Efficient scheduling using complex networks

Abstract

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.