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Real-Time Auction Logistics with Iot-Based Cloud Platform: (English)

Real-Time Auction Logistics with Iot-Based Cloud Platform: (English)

          
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About the Book

This dissertation, "Real-time Auction Logistics With IoT-based Cloud Platform" by Xiangtianrui, Kong, 孔祥天瑞, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: Major auction trading service providers have solved technological problems of dealing with millions of simultaneous biddings. Unfortunately, it is challenging to provide logistics that fulfils massive and lumpy demands in auction industry. Dynamic uncertainties make the auction planning, scheduling and execution control more complex. Moreover, the existing centralized approaches are frequently limited in their efficiency due to the large variations of deployed assets and numerous auction parameters to be considered. Meanwhile, Internet of Things (IoT) and cloud technology have been widely used in several industrial applications. These emerging technologies can enable real-time data collection, proactive response actions, as well as improved process visibility. Based on intelligent platform technologies, this thesis is among the first to systematically propose the concept of real-time auction logistics (AL). It is an essential part of perishable supply chain trading that plans, implements and controls the efficient, effective flow and storage of products, services and related information driven by auctions to meet both operators and customers' requirements. A research framework is established to provide guidelines for investigating real-time planning, scheduling and execution control in auction logistics. Four typical scenarios are examined. The first scenario presents cloud-based auction logistics information platform following three-layer standardized architecture while considering auction characteristics. Motivated by an industrial case of flower auction trading, practitioners are confronted with challenges to fully utilize real-time decision-makings to synchronize both material flow and information flow for multiple users. The proposed cloud platform is expected to make auction planning scalable with respect to transient demands by implementing autonomous logistics. Through the integration of IoT and cloud computing technologies, interactive omnichannel auction bidding is established. Heterogeneous physical assets such as auction trolleys could be easily virtualized, traced and tracked, and managed in the cloud. The second scenario demonstrates an adaptive scheduling for perishable goods auction. Following the hybrid flowshop scheduling (HFS) classification, a timely model for trolley loading and auction trading stages is developed. It is an extension of a typical HFS problem with several characteristics. A heuristic-based solution approach is proposed to minimize either makespan or value loss using a set of dispatching rules. The simulation experiments justify that schedulers can flexibly select dispatching rules under various demand patterns and operation time windows, as well as system configurations and trolley sizes. The third scenario discusses a new paradigm of goods-to-person auction execution method using cloud auction robot (CAR). CARs are used to pick up and deliver the auction products according to the given commands in real-time. A scalable CAR-enabled execution system (CARES) is presented to manage logistics workflows, tasks and behaviour of CAR-Agents in handling the real-time events and associated data. The CARES is flexible to cope with different auction mechanisms and processes with high re-configurability. A system prototype is also validated through physical emulations. Experiment results show that the CARES could well schedule the tasks for each robot to minimi


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Product Details
  • ISBN-13: 9781361043622
  • Publisher: Open Dissertation Press
  • Publisher Imprint: Open Dissertation Press
  • Height: 279 mm
  • No of Pages: 200
  • Spine Width: 13 mm
  • Width: 216 mm
  • ISBN-10: 1361043628
  • Publisher Date: 26 Jan 2017
  • Binding: Hardback
  • Language: English
  • Series Title: English
  • Weight: 757 gr


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