Measuring Spatial Uniformity in Manufacturing Layouts: Distinguishing Spatial Coverage from Distance Variability

Background: Logistics layouts are commonly evaluated using flow-distance criteria, but layouts with similar economic performance may differ in spatial coverage and pairwise-distance variability. This study examines whether measures of these concepts exhibit distinguishable sensitivity to layout-generation conditions and whether their empirical association supports treating them as complementary rather than redundant descriptors. Methods: Fuzzy LP-Alinks generated layout configurations in a full-factorial simulation comprising 108 conditions and 30 replications per condition; the minimum-flow-distance solution was retained from each run. Coverage was measured with the Normalized Coverage Score (NCS), and distance regularity with the Distance Variability Index (DVI), defined as the coefficient of variation of pairwise distances. Five-factor ANOVAs examined main and interaction effects of object count, link density, virtual-force magnitude, and fuzzy membership function settings, while joint-distribution analyses assessed the association between NCS and DVI. Results: The indicators exhibited different main-effect and interaction patterns. In particular, object count and link density had strong main effects on DVI but nonsignificant main effects on NCS, although their interaction substantially affected NCS. The joint distributions further showed that similar NCS values could coexist with different DVI values. Conclusions: Within the tested experimental setting, the results provide evidence that NCS and DVI behave as complementary rather than directly redundant descriptors of spatial configuration. This conclusion does not imply complete separability or formal discriminant validity.