Browsing by Author "Tang, Jiecheng"

Now showing 1 - 5 of 5
  • Thumbnail Image
    ItemOpen Access
    A holistic approach of Reconfigurable Manufacturing System (RMS) lifecycle
    (Cranfield University, 2019-05-09 10:34) Tang, Jiecheng
    Poster presented at Cranfield University’s 2019 Manufacturing Doctoral Community event.
  • Thumbnail Image
    ItemOpen Access
    A deep reinforcement learning based scheduling policy for reconfigurable manufacturing systems
    (Elsevier, 2021-10-20) Tang, Jiecheng; Salonitis, Konstantinos
    Reconfigurable manufacturing systems (RMS) is one of the trending paradigms toward a digitalised factory. With its rapid reconfiguring capability, finding a far-sighted scheduling policy is challenging. Reinforcement learning is well-equipped for finding highly efficient production plans that would bring near-optimal future rewards. For minimising reconfiguring actions, this paper uses a deep reinforcement learning agent to make autonomous decision with a built-in discrete event simulation model of a generic RMS. Aiming at the completion of the assigned order lists while minimising the reconfiguration actions, the agent outperforms the conventional first-in-first-out dispatching rule after self-learning.
  • Thumbnail Image
    ItemOpen Access
    Multi-objective reconfigurable manufacturing system scheduling optimisation: a deep reinforcement learning approach
    (Elsevier, 2023-11-22) Tang, Jiecheng; Haddad, Yousef; Patsavellas, John; Salonitis, Konstantinos
    Rapid product design updates, unstable supply chains, and erratic demand phenomena are challenging current production modes. Reconfigurable manufacturing systems (RMS) aim to provide a cost-effective solution for responding to these challenges. However, given their complex adjustable nature, RMSs cannot fully unlock their potential by applying old-fashion fixed dispatching rules. Reinforcement learning (RL) algorithms offer a useful approach for finding optimal solutions in such complex systems. This paper presents a framework to train a scheduling agent based on a proximal policy optimisation (PPO) algorithm. The results of a numerical case study that implemented the framework on a simplified RMS model, suggest a good level of robustness and reveal areas of unpredictable behaviour that could be the focus of further research.
  • Thumbnail Image
    ItemOpen Access
    Reconfigurable manufacturing system scheduling: a deep reinforcement learning approach
    (Elsevier, 2022-05-26) Tang, Jiecheng; Haddad, Yousef; Salonitis, Konstantinos
    Reconfigurable Manufacturing Systems (RMS) bring new possibilities toward meeting demand fluctuations while, at the same time, challenges scheduling efficiency. This paper presents a novel approach that, for the scheduling problem of RMS on multiple products, finds a dynamic control policy via a group of deep reinforcement learning agents. These teamed agents, embedded with a shared value decomposition network, aim on minimising the make-span of a constant updating order group by guiding a group of automated guided vehicles to move modules of machine, raw materials, and finished products inside the system.
  • Thumbnail Image
    ItemOpen Access
    Reconfigurable manufacturing systems characteristics in digital twin context
    (Elsevier, 2021-04-14) Tang, Jiecheng; Emmanouilidis, Christos; Salonitis, Konstantinos
    The concept of a reconfigurable manufacturing system (RMS) has been introduced to enable production systems to continuously evolve and respond rapidly to unpredicted and fluctuating market environments. To achieve this goal, RMS needs to exhibit six core characteristics: modularity, integrability, scalability, diagnosability, convertibility and customisation. These characteristics are required to ensure manufacturing systems’ resilience while maintaining productivity and quality. Assessing these characteristics at both the design and operating phase can be aided by the digital twinning (DT) of physical systems. To this end, the DT-RMS concept is introduced in this paper as a dynamic cyber-replica of the physical production environment, enabling a high-level of transparency about data, performance, and relevant reconfiguration decisions. As a result, DT-RMS responds to the need to integrate requirements and performance targets for the RMS characteristics at design and operating-time