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Mao, Y. and Zheng, L., 2019. Design and implementation of port bulk storage management system based on Internet of Things technology. In: Li, L.; Wan, X., and Huang, X. (eds.), Recent Developments in Practices and Research on Coastal Regions: Transportation, Environment and Economy. Journal of Coastal Research, Special Issue No. 98, pp. 62–66. Coconut Creek (Florida), ISSN 0749-0208. This paper builds the overall architecture of the warehouse material management system based on the Internet of Things (IoT) technology, and deploys the radiofrequency identification (RFID) data acquisition system that acts as the IoT perception layer. Furthermore, RFID middleware is structured. By analyzing the current status and management content of warehouse management, the design scheme of the IoT technology warehouse management system is proposed, and the hardware circuit design of the IoT is carried out, which provides ideas for the application of the IoT technology in the warehouse management system of bulk cargo terminals.

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.

The main subject of the article is managing of warehouse flow processes. Various technologies have been presented to support works performed through the entire supply chain. Which is aimed at reducing the time needed from the moment of receipt of the goods to its release. Increasing the efficiency of warehouse operations and reducing the cost of maintaining and operating the facility. The article also introduces the concept of intralogistics as applied to internal systems.

The world has witnessed the digital transformation and Industry 4.0 technologies in the past decade. Nevertheless, there is still a lack of automation and digitalization in certain areas of the manufacturing industry; in particular, warehouse automation often has challenges in design and successful deployment. The effective management of the warehouse and inventory plays a pivotal role in the supply chain and production. In the literature, different architectures of Warehouse Management Systems (WMSs) and automation techniques have been proposed, but most of those have focused only on particular sections of warehouses and have lacked successful deployment. To achieve the goal of process automation, we propose an Internet-of-Things (IoT)-based architecture for real-time warehouse management by dividing the warehouse into multiple domains. Architecture viewpoints were used to present models based on the context diagram, functional view, and operational view specifically catering to the needs of the stakeholders. In addition, we present a generic IoT-based prototype system that enables efficient data collection and transmission in the proposed architecture. Finally, the developed IoT-based solution was deployed in the warehouse of a textile factory for validation testing, and the results are discussed. A comparison of the key performance parameters such as system resilience, efficiency, and latency rate showed the effectiveness of our proposed IoT-based WMS architecture.

Background: Recent literature indicates that warehouse management costs account for a significant portion of overall logistics costs in companies. Warehousing requires the classification, controlling and management of inventory as well as processing of related information. Therefore, adopting efficient and reasonable warehouse management measures to achieve effective management and control of materials is a key means to flexibly adjusting the supply and demand of storage materials and reduce operating costs. There remains a gap in the understanding of benefits and barriers to the full adoption of Industry 4.0 technologies and decision support systems (DSSs) in warehouse management. Methods: This work applies a systematic literature review methodology of recent implementation case studies to analyze documented barriers and benefits of Industry 4.0 technology adoption in warehouse management. For analysis, benefits and barriers are ranked in order of importance using Pareto analysis based on their frequency of occurrence. Results: Improved process efficiency, the availability of real-time data, added competitive advantage and the ability to integrate business activities digitally are the top four most important benefits of implementing Industry 4.0 technologies and decision support systems in warehouse management. The prominent barriers to implementation are high life cycle cost, challenging physical environment/layout, inadequate supporting resource constraints, increased security risk and high energy consumption. Conclusions: Barriers to implementing Industry 4.0 technologies are interrelated in nature and prevent businesses from realizing the full benefit of implemented Industry 4.0 technologies. Adequate financial support, new knowledge and skills are required to be able to ensure the successful implementation of Industry 4.0 in warehousing management.

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.

Purpose – Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The purpose of this paper is to introduce a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light). Design/methodology/approach – Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The present paper introduces a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light. Findings – The proposed approach contributes to the understanding of the performance of different technologies in different application fields; some solutions are more suitable for a low-level warehouse, others bring greater benefits in the case of picking from multilevel shelving. Originality/value – The study concerns an issue that until now has received very little attention in the literature. It compares some traditional solutions with some innovative ones by an economic evaluation. The presented hourly cost function also takes into account the different errors arising and their probability of occurrence.

Technology has the power to affect and shape modern life aspects. Technology has speed up and simplify every task. One of those technologies that we currently use is Barcode. Barcode system can be implemented in the warehouse management system. By using Barcode in every process of the warehouse management system, it helps to minimize human error and provide accurate data in a real-time. Barcode also helps to integrate every process and improves efficiency in the warehouse management system. However, there are lots of warehouses that using manual handling. Therefore, in this research authors would like to discuss about the implementation of the barcode in the warehouse management system to improve warehouse efficiency. This research is conducted by using the qualitative method because this method will explain in depth the process in the warehouse management system as well as data collecting by conducting a semi-structural interview. Authors recommended barcode implementation in the warehouse management system to achieve warehouse efficiency.

This article defines a new dynamic storage assignment problem (DSAP) and develops an integrated mechanism for optimization purpose, based on the ABC classification and mutual affinity of products. A product affinity-based heuristic (PABH)—a technique based on data mining—is developed for calculation of pairwise relationships between products. It is shown that the analytical and multi-parametric DSAP is a quadratic assignment problem (QAP) and thus non-deterministic polynomial-time (NP)-hard. A greedy genetic algorithm (GA) is therefore developed for handling the computational complexity of the DSAP. Performance comparisons between the new approach and the traditional ABC classification method are conducted. The experimental results show that a preferred storage assignment approach is to simultaneously maximize the sum of affinity values and the product of zone indicators and order frequencies based on traditional ABC classification. The experiments on a distribution center of a family care product manufacturer indicate 7.14 to 104.48 % improvement in the average order picking time.

Order picking deals with the retrieval of articles from their storage locations in order to satisfy customer requests. The transformation and consolidation of customer orders into picking orders (batches) is pivotal for the performance of order picking systems. Typically, customer orders have to be completed by certain due dates in order to avoid delays in production or in the shipment to customers. The composition of the batches, their processing times, their assignment to order pickers and the sequence according to which they are scheduled determine whether and the extent to which the due dates are missed. This article shows how Variable Neighborhood Descent and Variable Neighborhood Search can be applied in order to minimize the total tardiness of a given set of customer orders. In a series of extensive numerical experiments, the performance of the two approaches is analyzed for different problem classes. It is shown that the proposed methods provide solutions which may allow order picking systems to operate more efficiently.