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Evaluating Decomposition Strategies to Enable Scalable Scheduling for a Real-World Multi-line Steel Scheduling Problem

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 Green open access

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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 > Provost and Vice Provost Offices
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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