A meta-model for assembly line balancing with learning effect and part feeding with supermarkets

This research presents a novel method for optimizing the design of assembly lines (ALs) through the combined consideration of two strategic choice problems: part feeding with supermarkets (PFS) and the simple assembly line balancing problem (SALBP) under learning effect (LE). By doing the same tasks, the production worker(s) in various realistic scenarios continuously develop their skills. As a result, if a task is processed later, its processing time decreases. The influence of LE on two decision problems-the optimum number of stations in a SALBP and the optimum number of supermarkets serving the lines-is investigated in this paper. For the study, mathematical modeling for supermarket location and the COMSOAL algorithm for SALBP are suggested. The well-known test problems are addressed by these methods in the literature. The findings show that the LE decreased the number of supermarkets, the number of stations in SALBP, and the overall cost of PFS, both directly and indirectly.