Problem formulations for the purpose of optimization typically represent a simplification of the real physical systems that are modeled. In order to obtain solutions that are robust to criteria not considered in the objectives and constraints definition, we propose to include in the results list additional solutions that are not necessarily optimal but have very different characteristics in the solution space than the optima. To this effect, we present a novel feature for optimization frameworks, namely a solution-space diversification. We present this feature on a backdrop of batch process design. We developed a method that optimizes the design of a new chemical process to be implemented in an existing multi-purpose batch plant operating in single product campaign mode and that takes into account the special requirements and constraints in the corresponding production facilities. In the optimization, several objectives with different priorities are considered. A flexible meta-heuristic algorithm, Tabu Search, has been implemented to solve this multi-objective combinatorial non-linear problem. This approach is particularly suited for the identification of the Pareto set of non-dominated solutions, to which designs with a pronounced structural diversity are added. For this purpose an indicator of the structural difference between two designs has been defined that takes into account the position and nature of material transfers between equipment units. Three case studies are used to illustrate how the novel approach delivers extended Pareto-sets with a high variability in the design space. It has been shown why diversification in the design space is relevant to obtain solutions representing a robust set of alternatives in order to deal with potential constraints and limitations that are not covered in the problem formulation. Such an extended Pareto-set maximizes the probability that at least one of the proposed solutions will be suitable for industrial implementation. (c) 2005 Elsevier Ltd. All rights reserved.