  The world is dynamic and changes over time, thus any optimization problem
used to model real life problems must address this dynamic nature, taking into
account the cost of changes to a solution over time. The multistage model was
introduced with this goal in mind. In this model we are given a series of
instances of an optimization problem, corresponding to different times, and a
solution is provided for each instance. The strive for obtaining near-optimal
solutions for each instance on one hand, while maintaining similar solutions
for consecutive time units on the other hand, is quantified and integrated into
the objective function. In this paper we consider the Generalized Multistage
$d$-Knapsack problem, a generalization of the multistage variants of the
Multiple Knapsack problem, as well as the $d$-Dimensional Knapsack problem. We
present a PTAS for Generalized Multistage $d$-Knapsack.
