Job shop scheduling by simulated annealing
Operations Research
Applying tabu search to the job-shop scheduling problem
Annals of Operations Research - Special issue on Tabu search
Insertion techniques for the heuristic solution of the job shop problem
Proceedings of the workshop on Discrete algorithms
A fast taboo search algorithm for the job shop problem
Management Science
Guided Local Search with Shifting Bottleneck for Job Shop Scheduling
Management Science
Tabu Search
Facts, Conjectures, and Improvements for Simulated Annealing
Facts, Conjectures, and Improvements for Simulated Annealing
Problem difficulty for tabu search in job-shop scheduling
Artificial Intelligence
A computational study of the job-shop and the flow-shop scheduling problems
A computational study of the job-shop and the flow-shop scheduling problems
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Empirical modeling and analysis of local search algorithms for the job-shop scheduling problem
Journal of Artificial Intelligence Research
An implementation view on job shop scheduling based on CPM
ICCOMP'07 Proceedings of the 11th WSEAS International Conference on Computers
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
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
Combining Constraint Programming and Local Search for Job-Shop Scheduling
INFORMS Journal on Computing
A GA/TS algorithm for the stage shop scheduling problem
Computers and Industrial Engineering
A tabu search heuristic procedure for the capacitated facility location problem
Journal of Heuristics
Analysis of new niching genetic algorithms for finding multiple solutions in the job shop scheduling
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing
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Over the last decade and a half, tabu search algorithms for machine scheduling have gained a near-mythical reputation by consistently equaling or establishing state-of-the-art performance levels on a range of academic and real-world problems. Yet, despite these successes, remarkably little research has been devoted to developing an understanding of why tabu search is so effective on this problem class. In this paper, we report results that provide significant progress in this direction. We consider Nowicki and Smutnicki's i-TSAB tabu search algorithm, which represents the current state-of-the-art for the makespan-minimization form of the classical job-shop scheduling problem. Via a series of controlled experiments, we identify those components of i-TSAB that enable it to achieve state-of-the-art performance levels. In doing so, we expose a number of misconceptions regarding the behavior and/or benefits of tabu search and other local search metaheuristics for the job-shop problem. Our results also serve to focus future research, by identifying those specific directions that are most likely to yield further improvements in performance.