IEEE NSW QLD DL: Resilient Resource Management in Multi-Process Manufacturing Systems

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 The 4th industrial revolution is making on-demand manufacturing a reality, which can offer many advantages. However, such anticipated advantages rely on advanced modelling, optimization and real-time implementation techniques to handle highly dynamic user requirements and operational environment. In this talk, we address the problem of resilient resource management for a typical multi-process manufacturing system, which consists of several parallel production processes. Each process has its own resources such as machines, AGVs, conveyors and storages, and production orders from customers with relevant pre-computed production schedules. Due to either changes of production orders or component faults, frequently some process may be interrupted. To ensure continuous operations, the current practice typically relies on a large number of standby components, resulting in low resource utilization. To ensure fast responsiveness to system changes with low costs, we propose a novel resource sharing strategy that real-time analyzes the resource demand in a target process, and resource flexibilities in other processes, and dynamically assign and schedule flexible resources to the target process to minimize the negative impact of resource scarcity to assigned production jobs, while ensuring jobs in other processes not being affected. A general mixed integer linear programming (MILP) formulation is adopted to capture resource needs and flexibility, and a real-time responsive coordinator is designed to ensure fast resource matching and task re-scheduling in relevant processes. The developed strategy is applied to a simulated manufacturing system with multiple fault scenarios, and the experimental results show the promising potential of our strategy in improving job completion rate, increasing resource utilization and achieving load balance.

Zoom Meeting Details:

Time: Mar 7, 2022, 02:00 PM Canberra, Melbourne, Sydney 
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  • Date: 07 Mar 2022
  • Time: 02:00 PM to 03:00 PM
  • All times are (UTC+10:00) Sydney
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Time: Mar 7, 2022 02:00 PM Canberra, Melbourne, Sydney 
Join from a PC, Mac, iPad, iPhone or Android device: 
    Please click this URL to start or join: https://macquarie.zoom.us/j/87236290542
 
Join from dial-in phone line: 
    Dial: +61 2 8015 2088
    Meeting ID: 872 3629 0542 
    International numbers available: https://macquarie.zoom.us/u/kexUcOsF7Q 

Join from a H.323/SIP room system: 
    Dial: SIP:87236290542@zmau.us
    or 103.122.166.55
    Meeting ID: 872 3629 0542

  • Sydney, New South Wales
  • Australia

  • Starts 01 February 2022 01:00 PM
  • Ends 07 March 2022 02:00 PM
  • All times are (UTC+10:00) Sydney
  • No Admission Charge


  Speakers

Rong Su Rong Su of NTU Singapore

Topic:

Resilient Resource Management in Multi-Process Manufacturing Systems

The 4th industrial revolution is making on-demand manufacturing a reality, which can offer many advantages. However, such anticipated advantages rely on advanced modelling, optimization and real-time implementation techniques to handle highly dynamic user requirements and operational environment. In this talk, we address the problem of resilient resource management for a typical multi-process manufacturing system, which consists of several parallel production processes. Each process has its own resources such as machines, AGVs, conveyors and storages, and production orders from customers with relevant pre-computed production schedules. Due to either changes of production orders or component faults, frequently some process may be interrupted. To ensure continuous operations, the current practice typically relies on a large number of standby components, resulting in low resource utilization. To ensure fast responsiveness to system changes with low costs, we propose a novel resource sharing strategy that real-time analyzes the resource demand in a target process, and resource flexibilities in other processes, and dynamically assign and schedule flexible resources to the target process to minimize the negative impact of resource scarcity to assigned production jobs, while ensuring jobs in other processes not being affected. A general mixed integer linear programming (MILP) formulation is adopted to capture resource needs and flexibility, and a real-time responsive coordinator is designed to ensure fast resource matching and task re-scheduling in relevant processes. The developed strategy is applied to a simulated manufacturing system with multiple fault scenarios, and the experimental results show the promising potential of our strategy in improving job completion rate, increasing resource utilization and achieving load balance.

 

Biography:

Dr Rong Su obtained his Bachelor of Engineering degree from the University of Science and Technology of China in 1997, and Master of Applied Science degree and PhD degree from the University of Toronto in 2000 and 2004, respectively. He was affiliated with the University of Waterloo and Technical University of Eindhoven before he joined Nanyang Technological University in 2010. Dr Su's research interests include multi-agent systems, discrete-event system theory, model-based fault diagnosis, cyber security analysis and synthesis, control and optimization of complex networks with applications in flexible manufacturing, intelligent transportation and green buildings. In the aforementioned areas he has more than 230 journal and conference publications, 1 monograph, 9 granted/filed patents. Dr Su is a senior member of IEEE, and an associate editor for Automatica (IFAC), Journal of Discrete Event Dynamic Systems: Theory and Applications, and Journal of Control and Decision. He was the Chair of the Technical Committee on Smart Cities in the IEEE Control Systems Society in 2016-2019, and currently a co-chair of IEEE RAS TC on Automation in Logistics. Dr Su is  the recipient of 2021 Hsue-shen Tsien Paper Award from IEEE/CAA Journal of Automatica Sinica, a distinguished Lecturer for CCDC 2020, and an IEEE Distinguished Lecturer for IEEE RAS. 





Acknowledgement: Flyer created with the help and inputs from Abby Liu