This approach aims at steering scheduling decisions at the work center level using production targets. These production targets are expressed as the quantities of components to be achieved for each operation and for each period over a scheduling horizon. Different mathematical models called global scheduling models (linear programs) proposed in the thesis determine these quantities. These global scheduling models correspond to different global scheduling strategies of the factory such as minimizing variability, controlling cycle times, etc. The local scheduling level aims to achieve the objectives set by the global scheduling models, while optimizing its own criteria and respecting its constraints.

The approach is validated by experiments based on a simulation model and industrial data. In this paper, we are concerned with the resolution of a multiobjective complex job-shop scheduling problem stemming from semiconductor manufacturing. To produce feasible and industrially meaningful schedules, this paper extends the recently proposed batch-oblivious approach by considering unavailability periods and minimum time lags and by simultaneously optimizing multiple criteria that are relevant in the industrial context. A novel criterion on the satisfaction of production targets decided at a higher level is also proposed. Because the solution approach must be embedded in a real-time application, decision makers must express their preferences before the optimization phase. In addition, a preference model is introduced where trade-off is only allowed between some criteria. Two a priori multiobjective extensions of Simulated Annealing are proposed, which differ in how the simultaneous use of a lexicographic order and weights is handled when evaluating the fitness. A known a posteriori approach of the literature is used as a benchmark.

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