Priority rules for job shops with weighted tardiness costs
Management Science
The shifting bottleneck procedure for job shop scheduling
Management Science
Evolution based learning in a job shop scheduling environment
Computers and Operations Research - Special issue on genetic algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Computational Study of Shifting Bottleneck Procedures forShop Scheduling Problems
Journal of Heuristics
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Genetic diversity as an objective in multi-objective evolutionary algorithms
Evolutionary Computation
Expert Systems with Applications: An International Journal
A multi-criteria approach for scheduling semiconductor wafer fabrication facilities
Journal of Scheduling
Computers and Operations Research
Computers and Industrial Engineering
Scheduling jobs on parallel machines with setup times and ready times
Computers and Industrial Engineering
Scheduling: Theory, Algorithms, and Systems
Scheduling: Theory, Algorithms, and Systems
Two-phase sub population genetic algorithm for parallel machine-scheduling problem
Expert Systems with Applications: An International Journal
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
A simulation-based two-stage scheduling methodology for controlling semiconductor wafer fabs
Expert Systems with Applications: An International Journal
Computers and Industrial Engineering
Hi-index | 12.05 |
In this paper we address multiobjective job shop scheduling problems. After several decades of research in scheduling problems, a variety of heuristics have been developed. The proposed algorithm is a hybrid of three frequently applied ones: the dispatching rule, the shifting bottleneck procedure, and the evolutionary algorithm. It is a two-stage algorithm, which integrates a rule-based memetic algorithm in the first stage and a re-optimization procedure of shifting bottleneck in the second. We conduct experiments using benchmark instances found in the literature to assess the performance of the proposed method. The experimental results show that the proposed method is effective and efficient for multiobjective scheduling problems.