Efficient scheduling using complex networks

TL;DR

This paper introduces a novel scheduling method for steel manufacturing that leverages complex network techniques and depth-first search, resulting in more efficient production schedules than traditional methods.

Contribution

It presents a new approach combining complex network analysis with depth-first search to improve scheduling efficiency in steel manufacturing.

Findings

Generated schedules outperform random real-world schedules

Approach is flexible and adaptable for long-term planning

Method demonstrates significant efficiency improvements

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.