Efficient methods for scheduling make-to-order assemblies under resource, assembly area and part availability constraints

We consider the problem of scheduling multiple, large-scale, make-to-order assemblies under resource, assembly area, and part availability constraints. Such problems typically occur in the assembly of high-volume, discrete make-to-order products. Based on a list scheduling procedure proposed by Kolisch, we introduce three efficient heuristic solution methods. Namely, a biased random sampling method and two tabu search-based large-step optimization methods. The two latter methods differ in the employed neighbourhood. The first one uses a simple API-neighbourhood while the second one uses a more elaborated so-called ‘critical neighbourhood’ which makes use of problem insight. All three procedures are assessed on a systematically generated set of test instances. The results indicate that especially the large-scale optimization method with the critical neighbourhood gives very good results which are significantly better than simple single-pass list scheduling procedures.