The shifting bottleneck procedure for job shop scheduling
Management Science
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
Computers and Industrial Engineering - Special issue on computational intelligence for industrial engineering
Decomposition methods for large job shops
Computers and Operations Research
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation
Productivity improvement: shifting bottleneck detection
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
A very fast TS/SA algorithm for the job shop scheduling problem
Computers and Operations Research
Ant colony optimization combined with taboo search for the job shop scheduling problem
Computers and Operations Research
Computers and Operations Research
A new hybrid GA/SA algorithm for the job shop scheduling problem
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
A novel genetic algorithm based on immunity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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An immune genetic algorithm based on bottleneck jobs is presented for the job shop scheduling problem in which the total weighted tardiness must be minimized. Bottleneck jobs have significant impact on final scheduling performance and therefore need to be considered with higher priority. In order to describe the characteristic information concerning bottleneck jobs, a fuzzy inference system is employed to transform human knowledge into the bottleneck characteristic values which reflect the features of both the objective function and the current optimization stage. Then, an immune operator is designed based on these characteristic values and a genetic algorithm combined with the immune mechanism is devised to solve the job shop scheduling problem. Numerical computations for problems of different scales show that the proposed algorithm achieves effective results by accelerating the convergence of the optimization process.