Like most real-world problems, the design of multipurpose batch facilities involves multiple objectives. However the existing literature on the subject has been mainly centred on mono-criterion objectives (Barbosa-Povoa, 2007). Therefore, multi-objective optimisation is a modelling approach that requires further study when applied to such facilities. The best way to deal with various goals simultaneously is to define the efficient frontier which offers the optimal solutions found by multi-objective optimization. In this work, the inspection of the efficient frontier allows the decision maker to select the most satisfactory plant topology with the respective equipment design and storage policies that minimizes the total cost of the system, while maximizing production, subject to operational restrictions. The approach to the detailed design of multipurpose batch facilities with periodic mode of operation, as proposed by Pinto et al. (2005), is now extended to address the problem of uncertainty associated with demand and the incorporation of economic aspects. The uncertainty is treated through a two-stage stochastic model, leading to a MILP formulation. A scenario is set up where the demand is represented by a discrete probability function and a cyclic operation is considered. The e- constraint method is employed to handle the multi-objective optimization. An example, where different situations are evaluated is solved and a topology analysis is made
