  In this paper, we propose a robust optimization-based heuristic algorithm for
the chance-constrained binary knapsack problem (CKP). We assume that the
weights of items are independent normally distributed. By utilizing the
properties of the submodular function, the proposed method approximates the CKP
to the robust knapsack problem with a cardinality constrained uncertainty set
parameterized by a uncertainty budget parameter. The proposed approach obtains
a heuristic solution by solving the approximated robust knapsack problem whose
optimal solution can be obtained by solving the ordinary binary knapsack
problem iteratively. The computational results show the effectiveness and
efficiency of the proposed approach.
