commerce warehouse settings that differ slightly in their application of scattered storage and in their picking policies. Based on these, we derive additional layouts and settings that yield further managerial insights. Our results reveal that the right combination of drop-off points, dynamic batching, the utilization of picking carts, and the picking zone layout can greatly improve the picking performance. In particular, some combinations of policies yield efficiency increases of more than 30% compared with standard policies currently used in practice. This paper was accepted by Chung Piaw Teo, optimization.