A Hybrid Data-Driven Performance Prediction and Decision-Making Approach for Production Layout Selection Under Uncertainty

Abstract

Abstract. As products become more sophisticated and quality demands grow, manufacturing systems are becoming increasingly complex. In particular, for small and medium enterprises (SMEs) operating complex, high-mix, low-volume production systems, manufacturing layout significantly impacts productivity and operational costs. Given this impact, accurate prediction of production performance becomes essential for optimizing layout configurations and resource allocation. In this article, the objective is to develop a decision-support framework for selecting the best production layouts in labor-intensive SME manufacturing systems. The framework uses discrete event simulation (DES) with cellular manufacturing system (CMS) models to generate datasets. And, Gaussian process regression (GPR) is applied to provide probabilistic predictions for key performance variables while accounting for inherent uncertainties. When GPR-based production volume predictions for different layouts result in overlapping prediction ranges, the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) is then utilized to systematically rank the alternatives using multiple key variables as evaluation criteria. A case study demonstrates the framework's effectiveness by comparing three alternative layouts with GPR-based production volume predictions and fuzzy TOPSIS-based decision-making. The multicriteria evaluation systematically ranks the alternatives using key performance variables including adjusted cycle time, total buffer capacity, average manpower efficiency, and number of cells.