The dynamic, multi-dimensional knapsack problem is an important benchmark for evaluating the performance of evolutionary algorithms in changing environments, especially because it has many real-world applications. In order to analyze the performance of ail evolutionary algorithm according to this benchmark, one needs to be able to change the current problem in a controlled manner. Several methods have been proposed to achieve this goal. In this paper, we briefly outline the proposed methods, discuss their shortcomings and propose a new method that can generate changes for a given severity level more reliably. We then present the experimental setup and results for the new method and compare it with existing methods. The Current results are promising and promote further study.