This paper presents a MultiObjective Genetic Algorithm (MOGA)
optimization framework for batch plant design. For this purpose, two
approaches are implemented and compared with respect to three criteria,
i.e., investment cost, equipment number and a flexibility indicator based
on work in process (the so-called WIP) computed by use of a discrete-event
simulation model. The first approach involves a genetic algorithm in order
to generate acceptable solutions, from which the best ones are chosen by
using a Pareto Sort algorithm. The second approach combines the previous
Genetic Algorithm with a multicriteria analysis methodology, i.e., the
Electre method in order to find the best solutions. The performances of the
two procedures are studied for a large-size problem and a comparison
between the procedures is then made.
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