New generation sequencing technologies offer unique opportunities and challenges for re-sequencing studies. In this article, we focus on re-sequencing experiments using the Solexa technology, based on bacterial artificial chromosome (BAC) clones, and address an experimental design problem. In these specific experiments, approximate coordinates of the BACs on a reference genome are known, and fine-scale differences between the BAC sequences and the reference are of interest. The high-throughput characteristics of the sequencing technology makes it possible to multiplex BAC sequencing experiments by pooling BACs for a cost-effective operation. However, the way BACs are pooled in such re-sequencing experiments has an effect on the downstream analysis of the generated data, mostly due to subsequences common to multiple BACs. The experimental design strategy we develop in this article offers combinatorial solutions based on approximation algorithms for the well-known max n-cut problem and the related max n-section problem on hypergraphs. Our algorithms, when applied to a number of sample cases give more than a 2-fold performance improvement over random partitioning.