Search

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]

Robotisation is increasing in warehouse operations, but human employment continues to be relevant. Traditionally manual activities, such as order picking, are being re-designed into collaborative human–robot tasks. This trend exemplifies the transition towards a human-centric Industry 5.0, focusing on synergy instead of seeking human replacement. However, human workers are increasingly hard to recruit and retain. We contribute to the underrepresented literature on human factors within the domain of operations and production management research and investigate the deployment of robotic technologies alongside human workers in a sustainable way. With a unique real-effort experiment, we investigate how the manipulation of picker's experienced levels of autonomy affects their job satisfaction and core self-evaluations, two key behavioural outcomes that determine employee turnover intentions. We establish that the introduction of human–robot collaboration positively affects job satisfaction for the contrasting collaboration dynamics of (i) gaining control (the human leading the robot) and (ii) ceding control (the human following the robot). This positive effect is larger when the human is following the robot. We additionally find that following the robot positively affects pickers' self-esteem and that self-efficacy related to human–robot interaction benefits from the introduction of collaborative robotics, regardless of the setup dynamics. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Enabled via recent technological advances coupled with the advent of new systems providers and decreased price points, automated and robotized order-picking solutions (e.g., pick-assisting autonomous mobile robots) have evolved as a surging market. Such innovative picking technologies aim to reduce labor costs, use available space more efficiently, and increase throughput rates. As implementation projects and the variety of solutions rise, managers decide which ones to select for their specific warehouse and products. However, comprehensive decision models for this strategic problem are lacking in the pertinent literature. We propose a mathematical optimization model for the novel problem that selects and sizes order-picking solutions and assigns them products and warehouse spaces. Expert interviews are used to identify the comprehensive decision-relevant costs and constraints. Specifically, we minimize setup, module, labor, and error costs while adhering to characteristics related to the area (e.g., available space), technology (e.g., throughput, handling capabilities of certain products), and product (e.g., physical dimensions). We conduct a case study and complement our findings with numerical experiments. We find significant cost reduction potential of up to 57% by selecting a mix of different order-picking solutions. Further analyses highlight the need to retain human workers and to account for maximum labor capacity. [ABSTRACT FROM AUTHOR]

Warehouse operations have been significantly improved because of the rapid advancement of cyber-physical system technologies, preparing for Work-of-the-Future. The challenge, however, is how to design a collaborative system to deliver optimal performance by multiple agents who are highly distributed but interconnected and operate with technologies that provide massive amounts of real-time data. To address the challenge, in this article, the Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse is developed to minimise total operation cost and time. The problem is addressed in two phases; the Global and Local phases. The global phase has higher computational power, maintaining a mathematical model, while the local phase has limited computational power and time, utilising heuristics to deliver the outcome. Computer experiments are utilised for validating the designed protocol compared with other alternatives. The results show that, in all given scenarios, the newly designed protocol outperforms alternatives with statistical significance. The original contribution of this research is the design and control of Cyber Collaborative Warehouse operations with a new focus on collaborative multi-agent interactions. In addition, a major implication is that future warehouses can benefit competitively by operating with Task Administration Protocols such as the new Cyber Collaborative Protocol for Dual-Cycle Task in Future Warehouse. [ABSTRACT FROM AUTHOR]

Third-party logistics (3PL) services are essential in many supply chains and aim at providing flexible and cost-effective transport and warehouse solutions. This study focuses on Warehouse Service Providers (WSPs) with shared, multi-client warehouses. We investigate how the main internal resources should be shared among clients. We focus on labor (order pickers and sorters) and pick storage space (zones) as the main internal resources in the order picking process in such a warehouse. We evaluate different possible resource-sharing configurations and develop a model to find the configuration that can satisfy set service-level agreements at minimum cost. The cost consists of labor and space cost, whereas the service-level requirements are based on the mean and tail of the order-throughput-time distribution. The results show that whether to share the resources or not highly depends on the clients' characteristics, such as the order demand rate. Generally, for low-demand clients, sharing all the resources is optimal, whereas for high-demand clients, dedicating all resources is preferred. In addition, the decision to share resources depends on the warehouse's characteristics, similarities between clients' product and order picking characteristics. The findings of this article provide valuable insights for WSPs to minimize cost, while meeting the service-level requirements of clients by optimally selecting the warehouse configuration. This may, in turn, benefit their clients by offering better pricing policies to them, which can also be a compelling selling point, ultimately contributing to business growth and attracting more clients. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Purpose: This research investigates how to optimize storage location assignment to decrease the order picking time and the waiting time of orders in the staging area of low-temperature logistics centers, with the goal of reducing food loss caused by temperature abuse. Design/methodology/approach: The authors applied ABC clustering to the products in a simulated database of historical orders modeled after the actual order pattern of a large cold logistics company; then, the authors mined the association rules and calculated the sales volume correlation indices of the ordered products. Finally, the authors generated three different simulated order databases to compare order picking time and waiting time of orders in the staging area under eight different storage location assignment strategies. Findings: All the eight proposed storage location assignment strategies significantly improve the order picking time (by up to 8%) and the waiting time of orders in the staging area (by up to 22%) compared with random placement. Research limitations/implications: The results of this research are based on a case study and simulated data, which implies that, if the best performing strategies are applied to different environments, the extent of the improvements may vary. Additionally, the authors only considered specific settings in terms of order picker routing, zoning and batching: other settings may lead to different results. Practical implications: A storage location assignment strategy that adopts dispersion and takes into consideration ABC clustering and shipping frequency provides the best performance in minimizing order picker's travel distance, order picking time, and waiting time of orders in the staging area. Other strategies may be a better fit if the company's objectives differ. Originality/value: Previous research on optimal storage location assignment rarely considered item association rules based on sales volume correlation. This study combines such rules with several storage planning strategies, ABC clustering, and two warehouse layouts; then, it evaluates their performance compared to the random placement, to find which one minimizes the order picking time and the order waiting time in the staging area, with a 30-min time limit to preserve the integrity of the cold chain. Order picking under these conditions was rarely studied before, because they may be irrelevant when dealing with temperature-insensitive items but become critical in cold warehouses to prevent temperature abuse. [ABSTRACT FROM AUTHOR]

Since the release of the first warehouse management system (WMS), these systems have been systematically developed and improved to optimize warehouse operations, including inventory management, order fulfillment, receiving, picking, packing, and shipping. Among the many well-documented and well-known WMS systems, there are other systems with a similar level of maturity and comparable functionality, but not as popular, but still very appealing. Such a system is Pinquark, the flagship product of one of the leading Polish software vendors. In this sense, the aim of this article is to present its main features and discuss its further development. In particular, we introduce and outline the idea of reorienting warehouse management from a process-based to an activity-based approach. In this context, we discuss the preliminary results obtained through the study of contemporary literature, including methods addressing vehicle routing and product allocation problems. [ABSTRACT FROM AUTHOR]

Purpose: The digital warehouse management system is an emergence that forms a critical part of the transformation of economic structure in Industry 4.0. In the present business scenario, the warehouse management system encounters a messy layout, poor damage control, unsatisfactory order management, lack of visibility and lack of technological interventions. Digital twin (DT) based warehouse system shows the ontology and knowledge graphs for competitive advantage by consolidating and transferring goods directly from an inbound supplier to an outbound customer on short notice and with no or limited storage. There remains a lack of clarity on how the DT can be implemented successfully in warehouse management. Design/methodology/approach: The current literature remains largely unstructured and scattered due to a lack of a systematic approach to integrating the research implications and analysis. This paper probes the conceptualization of the DT with the help of theoretical analysis using the systematic literature analysis method. Findings: The study explores essential concepts such as interoperability and integrability in implementing DT. Further, it analyzes the role of a supply chain control tower (SCCT) in modern supply chain management. A research framework is proposed for practitioners and academicians by incorporating the opportunities and challenges associated with DT implementation. The research findings are mainly threefold: Conceptualization of DT, Featuring SCCT and Exploration of cross-computer platform interfaces, scalability and maintenance strategies. Originality/value: This study is among the first to analyze and review DT applications in warehouse management. Moreover, the study proposes a theoretical toolbox for the practitioners to successfully implement the DT in warehouse DT-based warehouse management system: A theoretical toolbox for future research and applications. [ABSTRACT FROM AUTHOR]

Warehouses recently face increasing stress imposed by a volatile customer demand and increasing customer expectations in terms of ever shorter order response times. In that respect, warehouses more and more offer same-day and next-day shipment conditions. However, same-day shipment promises are challenging to fulfil, especially as the order fulfilment process operates against fixed deadlines imposed by the predefined truck departure times. As a natural mitigation strategy, warehouses set a cutoff point and offer same-day shipment only to customers that order until the cutoff point, but next-day shipment to all customers ordering thereafter. Setting an appropriate cutoff point is challenging as it affects multiple facets of the service quality, such as the order response time and the service level. In this paper, we study the design of cutoff-based shipment promises for stochastic deadline-oriented order fulfilment processes in warehouses. We present a discrete-time Markov chain model for exact steady-state performance analysis and propose two novel performance measures – α - and β - cutoff service level – for service level measurement in these systems. We numerically show the benefit of cutoff-based shipment promises. Even with a late cutoff point, there is a significant gain in the system performance. Furthermore, we find that warehouses should set the cutoff point such that it balances customer expectations in terms of service level and order response time. Finally, warehouses can improve their shipment promises when referring to β - instead of α - cutoff service level and by implementing measures reducing the utilisation and the variabilities of the order fulfilment process. [ABSTRACT FROM AUTHOR]

The most labor-intensive and time-consuming part of warehouse operations is order picking. This paper proposes a correlation-based storage location assignment (CBSLA) approach to minimize the travel distance of the picker in a picker-to-parts warehouse. At first, the proposed CBSLA approach forms some groups of stock-keeping units (SKUs) for different warehouse aisles. Then these groups of SKUs are assigned to the storage locations considering both the correlations between SKUs in a group and the correlation between groups of SKUs for efficient order picking. The effectiveness of the proposed method is measured for various warehouse configurations using simulation and compared with other well-known storage allocation methods. [ABSTRACT FROM AUTHOR]

This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity. [ABSTRACT FROM AUTHOR]

An intensified competition forces warehouses to handle more orders in shorter time windows. This complicates the timely retrieval of these customer orders. Planning order picking operations, thereby aiming to increase efficiency, inevitably results in balancing concerns, such as imbalances among pick areas, pickers or time periods. Reducing workload imbalances, therefore anticipating on workload peaks, results in a more stable order picking process. However, there exist several measures that can be used to evaluate and correct existing imbalances. This study contributes to academic literature by analysing, explaining and evaluating the effectiveness of various workload balancing approaches (e.g. Rawlsian's approach, range, mean-based) in order picking operations, more specifically in the context of balancing workload over time in case of restricted time windows for retrieving customer orders. Results show that the effect of warehouse layout characteristics and customer order parameters on the effectiveness of balancing measures is very limited. However, the underlying managerial reason (e.g. workforce allocation, transportation schedule or human well-being) for solving the operational workload balancing problem does significantly impact the effectiveness and choice of an appropriate balancing measure. [ABSTRACT FROM AUTHOR]

This study aimed to enhance the efficiency of the order picking process in Carclasse's parts warehouse in Braga to achieve this objective, a comprehensive analysis of all warehouse processes was conduct. The initial analysis revealed that approximately 59% of the references in stock were considered obsolete, as they had had zero turnover in the last 5 years. Additionally, a high error rate was identify in the order picking process, with around 6% of references being pick incorrectly. These errors led to discrepancies between the physical stock levels and the system records since some order picking errors went undetected. Moreover, queues were observed among internal customers when requesting material retrieval, which hindered workflow. Based on these findings, improvements were proposed to address the identified issues. One of the key proposals was the implementation of a Barcode scanning system to increase the digitalization of warehouse processes and rectify order picking and stock errors. Additionally, to reduce internal customers' waiting time, it was suggested to shift the responsibility of part identification for collection to mechanics and increase from 2 to 4 the number of identification stations. It is crucial to highlight that the implementation of the Barcode scanning system can not only reduce order-picking errors but also contributed to the overall optimization of Carclasse's parts warehouse. With the digitalization of processes, it is expected an enhancement in traceability and inventory accuracy, avoiding discrepancies between physical stock and system records. This can resulted in greater control and reliability of inventory data, providing a solid foundation for operational and strategic decision-making. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has changed people's shopping habits, causing them to turn to online shopping sites, especially on the internet. In the face of increasing order rates, it has become inevitable for manufacturers and suppliers to produce innovative solutions for collecting orders and delivering them on time. Companies/suppliers who had to use a limited number of manpower due to the pandemic have tried to solve this problem with the use of machinery. In this study, a new low-cost Order Picking and Distribution System application developed for the stages of order picking and distribution in the supply chain is described. This stage of the supply chain is the most problematic, time-consuming and costly. In general, more human resources are used in the proposed solutions. With this application, it is aimed to create a more efficient system in terms of time, manpower, warehouse space and cost. For this purpose, a prototype Order Picking and Distribution System application was designed, manufactured and implemented in real time. [ABSTRACT FROM AUTHOR]

In the paper, we solved the problems regarding the improvement of the production process of allocating material to the inlet of a production hall using the Plant Simulation software in a selected company. The solved problematics are high costs for the shipping company, elimination of the necessity to transfer the semi-finished product from the external warehouse to the production hall and non-effective layout. We deal with the characteristics of the current state of the production process in the production company and create its model in Plant Simulation. Thereafter we propose solutions that can make that process more efficient. The software enables the comparison of complex production alternatives. [ABSTRACT FROM AUTHOR]

This paper studies the fire performance of steel warehouse with their main elements insulated with fire protection boards composed of gypsum‐based mortars with perlite using the heat transfer and thermomechanical analysis in finite element (FE) software. The primary aim is to identify the optimal thickness of the fire protections boards based on the assigned fire model in thermal analysis. Besides that, a parametric study is also conducted by considering various factors that are often overlooked or not explicitly mentioned in fire design guidelines, such as EN 1991‐1‐2. The findings facilitate the development of optimized fire design approaches, ensuring the structural integrity and safety of steel warehouses under fire conditions. [ABSTRACT FROM AUTHOR]

Route planning for automated guided vehicles (AGVs) is one of the key factors that affects work efficiency of automated storage and retrieval systems (AS/RSes). Route planning plays an important role in the operation of AGVs. Since the characteristic of AS/RSes is chessboard-like, the environment is more complex than traditional route planning environments because the number of nodes is large, more than one shortest route exists between two nodes, and the routes with the shortest distance may not be the most energy-saving routes. Although the traditional route planning algorithms such as the classical Q-learning algorithm can work well in AGV route planning, it also has some limitations. This paper proposes a novel multi-AGV route planning approach to solving the AGV route planning problem in the chessboard-like warehouse, which can improve the route planning efficiency greatly. First, by combining adjacency matrix and reward matrix, we propose a low-dimensional adjacency-reward matrix for route planning. This algorithm improves the efficiency of classical Q-learning algorithms and accelerates dynamic route planning significantly. We further improve the algorithm by considering the travel directions to minimize the number of turns in the route and additionally by considering whether the AGV is loaded or not and plan routes accordingly. Finally, we propose a multi-AGV online collision-free route planning algorithm based on these considerations for dynamic route planning for multi-AGVs operating in a large-scale warehouse. The proposed algorithms are validated with several case studies. [ABSTRACT FROM AUTHOR]

With the rapid development of e-commerce, the orders processed in B2C warehouses are characterised by heterogeneous and small volume. The traditional storage assignment strategies used in the picker-to-parts warehouses do not have advantage any more. In this case, the scattered storage strategy is a good alternative. In this paper, we study a new scattered storage strategy that allows the same product to be placed in multiple storage locations. The correlation between products which reflects how frequently any two products will be ordered together in the same order is considered. The problem is formulated as a 0-1 integer programming model to minimise the weighted sum of distances between the products, with weight being the elements of the correlation matrix. To solve large-scale problems, a GA and a basic PSO algorithm are developed. To improve solution quality, a new PSO algorithm based on the problem characteristic is designed and a hybrid algorithm combing it with GA is proposed. Experiments show that the solutions of these algorithms are close to the optimal solutions for the small-sale problems. For larger problems, the specially designed new PSO greatly improves solution quality as compared to the basic algorithms and the hybrid algorithm makes further improvement. [ABSTRACT FROM AUTHOR]

Warehouse design and planning involve complex decisions on receiving, storage, order picking and shipping products (i.e., stock-keeping units - SKUs) and can affect the performance of entire supply chains. With the advancement of Industry 4.0 and increased data availability, high-computing power, and ample storage capacity, Machine Learning (ML) has become an appealing technology to address warehouse planning challenges such as Storage Location Assignment Problems (SLAP) and Order Picking Problems (OPP) for intelligent warehousing management. This paper presents a state-of-the-art review of ML applied to Warehouse Management Systems (WMS) through the analysis of recent research application articles. A mapping to classify the scientific literature in this new research area, including ML methods, algorithms, data sources and use cases of ML-aided WMS, as well as further research perspectives and challenges, are introduced. Preliminary results suggest that the possible research areas in ML-WMS are still incipient and need to be further explored. [ABSTRACT FROM AUTHOR]

Stocktaking refers to the examination or counting over of materials, supplies, goods or products inside a stockroom, store, warehouse. There are many challenges concerning this important business function, which are even more demanding inside an unstructured large warehouse. Huge variance in size, volume, shape of the stock-keeping units that are stored in bulk without packaging, leads to many emerging problems that necessitate significant research endeavors so as to develop solutions that can automate this process. Nowadays, there are many approaches related to computer vision-based stocktaking in unstructured warehouses with products stored in bulk, which employ several existing technologies namely Deep Learning, Optical Character Recognition, and 3D model retrieval. The paper at hand aims at presenting the i-Count Air concept which outlines the utilization of Unmanned Aerial Vehicles, endowed with cognitive hardware sensors, active vision and an innovative perception and stocktaking software, that will push the boundaries of robotic perception and help automate the inventory counting function in a Warehouse 5.0 environment. [ABSTRACT FROM AUTHOR]

Smart warehouses require software-based decision-making tools to manage the receiving, storing, and picking of products. A major challenge in achieving efficient operations is deciding where to store products associated with incoming orders. The storage location assignment problem (SLAP) is more complex in large-size warehouses due to several functional objectives and numerous possible shelving solutions. This paper introduces an artificial intelligence algorithm that seeks to find an acceptable solution to SLAP with presented linear and nonlinear objective functions. The near-optimal technique exploits basin-hopping and simulated-annealing algorithms to find a solution when considering four functional objectives including worker safety, which has not been optimized using similar approaches. The algorithm is experimentally evaluated, and results demonstrate that reasonablely achieved solutions are comparable to those obtained by well-known existing solvers. Furthermore, the problem could be solved with non-linear objectives which is beyond the commercial solvers' like SCIP capability. [ABSTRACT FROM AUTHOR]

The development of grain storage warehouses has low high-tech coverage because of the special features of grain products. Traditional grain warehouses are facing problems including limited land resources, increased labor costs, and poorer health indicators. Accompanied by the development of information technology and the Internet of Things, automated three-dimensional warehouses have been widely used in industry with the advantages of unmanned, informationized, intensive, high-speed and seamless. In the field of grain warehousing, the finished grain three-dimensional warehouse based on mechatronics, automation, control and logistics is expected to solve the problem of grain warehousing. The traditional grain storage industry will be driven by new technology to ensure national food security. [ABSTRACT FROM AUTHOR]

Copyright of Elektronik Industrie is the property of Hüthig GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A literature review on the order picking process in warehouses is presented for delineating the trends in time of research topics in this field. A total of 269 journal papers published between 2007 and 2022 were retrieved from Scopus. After a methodological classification, descriptive analyses were performed on authors, journals, subject area and top publishing countries. Bibliometric tools were used to map the topics covered by the reviewed studies, categorise them and determine possible relationships. Papers' contents were evaluated in terms of eight categories, including five typical issues of order picking systems, plus three aspects dealing with the characteristics of the application. Insights about the extent to which these aspects have been covered in the literature are derived; relationships between the various aspects of the picking process are also delineated. Suggestions for future research activities are finally deducted, offering researchers and practitioners strong bases for works on order picking systems. [ABSTRACT FROM AUTHOR]

This field study examined the automatic speech recognition (ASR) of voice‐directed computerized systems for order selectors employed in large industrial settings (e.g., fulfillment centers, distribution centers, warehouses, and manufacturing plants). Voice‐directed systems for order selection require selectors to listen to instructions via a headset and speak into a microphone, directing each worker to select products for store orders throughout the facility. Originally, ASR used voice recognition that required "voice enrollment" (voice setup) for each worker plus a trainer's time required as part of the setup. Voice setup generally averaged about 60 min for both the worker and the trainer. Lately, a newer technology now utilizes "speech recognition," which eliminates voice enrollment altogether. This study measured order selector voice setup times between voice recognition and speech recognition in five facilities. In two distribution centers where speech recognition was implemented, all voice setup hours for all order selectors (n = 55) plus the trainer's time were eliminated. This amounted to a total savings of 110 h. Moreover, using speech recognition becomes a recurring saving for each new employee entering the organization. Now the focus of training is shifted from voice setup to immediately training workers to select orders via voice, an ergonomic improvement. [ABSTRACT FROM AUTHOR]

This paper investigates manual order picking, where workers travel through the warehouse to retrieve requested items from shelves. To minimise the completion time of orders, researchers have developed various routing procedures that guide order pickers through the warehouse. The paper at hand contributes to this stream of research and proposes an optimal order picker routing policy for a conventional warehouse with two blocks and arbitrary starting and ending points of a tour. The procedure proposed in this paper extends an earlier work of Löffler et al. (2018. Picker routing in AGV-assisted order picking systems, Working Paper, DPO-01/2018, Deutsche Post Chair-Optimization of Distribution Networks, RWTH Aachen University, 2018) by applying the concepts of Ratliff and Rosenthal (1983. "Order-picking in a Rectangular Warehouse: a Solvable Case of the Traveling Salesman Problem." Operations Research 31 (3): 507–521) and Roodbergen and de Koster (2001a. "Routing Order Pickers in a Warehouse with a Middle Aisle." European Journal of Operational Research 133 (1): 32–43) that used graph theory and dynamic programming for finding an optimal picker route. We also propose a routing heuristic, denoted S*-shape, for conventional two-block warehouses with arbitrary starting and ending points of a tour. In computational experiments, we compare the average order picking tour length in a conventional warehouse with a single block to the case of a conventional warehouse with two blocks to assess the impact of the middle cross aisle on the performance of the warehouse. Furthermore, we evaluate the performance of the S*-shape heuristic by comparing it to the exact algorithm proposed in this study. [ABSTRACT FROM AUTHOR]

The use of simulation and reinforcement learning can be viewed as a flexible approach to aid managerial decision-making, particularly in the face of growing complexity in manufacturing and logistic systems. Efficient supply chains heavily rely on steamlined warehouse operations, and therefore, having a well-informed storage location assignment policy is crucial for their improvement. The traditional methods found in the literature for tackling the storage location assignment problem have certain drawbacks, including the omission of stochastic process variability or the neglect of interaction between various warehouse workers. In this context, we explore the possibilities of combining simulation with reinforcement learning to develop effective mechanisms that allow for the quick acquisition of information about a complex environment, the processing of that information, and then the decision-making about the best storage location assignment. In order to test these concepts, we will make use of the FlexSim commercial simulator. [ABSTRACT FROM AUTHOR]

The fourth industrial revolution focuses on the digitization and automation of supply chains resulting in a significant transformation of methods for goods production and delivery systems. To enable this, automated warehousing is demanding unprecedented vehicle-to-vehicle and vehicle-to-infrastructure communication rates and reliability. The 60 GHz frequency band can deliver multi-gigabit/second data rates to satisfy the increasing demands of network connectivity by smart warehouses. In this paper, we aim to investigate the network connectivity in the 60 GHz millimeter-wave band inside an automated warehouse. A key hindrance to robust and high-speed network connectivity, especially, at mmWave frequencies stems from numerous non-line-of-sight (nLOS) paths in the transmission medium due to various interacting objects such as metal shelves and storage boxes. The continual change in the warehouse storage configuration significantly affects the multipath reflected components and shadow fading effects, thus adding complexity to establishing stable, yet fast, network coverage. In this study, network connectivity in an automated warehouse is analyzed at 60 GHz using Network Simulator-3 (NS-3) channel simulations. We examine a simple warehouse model with several metallic shelves and storage materials of standard proportions. Our investigation indicates that the indoor warehouse network performance relies on the line-of-sight and nLOS propagation paths, the existence of reflective materials, and the autonomous material handling agents present around the access point (AP). In addition, we discuss the network performance under varied conditions including the AP height and storage materials on the warehouse shelves. We also analyze the network performance in each aisle of the warehouse in addition to its SINR heatmap to understand the 60 GHz network connectivity. [ABSTRACT FROM AUTHOR]

In order to establish the design method and process of intelligent storage system for panel custom furniture enterprises, taking a factory with a daily output of 3 000 units as an example, through investigation and analysis of the proportion of direct sales orders and distribution orders, the storage capacity requirements such as the specifications and quantity of pallets and storage places were calculated. The capacity requirements of transfer systems such as transplanters, lifts and stackers were calculated according to the frequency of storage and working hours. According to the quantity and time required, the capacity requirements of loading system such as delivery staging area and loading channel were calculated. The results showed that the method and process established in this study are suitable for hardware, system requirement analysis and design of intelligent storage system of panel custom furniture enterprises, and can provide references for enterprises to scientifically design intelligent storage hardware system. [ABSTRACT FROM AUTHOR]

Smart warehouses are revolutionizing traditional logistics operations by incorporating advanced technologies such as Internet of Things, robotics, and artificial intelligence. In these complex and dynamic environments, control and operation instructions need to be transmitted through wireless networks. Therefore, wireless communication plays a crucial role in enabling efficient and reliable operations. Meanwhile, channel measurements and modeling in smart warehouse scenarios are essential for understanding and optimizing wireless communication performance. By accurately characterizing radio channels, communication systems can be better designed and deployed to meet unique challenges in smart warehouse scenarios. In this paper, we present an overview of smart warehouse scenarios and explore channel characteristics in smart warehouse scenarios. We conducted a measurement campaign for millimeter wave radio channels in smart warehouse scenarios. A vector network analyzer-based channel sounder was exploited to record channel characteristics at 28 GHz. Based on the measurements, large-scale channel parameters, including path loss, root-mean-square (RMS) delay spread, and Rician K factor were investigated. The unique channel characteristics in smart warehouse scenarios were explored. [ABSTRACT FROM AUTHOR]

Panel furniture uses an intelligent management system, combined with the production method of splitting orders by process, to achieve large quantities and large-scale manufacturing, but because of the insufficient and incomplete use of technology, capacity bottlenecks still exist. The problem of rework and replenishment is a long-term problem in furniture production. Under the constraints of existing production rules, the time difference of plates rework forces the original batch of plates to wait, which reduces the efficiency of warehousing. From the perspective of intelligent manufacturing for the optimization of the plates rework process, this study, through on-site observation records and data analysis of the production system, aimed to find short-term solutions and long-term solutions. In the short-term response, the time node for the completion of the replenishment is mainly according to the process regulations, and the plates are packaged into the warehouse after the replenishment is completed in batches. The long-term response strategy is mainly to achieve the interconnection of different production systems to achieve mutual information, and the paperless online operation of the plates rework process increases the subjective initiative of each process to improve the overall efficiency of the plates rework process. [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]

Copyright of Railway Construction Technology is the property of Railway Construction Technology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In this paper, we consider the order picking problem (OPP), which constitutes one of the special cases of the Steiner travelling salesperson problem and addresses the costliest operation in a warehouse. Given a list of items to be picked and their locations in the warehouse layout, the OPP aims to find the shortest route that starts from a depot point, picks all the items in the list, and returns to the depot. This paper fills two important gaps regarding the OPP. First, to the best of our knowledge, we present the first complexity results on the problem. Second, we propose a heuristic approach that makes use of its graph-theoretic properties. Computational experiments on randomly generated instances show that the heuristic not only outperforms its state-of-the-art counterparts in the literature, but it is also robust in terms of changing problem parameters. [ABSTRACT FROM AUTHOR]

Purpose: The assorted piece-wise retail orders in a cosmetics warehouse are fulfilled through a separate fast-picking area called Forward Buffer (FB). This study determines "just-right" size of FB to ensure desired Customer Service Level (CSL) at least storage wastages. It also investigates the impact of FB capacity and demand variations on FB leanness. Design/methodology/approach: A Value Stream Mapping (VSM) tool is applied to analyse the warehouse activities and mathematical model is implemented in MATLAB to quantify the leanness at desired CSL. A comprehensive framework is developed to determine lean FB buffer size for a Retail Distribution Centre (RDC) of a cosmetics industry. Findings: The CSL increases monotonically; however, the results concerning spent efforts towards CSL improvement gets diminished with raised demand variances. The desired CSL can be achieved at least FB capacity and fewer Storage Waste (SW) as it shifts towards more lean system regime. It is not possible to improve Value Added (VA) time beyond certain constraints and therefore, it is recommended to reduce Non-Value Added (NVA) order processing activities to improve leanness. Research limitations/implications: This study determines "just-right" capacity and investigates the impact of buffer and demand variations on leanness. It helps managers to analyse warehouse processes and design customized distribution policies in food, beverage and retail grocery warehouse. Practical implications: Proposed buffering model offers customized strategies beyond pre-set CSL by varying it dynamically to reduce wastages. The mathematical model deriving lean sizing and mitigation guidelines are constructive development for managers. Originality/value: This research provides an inventive approach of VSM model and Mathematical algorithm endorsing lean thinking to design effective buffering policies in a forward warehouse. [ABSTRACT FROM AUTHOR]

• Investigates a joint optimization problem of order picking and delivery. • Includes due dates, multi-picking stations, and multi-regions in the problem. • Develops a MIP model, a relaxed model for the lower bound, and a heuristic procedure. • Reduces the total costs significantly compared with benchmark procedures. • Provides guidance and managerial implications to warehouse logistics management. E-commerce platforms, driven by consumer demand for same-day or even half-day delivery, face challenges such as high order volumes with varying due dates, multiple picking stations, and multiple delivery regions. These challenges may lead to order tardiness and decreased efficiency and responsiveness of warehouse logistics. This study jointly optimizes order picking and delivery in ergonomic picking systems by addressing order assignment, sequencing, picking, and delivery operations while considering due dates and minimizing order tardiness and delivery costs. A mixed integer programming (MIP) model is formulated, and an improved knowledge-guided fruit fly optimization algorithm with the shortest waiting time rule (IKGFOA-SWT) as a heuristic solution procedure is developed to solve the problem. The IKGFOA-SWT comprises three modules including an order assignment and sequencing module using IKGFOA, an order picking module employing the SWT rule, and a batch delivery module balancing the delivery and tardiness costs. Computational experiments are conducted to validate the MIP model and evaluate the performance of IKGFOA-SWT. The benchmark experiment underscores the importance of integrating order picking and delivery operations within a unified scheduling framework rather than treating them as independent optimization tasks. The small-scale experiment verifies the effectiveness of IKGFOA-SWT in finding optimal solutions. The large-scale experiment demonstrates the superiority of IKGFOA-SWT in generating high-quality solutions. This study provides valuable managerial insights and practical tools for the optimization of warehouse logistics operations, and contributes to the new norms of supply chain sustainability and resilience. [ABSTRACT FROM AUTHOR]

In recent years, the increasingly pressing problem for any commercial company is that of being flexible in order to respond to market demands. One of the places where continuous adaptation to market requirements is needed are warehouses. Depending on the needs, the warehouse must be able to redefine its routes for storing and delivering products. FlexSim software can be used for modelling, simulation, and improvement systems such as manufacturing, warehousing, health, material handling, logistics, etc. The aim of this paper is to improve the existing flow in a warehouse. For this, in the first stage, the warehouse is simulated in its existing state in order to be able to see the reception times, the storage mode, the package preparation times as well as the transport times. Based on the data obtained, in the second part, a simulation will be carried out in which the flow in the warehouse will be improved, starting from the different aspects identified in the initial simulation. FlexSim software is used to define the model, simulate and improve the warehouse. [ABSTRACT FROM AUTHOR]

The paper presents a outline of a modern large-area warehouse of an enterprise engaged in the sale of, among others, food products, software, books, automotive components, and many others. The techniques, methods, research and design tools used were described. A control system was designed, its plan and network connections of control devices based on the characteristics of technological documentation. The design of logical structures controlling the conveyor line, network topology configuration and also the safety system was proposed. The performance and errors of the line were checked in the simulation process. The paper also includes the process of commissioning and testing an industrial PLC (Programmable Logic Controller) on a real object, which is a modern warehouse. [ABSTRACT FROM AUTHOR]