Optimization of Workshop Layout Based on an Enhanced Sparrow Search Algorithm

With the expansion of manufacturing enterprises, traditional production models face significant challenges. The layout of workshop facilities, a critical aspect of enterprise decision-making, directly influences operational efficiency and economic benefits. However, optimizing workshop facility layouts is a complex issue involving multiple objectives and adherence to various constraints. To address this, this study proposes a dual-objective hybrid programming model aimed at minimizing logistics costs while maximizing non-logistics correlations. An improved sparrow optimization algorithm (SMHO), based on multi-strategy integration, is introduced to enhance algorithmic performance through a two-stage progressive approach. Initially, population positions are initialized using a random generation method. Subsequently, the social behavior simulation mechanism from the hippopotamus optimization algorithm is incorporated to refine the position update rule of the sparrow algorithm. Finally, Rosenbrock's rotational perturbation strategy is applied after each iteration. Compared with several meta-heuristic algorithms, including Harris Hawk, Skyhawk, Sparrow, and Hippopotamus, SMHO exhibits strong competitiveness.