Adham, MT;
Bentley, PJ;
Diaz, D;
(2017)
Evaluating Decomposition Strategies to Enable Scalable Scheduling for a Real-World Multi-line Steel Scheduling Problem.
In:
(Proceedings) IEEE Symposium Series on Computational Intelligence (IEEE SSCI).
(pp. pp. 3130-3137).
IEEE
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Abstract
Steel scheduling is recognised as one of the most difficult real-world scheduling problems. It is characterised by a wide range of operational constraints, variable dependencies and multiple objectives. This paper uses a divide and conquer method to reduce the combinatorial complexity of a real-world multi-line steel scheduling problem. The problem is first decomposed into sub-problems which are solved individually in parallel using parallel branch and bound, then sub-problems are combined to form a solution to the original problem. Three decomposition strategies are compared, specifically: a manual heuristic domain knowledge (DOM) intensive strategy, K-means++ (KM) clustering and Self-organising maps (SOM). Experimental results show that using SOM for decomposition is a promising approach. This paper demonstrates that despite being a highly complex and constrained problem, it is possible to use divide and conquer to achieve potentially good scalability characteristics without significant detriment to the solution quality.
| Type: | Proceedings paper |
|---|---|
| Title: | Evaluating Decomposition Strategies to Enable Scalable Scheduling for a Real-World Multi-line Steel Scheduling Problem |
| Event: | IEEE Symposium Series on Computational Intelligence (IEEE SSCI) |
| Location: | Honolulu, HI |
| Dates: | 27 November 2017 - 01 December 2017 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SSCI.2017.8285185 |
| Publisher version: | https://doi.org/10.1109/SSCI.2017.8285185 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Science & Technology, Technology, Computer Science, Artificial Intelligence, Engineering, Electrical & Electronic, Computer Science, Engineering, TRAVELING SALESMAN PROBLEM, OPTIMIZATION, NETWORKS |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10059808 |
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