Job shop scheduling by simulated annealing
Operations Research
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
A branch and bound algorithm for the job-shop scheduling problem
Discrete Applied Mathematics - Special volume: viewpoints on optimization
A fast taboo search algorithm for the job shop problem
Management Science
Statistical mechanics methods and phase transitions in optimizationproblems
Theoretical Computer Science - Phase transitions in combinatorial problems
Tabu Search
Investigation of the Fitness Landscapes in Graph Bipartitioning: An Empirical Study
Journal of Heuristics
An Advanced Tabu Search Algorithm for the Job Shop Problem
Journal of Scheduling
Genetic algorithms, path relinking, and the flowshop sequencing problem
Evolutionary Computation
A new look at the easy-hard-easy pattern of combinatorial search difficulty
Journal of Artificial Intelligence Research
Backbones in optimization and approximation
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A novel local search algorithm for the traveling salesman problem that exploits backbones
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Solving job shop scheduling problems utilizing the properties of backbone and "big valley"
Computational Optimization and Applications
A new dispatching rule based genetic algorithm for the multi-objective job shop problem
Journal of Heuristics
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We characterize the search landscape of random instances of the job shop scheduling problem (JSP). Specifically, we investigate how the expected values of (1) backbone size, (2) distance between near-optimal schedules, and (3) makespan of random schedules vary as a function of the job to machine ratio (N/M). For the limiting cases N/M → 0 and N/M → ∞ we provide analytical results, while for intermediate values of N/M we perform experiments. We prove that as N/M → 0, backbone size approaches 100%, while as N/M → ∞ the backbone vanishes. In the process we show that as N/M → 0 (resp. N/M → ∞), simple priority rules almost surely generate an optimal schedule, providing theoretical evidence of an "easy-hard-easy" pattern of typical-case instance difficulty in job shop scheduling. We also draw connections between our theoretical results and the "big valley" picture of JSP landscapes.