  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.
