Work allocation to stations with various learning slopesin assembly lines for lots

摘要

This paper addresses allocating work elements with various learning slopes to stations in an assembly line for lots, tornminimize the makespan of a lot of products. The line operates under learning, and no buffers are permitted in betweenrnthe stations. Due to the nature of work, station's learning slopes can be different. We propose a two stagernoptimization methodology: (1) NLP optimization with some constraints relaxation; (2) Modified line balancingrnprocedure that finds a non-relaxed solution that is the closest to the unconstrained solution found in the first stage.rnThe savings in the optimal makespan over the balanced loading case are demonstrated.