Local Search and Tabu Search Algorithms for Machine Scheduling of a Hybrid Flow Shop Under Uncertainty

In production systems, scheduling problems need to be solved under complex environmental conditions. In this paper, we present a comprehensive scheduling approach that is applicable in real industrial environments. To cope with the parameter uncertainty of real world problems, forecasting, classification, and simulation techniques are combined with heuristic optimization algorithms. Thus, the approach allows for identifying and including demand fluctuations and scrap rates, and offers a selection of suitable schedules depending on particular demand constellations in scheduling. Furthermore, we adapt seven optimization algorithms for two-stage hybrid flow shops with unrelated machines, machine qualifications, and skipping stages with the objective to minimize the makespan. The combination of methods is validated on a real production case of the automobile industry. The paper shows for the application case that metaheuristics provide significantly better results than SPT and safety factors, above a certain size, can reduce their effect preventing incomplete demand positions.