Efficient scheduling using complex networks
It offers a novel scheduling method for steel manufacturing, but the improvement is only over random schedules, not state-of-the-art baselines.
The paper tackles production scheduling in steel manufacturing, achieving more efficient schedules than random selection from real processes by combining complex network techniques with depth-first search.
We consider the problem of efficiently scheduling the production of goods for a model steel manufacturing company. We propose a new approach for solving this classic problem, using techniques from the statistical physics of complex networks in conjunction with depth-first search to generate a successful, flexible, schedule. The schedule generated by our algorithm is more efficient and outperforms schedules selected at random from those observed in real steel manufacturing processes. Finally, we explore whether the proposed approach could be beneficial for long term planning.