  We propose an exact algorithm for solving biobjective integer programming
problems, which arise in various applications of operations research. The
algorithm is based on solving Pascoletti-Serafini scalarizations to search
specified regions (boxes) in the objective space and returns the set of
nondominated points. We implement the algorithm with different strategies,
where the choices of the scalarization model parameters and splitting rule
differ. We then derive bounds on the number of scalarization models solved; and
demonstrate the performances of the variants through computational experiments
both as exact algorithms and as solution approaches under time restriction. The
experiments demonstrate that different strategies have advantages in different
aspects: while some are quicker in finding the whole set of nondominated
solutions, others return good-quality solutions in terms of representativeness
when run under time restriction. We also compare the proposed approach with
existing algorithms. The results of our experiments show the satisfactory
behaviour of our algorithm, especially when run under time limit, as it
achieves better coverage of the whole frontier with a smaller number of
solutions compared to the existing algorithms.
