Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design

Catherine Azzaro-Pantel (1) , Pascale Zaraté (2)

1 LGC - Laboratoire de Génie Chimique
2 IRIT - Institut de recherche en informatique de Toulouse

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.

Mots clés: Batch plant design; Multiobjective optimization; Genetic algorithm; Multicriteria decision analysis

Domaines: Génie des procédés; Génie chimique

Catherine Azzaro-Pantel, Pascale Zaraté. Mutual benefits of two multicriteria analysis methodologies: A case study for batch plant design. Engineering Applications of Artificial Intelligence, 2009, 22 (4-5), pp.546-556. ⟨10.1016/j.engappai.2009.02.008⟩. ⟨hal-03573106⟩