Optimal speedup of Las Vegas algorithms
Information Processing Letters
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
A fast taboo search algorithm for the job shop problem
Management Science
Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
Management Science
Dynamic problem structure analysis as a basis for constraint-directed scheduling heuristics
Artificial Intelligence
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Constraint-Based Scheduling
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Bayesian Approach to Tackling Hard Computational Problems
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Learning the Empirical Hardness of Optimization Problems: The Case of Combinatorial Auctions
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Learning evaluation functions to improve optimization by local search
The Journal of Machine Learning Research
An Advanced Tabu Search Algorithm for the Job Shop Problem
Journal of Scheduling
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
On universal restart strategies for backtracking search
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A hybrid constraint programming/local search approach to the job-shop scheduling problem
CPAIOR'08 Proceedings of the 5th international conference on Integration of AI and OR techniques in constraint programming for combinatorial optimization problems
Slack-based heuristics for constraint satisfaction scheduling
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
A competent memetic algorithm for complex scheduling
Natural Computing: an international journal
A hybrid harmony search algorithm for the flexible job shop scheduling problem
Applied Soft Computing
Journal of Intelligent Manufacturing
An integrated search heuristic for large-scale flexible job shop scheduling problems
Computers and Operations Research
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Since their introduction, local search algorithms have consistently represented the state of the art in solution techniques for the classical job-shop scheduling problem. This dominance is despite the availability of powerful search and inference techniques for scheduling problems developed by the constraint programming community. In this paper, we introduce a simple hybrid algorithm for job-shop scheduling that leverages both the fast, broad search capabilities of modern tabu search algorithms and the scheduling-specific inference capabilities of constraint programming. The hybrid algorithm significantly improves the performance of a state-of-the-art tabu search algorithm for the job-shop problem and represents the first instance in which a constraint programming algorithm obtains performance competitive with the best local search algorithms. Furthermore, the variability in solution quality obtained by the hybrid is significantly lower than that of pure local search algorithms. Beyond performance demonstration, we perform a series of experiments that provide insights into the roles of the two component algorithms in the overall performance of the hybrid.