Integer and combinatorial optimization
Integer and combinatorial optimization
Sequencing with earliness and tardiness penalties: a review
Operations Research
A survey of algorithms for the single machine total weighted tardiness scheduling problem
Discrete Applied Mathematics - Southampton conference on combinatorial optimization, April 1987
Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Weighted-tardiness scheduling on parallel machines with proportional weights
Operations Research
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Synergy in cooperating agents: designing manipulators from task specifications
Synergy in cooperating agents: designing manipulators from task specifications
The Hearsay-II Speech-Understanding System: Integrating Knowledge to Resolve Uncertainty
ACM Computing Surveys (CSUR)
A SOM-based multi-agent architecture for multirobot systems
International Journal of Robotics and Automation
Agent based framework for emergency rescue and assistance planning
PAISI'07 Proceedings of the 2007 Pacific Asia conference on Intelligence and security informatics
Integrating rush orders into existent schedules for a complex job shop problem
Applied Intelligence
Computers and Operations Research
An a-team based architecture for constraint programming
PRIMA'06 Proceedings of the 9th Pacific Rim international conference on Agent Computing and Multi-Agent Systems
Coordinating learning agents for multiple resource job scheduling
ALA'09 Proceedings of the Second international conference on Adaptive and Learning Agents
Sequence-dependent group scheduling problem on unrelated-parallel machines
Expert Systems with Applications: An International Journal
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We present a new agent-based solution approach for the problem of scheduling multiple non-identical machines in the face of sequence dependent setups, job machine restrictions, batch size preferences, fixed costs of assigning jobs to machines and downstream considerations. We consider multiple objectives such as minimizing (weighted) earliness and tardiness, and minimizing job-machine assignment costs. We use an agent-based architecture called Asynchronous Team (A-Team), in which each agent encapsulates a different problem solving strategy and agents cooperate by exchanging results. Computational experiments on large instances of real-world scheduling problems show that the results obtained by this approach are significantly better than any single algorithm or the scheduler alone. This approach has been successfully implemented in an industrial scheduling system.