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Title: A unified semantic web-enabled framework for intelligent manufacturing supply chain
Author: Saeidlou, Salman
ISNI:       0000 0004 7968 0247
Awarding Body: University of Birmingham
Current Institution: University of Birmingham
Date of Award: 2019
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The importance of well-constructed and efficient information synchronisation is critical to the successful tracking and monitoring of support processes in the context of manufacturing information flow. This project provides an innovative approach to exploring and addressing failure in traditional supply chain systems in the provision of dynamic and scalable information synchronisation. The latter has resulted due to the lack of protocols for the sharing of manufacturing data. This project aims to develop an accessible system whereby manufacturing supply chains can interact semantically in the exchange, interpretation and synchronisation of data. Contemporary semantic web effort in the supply chain field has been limited to the production and process knowledge areas; this project extends this by linking dynamic production data to create production execution knowledge. The novelty of the project is evidenced in the development of a synchronised data sharing system allowing better decision-making resources with intrinsic manufacturing intelligence. By identifying and addressing specific manufacturing challenges to mitigate disturbances within the supply chain, this project will have a significant impact on the proactivity of manufacturers and suppliers. The overall aim of this research is to develop, test and demonstrate efficient creation and re-configuration of intelligent manufacturing supply-chain and manufacturing networks capable of rapid and dynamic response to changes and disturbances. The methodology used for this research mainly focuses on: (i) the development of an intelligent ontology-based data query system; (ii) the development of heuristics and optimisation of manufacturing systems through the use of agent-based technologies; (iii) the application of artificial intelligence techniques for simulating the manufacturing environment; and (iv) the use of intelligent software agents and linked data for predictive manufacturing. A software platform is developed in order to simulate and form a highly adaptive supply chain based on a real industrial use case. The responsive characteristic of the system needs to be proactive, automated and self-regulated. This is realised through the development of a multi-agent system that would deploy autonomous agents, representing online physical systems, with capabilities of negotiation, cooperation, communication, learning and taking action. The results clearly illustrate the validity of the proposed data query, resource allocation and predictive models, when compared to the literature and a set of operation research benchmarks.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: TJ Mechanical engineering and machinery