Abstract
Efficient resource allocation is important in distributed production networks such that the appropriate resources can be allocated to achieve the target output with the desired quality and value. This chapter explores the machine learning approach for dynamic production resource allocation. With the enormous data collected by the Internet of Things (IoT), the hidden knowledge in resource allocation can be discovered by means of the machine learning algorithm. This is considered to be one of the pioneering research studies in IoT-based smart production systems. Given that the Markovian properties of the production network are defined, the artificial neural network, among computational intelligence techniques, is firstly applied for the production configuration determination. Moreover, a reinforcement learning approach is adopted to further exploit the possible self-aware and self-organized production network. This chapter contributes in modeling the next paradigm shift of the production network by investigating advanced production systems with the IoT and artificial intelligence.
Original language | English |
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Title of host publication | Digital Manufacturing |
Subtitle of host publication | Key Elements of a Digital Factory |
Publisher | Elsevier |
Pages | 247-277 |
Number of pages | 31 |
ISBN (Electronic) | 9780443138126 |
ISBN (Print) | 9780443138133 |
DOIs | |
Publication status | E-pub ahead of print - 19 Jan 2024 |
Keywords
- Artificial neural network
- Cyber-physical system
- Distributed production network
- Internet of things
- Reinforcement learning
- Resource allocation
- Smart production system
ASJC Scopus subject areas
- General Engineering