Constraint satisfaction in logic programming
Constraint satisfaction in logic programming
Intelligent scheduling
Experimental evaluation of preprocessing algorithms for constraint satisfaction problems
Artificial Intelligence
Scheduling in Computer and Manufacturing Systems
Scheduling in Computer and Manufacturing Systems
Consistency techniques for numeric CSPs
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Binary vs. non-binary constraints
Artificial Intelligence
Generalizing GraphPlan by formulating planning as a CSP
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Mathematically clinching a playoff spot in the NHL and the effect of scoring systems
Canadian AI'08 Proceedings of the Canadian Society for computational studies of intelligence, 21st conference on Advances in artificial intelligence
Assessing the value of future and present options in real-time planning
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Maximizing future options: an on-line real-time planning method
MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
Finding relations among linear constraints
AISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Symbolic Computation
A generic method for identifying and exploiting dominance relations
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
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The use of heuristics as a means to improve constraint solver performance has been researched widely. However, most work has been on problem-independent heuristics (e.g., variable and value ordering), and has focused on offline problems (e.g., one-shot constraint satisfaction). In this paper, we present an online scheduling problem for which we are developing a real-time scheduling algorithm. While we can and do use generic heuristics in the scheduler, here we focus on the use of domain-specific redundant constraints to effectively approximate optimal offline solutions. We present a taxonomy of redundant domain constraints, and examine their impact on the effectiveness of the scheduler. We also describe several techniques for generating redundant constraints, which can be applied to a large class of job shop scheduling problems.