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In this paper, a discrete-event simulation model is
coupled with a genetic algorithm to treat highly combinatorial
scheduling problems encountered in a production campaign of a fine
chemistry plant. The main constraints and features of fine chemistry
have been taken into account in the development of the model, thus
allowing a realistic evaluation of the objective function used in the
stochastic optimization procedure. After a presentation of problem
combinatorics, the coupling strategy is then...
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