Proceedings of the third international conference on Genetic algorithms
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
Search, polynomial complexity, and the fast messy genetic algorithm
Search, polynomial complexity, and the fast messy genetic algorithm
A fast taboo search algorithm for the job shop problem
Management Science
Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
Simulation optimization with the linear move and exchange move optimization algorithm
Proceedings of the 31st conference on Winter simulation: Simulation---a bridge to the future - Volume 1
The ant colony optimization meta-heuristic
New ideas in optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Efficient Global Optimization of Expensive Black-Box Functions
Journal of Global Optimization
A Time-Sensitive System for Black-Box Combinatorial Optimization
ALENEX '02 Revised Papers from the 4th International Workshop on Algorithm Engineering and Experiments
A Template for Scatter Search and Path Relinking
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
A Knowledge-Based Approach To Response Surface Modelling in Multifidelity Optimization
Journal of Global Optimization
A framework for managing models in nonlinear optimization of computationally expensive functions
A framework for managing models in nonlinear optimization of computationally expensive functions
Distributed evolutionary algorithms for simulation optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Improvement of intelligent optimization by an experience feedback approach
EA'07 Proceedings of the Evolution artificielle, 8th international conference on Artificial evolution
A genetic-algorithm-based fusion system optimization for 3D image interpretation
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
| Hi-index | 0.00 |
Scheduling requires to set-up a number of parameters that have a direct influence on the schedule quality. Since scheduling is a highly unstable process, it is usually a long and complex task to tune manually these parameters in order to optimize a set of objectives. Meta-heuristics have recently been successfully used for schedule optimization, but an important modeling effort is usually required in order to express the problem to solve within the specific framework of each method. Moreover, these techniques are often time-consuming and their application to problems of industrial size may be hazardous. It is suggested in this article a way to combine meta-heuristics in a black box approach in order to select, then set-up scheduling parameters on industrial-scale scheduling problems, i.e. problems where several tens of criteria can be combined in order to build an objective function, several tens of parameters can be used, with a schedule involving several hundreds of machines and several thousands of tasks. An implementation framework has been developed and tested on an industrial scheduler, named Ortems®. The first results of the use of this framework on real industrial databases are described and commented.