Priority rules for job shops with weighted tardiness costs
Management Science
The shifting bottleneck procedure for job shop scheduling
Management Science
An algorithm for solving the job-shop problem
Management Science
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
A genetic algorithm for the job shop problem
Computers and Operations Research - Special issue on genetic algorithms
A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation
Computers and Industrial Engineering
A fast taboo search algorithm for the job shop problem
Management Science
Decomposition methods for large job shops
Computers and Operations Research
Job Shop Scheduling with Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
A hybrid approach to large-scale job shop scheduling
Applied Intelligence
A genetic algorithm for the job shop on an ASRS warehouse
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
Hi-index | 0.98 |
In this paper, we present a prediction based iterative decomposition algorithm for solving large-scale job shop scheduling problems using the rolling horizon scheme and the prediction mechanism, in which the original large-scale scheduling problem is iteratively decomposed into several sub-problems. In the proposed algorithm, based on the job-clustering method, we construct the Global Scheduling Characteristics Prediction Model (GSCPM) to obtain the scheduling characteristics values, including the information of the bottleneck jobs and the predicted value of the global scheduling objective. Then, we adopt the above scheduling characteristics values to guide and coordinate the process of the problem decomposition and the sub-problem solving. Furthermore, we propose an adaptive genetic algorithm to solve each sub-problem. Numerical computational results show that the proposed algorithm is effective for large-scale scheduling problems.