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Ant colony intelligence for flexible manufacturing scheduling in the framework of contract-net protocol

TLDR
In this paper, an agent-based flexible scheduling is built for real-world manufacturing systems, where the allocation of manufacturing resources is achieved through negotiation among the job and machine agents in a multi-agent system (MAS).
Abstract
An efficient agent-based flexible scheduling is built for real-world manufacturing systems in this paper. Considering the alternative processes and alternative machines, the allocation of manufacturing resources is achieved through negotiation among the job and machine agents in a multi-agent system (MAS). Ant Colony Intelligence (ACI) is proposed to be combined with Contract Net Protocol (CNP) so as to make agents adaptive to the changing circumstance. In the framework of CNP, the interactive bidding process is adopted. ACI is integrated into both machine agents and job agents to solve the task allocation and sequencing problem. CNP is introduced to allow the agents to cooperate and coordinate their local schedules in order to find globally near-optimal robust schedules. The negotiation protocol is an interactive bidding mechanism based on the hybrid contract net protocol. The implementation of the issues using CNP model is discussed. Experimental results verify the effectiveness and efficiency of the proposed algorithm integrated with ant-inspired coordination.

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Journal ArticleDOI

Application of Ontology in Event-Driven Job-Shop Scheduling Problems

TL;DR: This paper introduces event driven concept and apply ontologies to Job-Shop scheduling problem FT46 and inference of ontology models allows the system to gain the dynamic information of workshop, and then rule engine is used to match event patterns to optimize the job shop scheduling problem.
References
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Journal ArticleDOI

Application of Ontology in Event-Driven Job-Shop Scheduling Problems

TL;DR: This paper introduces event driven concept and apply ontologies to Job-Shop scheduling problem FT46 and inference of ontology models allows the system to gain the dynamic information of workshop, and then rule engine is used to match event patterns to optimize the job shop scheduling problem.
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