Portfolio optimization methodologies play a central role in strategic asset allocation (SAA), where it is desirable to have portfolios that are efficient, diversified, and stable. Since the development of the traditional mean-variance approach of Markowitz (1952), many improvements have been made to overcome problems such as lack of diversification and strong sensitivity of optimal portfolio weights to expected returns.The Black and Litterman (1992) model (BL) is among the most used approaches. The idea behind this model is that expected returns are the result of two important sources of information: the first is market information in the form of equilibrium returns (implicit returns that clear out the outstanding market allocation), and the second is analysts' views, which tilt the market portfolio to another diversified portfolio compatible with investor beliefs. In this fashion, portfolio managers get an intuitive but formal model to generate optimal allocation. However, while the BL model offers a very useful and intuitive approach to deal with asset allocation, the inputs considered for the calculation of equilibrium returns are subject to estimation error, and thus expected returns will also contain estimation error. Michaud (1998) proposed the use of a statistical tool known as resampling to deal with estimation error, which is an important source of lack of diversification in mean-variance portfolios. This technique considers that data come from a stochastic process instead of being a deterministic input as in Markowitz (1952). This paper proposes the use of a portfolio optimization methodology which combines features of both the BL and resampling methodologies. This novel methodology allows the combination of equilibrium and investor's views as in BL, and at same time deals with estimation risk as in Michaud (1998). Thus, it generates robust and diversified optimal allocations which are desirable properties for long-term investors such as central banks and sovereign wealth funds. We empirically test the new methodology using a sample of fixed income and equity indices, achieving very supportive results. We find strong evidence supporting the use of resampling techniques to improve standard models like BL and Markowitz, and this result is more pronounced for medium levels of risk. In general, our proposed methodologies, both with and without views, generated very competitive portfolios compared to the other methodologies, considering the three evaluation dimensions: financial efficiency, diversification, and allocation stability. For medium levels of risk, our methodologies are markedly better than others.