  Real-world combinatorial optimization problems are often stochastic and
dynamic. Therefore, it is essential to make optimal and reliable decisions with
a holistic approach. In this paper, we consider the dynamic chance-constrained
knapsack problem where the weight of each item is stochastic, the capacity
constraint changes dynamically over time, and the objective is to maximize the
total profit subject to the probability that total weight exceeds the capacity.
We make use of prominent tail inequalities such as Chebyshev's inequality, and
Chernoff bound to approximate the probabilistic constraint. Our key
contribution is to introduce an additional objective which estimates the
minimal capacity bound for a given stochastic solution that still meets the
chance constraint. This objective helps to cater for dynamic changes to the
stochastic problem. We apply single- and multi-objective evolutionary
algorithms to the problem and show how bi-objective optimization can help to
deal with dynamic chance-constrained problems.
