Priority rules for job shops with weighted tardiness costs
Management Science
The shifting bottleneck procedure for job shop scheduling
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
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
A fast taboo search algorithm for the job shop problem
Management Science
A systematic procedure for setting parameters in simulated annealing algorithms
Computers and Operations Research
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Decomposition methods for large job shops
Computers and Operations Research
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
Computers and Operations Research
Optimization methods for large-scale production scheduling problems
EUROCAST'07 Proceedings of the 11th international conference on Computer aided systems theory
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A prediction based iterative decomposition algorithm for scheduling large-scale job shops
Mathematical and Computer Modelling: An International Journal
Evolutionary algorithm for stochastic job shop scheduling with random processing time
Expert Systems with Applications: An International Journal
Solving Japanese nonograms by Taguchi-based genetic algorithm
Applied Intelligence
| Hi-index | 0.00 |
A decomposition based hybrid optimization algorithm is presented for large-scale job shop scheduling problems in which the total weighted tardiness must be minimized. In each iteration, a new subproblem is first defined by a simulated annealing approach and then solved using a genetic algorithm. We construct a fuzzy inference system to calculate the jobs' bottleneck characteristic values which depict the characteristic information in different optimization stages. This information is then utilized to guide the process of subproblem-solving in an immune mechanism in order to promote the optimization efficiency. Numerical computational results show that the proposed algorithm is effective for solving large-scale scheduling problems.