Application of optimization techniques to parameter set-up in scheduling

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(R). The first results of the use of this framework on real industrial databases are described and commented. (C) 2004 Elsevier B.V. All rights reserved.