Download PDFOpen PDF in browser

Genetic Algorithms for Scheduling and Optimization of Ore Train Networks

12 pagesPublished: September 17, 2018


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.

Keyphrases: evolutionary algorithm, Iron ore railway network, Real-time optimisation

In: Daniel Lee, Alexander Steen and Toby Walsh (editors). GCAI-2018. 4th Global Conference on Artificial Intelligence, vol 55, pages 81--92

BibTeX entry
  author    = {Ghulam Mubashar Hassan and Mark Reynolds},
  title     = {Genetic Algorithms for Scheduling and Optimization of Ore Train Networks},
  booktitle = {GCAI-2018. 4th Global Conference on Artificial Intelligence},
  editor    = {Daniel Lee and Alexander Steen and Toby Walsh},
  series    = {EPiC Series in Computing},
  volume    = {55},
  pages     = {81--92},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/fzrq}}
Download PDFOpen PDF in browser