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 ma ke-to-order products. Based on a list scheduling procedure proposed by Koli sch in 1999 we introduce three efficient heuristic solution methods. Namely , a biased random sampling method and two tabu search-based large-step opti mization methods. The two latter methods differ in the employed neighbourho od. The first one uses a simple API-neighbourhood while the second one uses a more elaborated so-called 'critical neighbourhood' which makes use of pr oblem insight. All three procedures are assessed on a systematically genera ted set of test instances. The results indicate that especially the large-s tep optimization method with the critical neighbourhood gives very good res ults which are significant better than simple single-pass list scheduling p rocedures.