Genetic Algorithms for Scheduling and Optimization of Ore Train Networks

Research output: Chapter in Book/Conference paperConference paperpeer-review


Search and optimization problems are a major arena for the practical application of Artificial Intelligence. However, when supply chain optimization and scheduling is tackled, techniques based on linear or non-linear programming are often used in preference to Evolutionary Computation such as Genetic Algorithms (GAs). It is important to analyse whether GA are suitable for continuous real-world supply chain scheduling tasks which need regular updates.
We analysed a practical situation involving iron ore train networks which is indeed one of significant economic importance. In addition, iron ore train networks have some interesting and distinctive characteristics so analysing this situation is an important step toward understanding the performance of GA in real-world supply chain scheduling. We compared the performance of GA with Nonlinear programming heuristics and existing industry scheduling approaches. The main result is that our comparison of techniques here produce an example in which GAs perform well and is a cost effective approach.
Original languageEnglish
Title of host publicationProceedings GCAI-2018. 4th Global Conference on Artificial Intelligence
EditorsDaniel Lee, Alexander Steen, Toby Walsh
Place of PublicationLuxembourg
Publication statusPublished - 17 Sept 2018
Event4th Global Conference on Artificial Intelligence: GCAI 2018 - Luxembourg City, Luxembourg
Duration: 17 Sept 201819 Sept 2018

Publication series

NameEPiC Series in Computing
ISSN (Print)2398-7340


Conference4th Global Conference on Artificial Intelligence
CityLuxembourg City


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