Job shop scheduling by simulated annealing
Operations Research
Easy problems are sometimes hard
Artificial Intelligence
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Phase transitions and the search problem
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
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
Tabu Search
Generating Satisfiable Problem Instances
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Backbone fragility and the local search cost peak
Journal of Artificial Intelligence Research
When gravity fails: local search topology
Journal of Artificial Intelligence Research
Sparse constraint graphs and exceptionally hard problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Backbones in optimization and approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Clustering at the phase transition
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Scheduling tasks with precedence constraints to solicit desirable bid combinations
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Computers and Operations Research - Anniversary focused issue of computers & operations research on tabu search
A review of metrics on permutations for search landscape analysis
Computers and Operations Research
Crossover: the divine afflatus in search
Proceedings of the 9th annual conference companion on Genetic and evolutionary computation
Overcoming hierarchical difficulty by hill-climbing the building block structure
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Winner determination for combinatorial auctions for tasks with time and precedence constraints
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Marco Somalvico Memorial Issue
Journal of Artificial Intelligence Research
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
The creativity potential within evolutionary algorithms
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Solving job shop scheduling problems utilizing the properties of backbone and "big valley"
Computational Optimization and Applications
Machine scheduling in custom furniture industry through neuro-evolutionary hybridization
Applied Soft Computing
Understanding the behavior of Solution-Guided Search for job-shop scheduling
Journal of Scheduling
A GA/TS algorithm for the stage shop scheduling problem
Computers and Industrial Engineering
Information Sciences: an International Journal
Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling
Journal of Intelligent Manufacturing
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Tabu search algorithms are among the most effective approaches for solving the job-shop scheduling problem (JSP). Yet, we have little understanding of why these algorithms work so well, and under what conditions. We develop a model of problem difficulty for tabu search in the JSP, borrowing from similar models developed for SAT and other NP-complete problems. We show that the mean distance between random local optima and the nearest optimal solution is highly correlated with the cost of locating optimal solutions to typical, random JSPs. Additionally, this model accounts for the cost of locating sub-optimal solutions, and provides an explanation for differences in the relative difficulty of square versus rectangular JSPs. We also identify two important limitations of our model. First, model accuracy is inversely correlated with problem difficulty, and is exceptionally poor for rare, very high-cost problem instances. Second, the model is significantly less accurate for structured, non-random JSPs. Our results are also likely to be useful in future research on difficulty models of local search in SAT, as local search cost in both SAT and the JSP is largely dictated by the same search space features. Similarly, our research represents the first attempt to quantitatively model the cost of tabu search for any NP-complete problem, and may possibly be leveraged in an effort to understand tabu search in problems other than job-shop scheduling.