  The interval subset sum problem (ISSP) is a generalization of the well-known
subset sum problem. Given a set of intervals
$\left\{[a_{i,1},a_{i,2}]\right\}_{i=1}^n$ and a target integer $T,$ the ISSP
is to find a set of integers, at most one from each interval, such that their
sum best approximates the target $T$ but cannot exceed it. In this paper, we
first study the computational complexity of the ISSP. We show that the ISSP is
relatively easy to solve compared to the 0-1 Knapsack problem (KP). We also
identify several subclasses of the ISSP which are polynomial time solvable
(with high probability), albeit the problem is generally NP-hard. Then, we
propose a new fully polynomial time approximation scheme (FPTAS) for solving
the general ISSP problem. The time and space complexities of the proposed
scheme are ${\cal O}\left(n \max\left\{1 / \epsilon,\log n\right\}\right)$ and
${\cal O}\left(n+1/\epsilon\right),$ respectively, where $\epsilon$ is the
relative approximation error. To the best of our knowledge, the proposed scheme
has almost the same time complexity but a significantly lower space complexity
compared to the best known scheme. Both the correctness and efficiency of the
proposed scheme are validated by numerical simulations. In particular, the
proposed scheme successfully solves ISSP instances with $n=100,000$ and
$\epsilon=0.1\%$ within one second.
