There are two main types of process scheduling algorithms commonly used in aircraft/spacecraft avionics systems. The first category consists of dynamic algorithms, which dynamically assign priorities to processes on the basis of runtime parameters. The second category consists of static algorithms, which statically determine priorities before runtime. The main disadvantage of applying dynamic process scheduling algorithms to avionics systems is the extra runtime overhead produced by these algorithms. This overhead is mainly related to the time required to sort active processes in the ready queue upon each process preemption or the arrival of each new process. The mentioned overhead encourages the use of static algorithms. But static algorithms have their own disadvantages. In fact, these algorithms bound the maximum available CPU utilization and have difficulties with non-periodic processes. This paper proposes and evaluates an approach to exploiting skewed associative memories in order to replace the time-consuming sorting operation by an efficient search operation. Both analytical models and simulation results show that the proposed approach can reduce the time complexity of the runtime overhead of dynamic scheduling algorithms (in terms of n the number of active processes) from O(nlogn) to O(n). This can considerably increase the performance of dynamic scheduling algorithms and make them much more feasible to be used in aircraft/spacecraft avionics systems.