Manufacturing control with a market-driven contract net
Manufacturing control with a market-driven contract net
Intelligent scheduling
The AARIA agent architecture: an example of requirements-driven agent-based system design
AGENTS '97 Proceedings of the first international conference on Autonomous agents
Multiobjective Scheduling by Genetic Algorithms
Multiobjective Scheduling by Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Tabu Search
Simulation Modeling and Analysis
Simulation Modeling and Analysis
Genetic Algorithms
An Architecture for Multi-agent Negotiation Using Private Preferences in a Meeting Scheduler
PRICAI '98 Proceedings of the 5th Pacific Rim International Conference on Artificial Intelligence: Topics in Artificial Intelligence
Dynamic manufacturing scheduling using both functional and resource related agents
Integrated Computer-Aided Engineering
FBS-enhanced agent-based dynamic scheduling in FMS
Engineering Applications of Artificial Intelligence
An iterative agent bidding mechanism for responsive manufacturing
Engineering Applications of Artificial Intelligence
A multi-agent system using iterative bidding mechanism to enhance manufacturing agility
Expert Systems with Applications: An International Journal
Dynamic parts scheduling in multiple job shop cells considering intercell moves and flexible routes
Computers and Operations Research
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Manufacturing scheduling is the process of selecting from alternative plans and assigning manufacturing resources and time to the set of manufacturing processes in the plan. It is an optimization process by which limited manufacturing resources are allocated over time among parallel and sequential activities. This paper presents a new approach by combining genetic algorithms based search and agent-based negotiation for manufacturing scheduling. Since agent-based approaches emphasize on flexibility and responsiveness and genetic algorithms pursue the optimality of solutions, a combination of agent-based approaches and genetic algorithms provides a promising way to enhance the performance of manufacturing scheduling systems. After a brief research literature review, the paper introduces the proposed integration approach and describes the methods and algorithms for implementing a genetic algorithm in an agent-based manufacturing scheduling system. Some experimental results are also presented.