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 which has been proposed in Kolisch [19] 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 neighborhood. The first one uses a simple API-neighborhood while the second one uses a more elaborated so-called 'critical neighborhood' 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-step optimization method with the critical neighborhood gives very good results which are significant better than simple single-pass list scheduling procedures.
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