Agent-based distributed manufacturing scheduling: an ontological approach
Research output: Contribution to journal › Article
- School of Mechanical Engineering, University of Birmingham Edgbaston Birmingham B15 2TT UK
- School of Mathematics and Computer Science, Amirkabir University of Technology Tehran Iran
- Center of Infectious Diseases, West China Hospital, Sichuan University, Chengdu, China; Division of Infectious Diseases, State Key Laboratory of Biotherapy, Chengdu, China; Department of Infection Control, West China Hospital, Sichuan University, Chengdu, China. Electronic address: firstname.lastname@example.org.
The purpose of this paper is the need for self-sequencing operation plans in autonomous agents. These allow resolution of combinatorial optimisation of a global schedule, which consists of the fixed process plan jobs and which requires operations offered by manufacturers. The proposed agent-based approach was adapted from the bio-inspired metaheuristic- particle swarm optimisation (PSO), where agents move towards the schedule with the best global makespan. The research has achieved a novel ontology-based optimisation algorithm to allow agents to schedule operations whilst cutting down on the duration of the computational analysis, as well as improving the performance extensibility amongst others. The novelty of the research is evidenced in the development of a synchronised data sharing system allowing better decision-making resources with intrinsic manufacturing intelligence. The multi-agent platform is built upon the Java Agent Development Environment (JADE) framework. The operation research case studies were used as benchmarks for the evaluation of the proposed model. The presented approach not only showed a practical use case of a decentralised manufacturing system, but also demonstrated near optimal makespans compared to the operational research benchmarks.
|Number of pages||23|
|Publication status||Published - 8 Jan 2019|
- multi-agent systems, knowledge –based systems, ontology, distributed systems, metaheuristics