In Fixed Parameter Analysis (FPA) the value of the target problem parameter remains constant throughout the analysis. Variable Parameter Analysis (VPA), on the other hand, lets the parameter value depend on the structure of each specific instance and often exhibits more favorable asymptotic behavior as the input size grows. This paper applies VPA to an intractable optimization problem with $n$ objects, showing that the exponential-time factor in enumerating the feasible solution set is attributed solely to the variable parameter $\nu$. As the instance size increases, $\nu$ typically becomes much smaller than $n$, thereby reducing the exponential component in practice. Empirical tests on the one‑machine scheduling problem demonstrate that VPA‑based algorithms achieve faster convergence and lower runtime on most randomly generated instances compared to traditional FPA methods.\n\nReview: VPA offers a fresh perspective on parameterized complexity, especially for scenarios where the parameter naturally shrinks with instance structure, providing solid theoretical support for designing and optimizing practical scheduling algorithms.