The shifting bottleneck procedure for job shop scheduling
Management Science
Multiagent systems: a modern approach to distributed artificial intelligence
Multiagent systems: a modern approach to distributed artificial intelligence
An agent-based approach for building complex software systems
Communications of the ACM
Introduction to Multiagent Systems
Introduction to Multiagent Systems
The Vision of Autonomic Computing
Computer
Further extensions of FIPA Contract Net Protocol: threshold plus DoA
Proceedings of the 2004 ACM symposium on Applied computing
Negotiation in multi-agent systems
The Knowledge Engineering Review
A survey of multi-agent organizational paradigms
The Knowledge Engineering Review
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Developing Multi-Agent Systems with JADE (Wiley Series in Agent Technology)
Metaheuristics for Scheduling in Industrial and Manufacturing Applications
Metaheuristics for Scheduling in Industrial and Manufacturing Applications
Principles of Sequencing and Scheduling
Principles of Sequencing and Scheduling
Supply chain formation using agent negotiation
Decision Support Systems
International Journal of Computer Integrated Manufacturing
Introducing Trust Establishment Protocol in Contract Net Protocol
ACE '10 Proceedings of the 2010 International Conference on Advances in Computer Engineering
A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems
Applied Soft Computing
IEEE Computational Intelligence Magazine
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
A multi-agent system for distributed multi-project scheduling: An auction-based negotiation approach
Engineering Applications of Artificial Intelligence
Self-Optimization module for Scheduling using Case-based Reasoning
Applied Soft Computing
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Current Manufacturing Systems challenges due to international economic crisis, market globalization and e-business trends, incites the development of intelligent systems to support decision making, which allows managers to concentrate on high-level tasks management while improving decision response and effectiveness towards manufacturing agility. This paper presents a novel negotiation mechanism for dynamic scheduling based on social and collective intelligence. Under the proposed negotiation mechanism, agents must interact and collaborate in order to improve the global schedule. Swarm Intelligence (SI) is considered a general aggregation term for several computational techniques, which use ideas and inspiration from the social behaviors of insects and other biological systems. This work is primarily concerned with negotiation, where multiple self-interested agents can reach agreement over the exchange of operations on competitive resources. Experimental analysis was performed in order to validate the influence of negotiation mechanism in the system performance and the SI technique. Empirical results and statistical evidence illustrate that the negotiation mechanism influence significantly the overall system performance and the effectiveness of Artificial Bee Colony for makespan minimization and on the machine occupation maximization.