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Lubricant Storage
Published in R. David Whitby, Lubricant Blending and Quality Assurance, 2018
An automated storage and retrieval system (ASRS) consists of a variety of computer-controlled methods for automatically placing and retrieving loads from specific storage locations. ASRSs are typically used in applications where: There is a very high volume of loads being moved into and out of storage.Storage density is important because of space constraints.No value-adding content is present in this process.Accuracy is critical because of potential expensive damage to the products.
Predefined-time bipartite tracking consensus for second-order multi-agent systems with cooperative and antagonistic networks
Published in Journal of Control and Decision, 2023
Yuanhong Ren, Zhiwen Chen, Yong Ji, Zhiwei Li
The collaborative control of multi-agent systems (MASs) has been widely applied in many engineering fields, such as Unmanned Aerial Vehicle (UAV) and Unmanned Ground Vehicle (UGV) formations, Automated Storage and Retrieval System (AS/RS), and Sensor Network Management (see Bendjima & Feham, 2012; Kouloughli et al., 2018; Tran et al., 2020; J. Wang & Xin, 2012; W. Yang et al., 2010; Z. Yang et al., 2021). Consensus is the basis for achieving the collaborative control for MASs. The basic task of solving the consensus problem is to design a suitable centralised or distributed control protocol, so that the state values of some agents reach a certain fixed or time-varying quantity specified in the consensus definition. By following the consensus goals in practical engineering fields, several different kinds of consensus problems of MASs have been summarised, including average consensus (Liu et al., 2012; X. Wang et al., 2018), leader-following consensus (Guan et al., 2012; X. Li et al., 2019; Wen & Zheng, 2019; M. Zhao et al., 2018), cluster consensus (H. X. Hu et al., 2016; Qin & Yu, 2013), etc. Specifically, leader-following/tracking consensus, which was proved to be an energy-saving mechanism in Hummel (1995), is to design controllers so that all the followers can track the leader in a finite time.
A multi-agent and internet of things framework of digital twin for optimized manufacturing control
Published in International Journal of Computer Integrated Manufacturing, 2022
Qingwei Nie, Dunbing Tang, Haihua Zhu, Hongwei Sun
As shown in Figure 5, there are four kinds of equipment in the digital twin shopfloor: warehouse (e.g. automated storage and retrieval system), automated guided vehicle (AGV), machine tools and the digital twin server. The purpose of the multi-agent model is to achieve the scheduling based on real-time data of the shopfloor. The symbols of these agents are respectively AAS/RS (Agent for Automated Storage and Retrieval System), AAGV (Agent for AGV), AM (Agent for a machine tool), and ADT (Agent for digital twin). In addition, a computing server provides ADT with basic computing power. The mapped virtual model is displayed in a specific way, which encapsulates the objective optimization algorithm and perceptively processes system emergencies. Agent for a machine tool (AM)
Exploring warehouse management practices for adoption of IoT-blockchain
Published in Supply Chain Forum: An International Journal, 2023
Shashank Kumar, Rakesh D. Raut, Pragati Priyadarshinee, Balkrishna E. Narkhede
In recent years, technologies and automation have evolved as critical factors for improving supply chain performance (SC), including sourcing, planning, material handling, and reverse logistics (Nitsche, Straube, and Wirth 2021). Although the necessity for automation at every node of the SC has been for years, the emergence of the Industry 4.0 concept has accentuated it (Preindl, Nikolopoulos, and Litsiou 2020). This can be observed in the forecasted growth of automation industries in warehousing sectors, which are expected to grow from USD 15 million in 2015 to USD 30 million by 2026 (Mazareanu 2020). The government and private organisations have begun investing in SC automation specifically for material handling to improve the throughput, responsiveness, visibility, trust, labour cost, and labour shortages (Kumar, Narkhede, and Jain 2021). The use of automated equipment for material handling such as automated storage and retrieval system (AS/RS), intelligent bins, sensor-based AGVs, and robots have increased, which also laying the groundwork for adopting smart technologies such as AI, blockchain, Internet of things (IoT), and machine learning for warehousing operations (Kumar et al. 2021). Among all these equipment and technologies, the IoT-enabled devices are likely to play a significant by providing a platform to communicate and share data. IoT devices will enable real-time tracking and tracing, as well as a controlled and transparent flow of goods and information in an automated warehouse without human interaction (Lee et al. 2017), which further aids in increasing visibility and minimising the loss of goods in the supply chain (SC) network (Zhang et al. 2021). Despite the many advantages of IoT technologies for business processes, it is not recognised as cyber-proof and cannot ensure data integrity at 100% (Lockl et al. 2020). SC organisation in India recently reported the theft of unfinished products, finished inventories, and packing material. Organizations have also reported a rise in fraud in large-scale transactions, reverse logistics, collaboration with third parties, non-contractual business allocation, and vendors’ use of grey market products. Apart from these issues, fraudulent payments to transporters, resource exploitation, fictitious customer creation to increase sales, and incorrect reporting of supply shortages were also recorded during COIVD pandemic (Geschonneck 2020). In light of the growing trend of automation in the warehousing sector, and increased fraud in SC, firms have started reconsidering new warehousing practices, smart technologies, and infrastructure development for forward and reverse logistics. (Gruchmann et al. 2021; Hrouga, Sbihi, and Chavallard 2022; Joshi et al. 2022).