A Constraint Programming Approach to Optimise Sugarcane Rail Operations

, , & (2010) A Constraint Programming Approach to Optimise Sugarcane Rail Operations. In Taha, Z (Ed.) Proceedings of the 11th Asia Pacific Industrial Engineering and Management Systems Conference. University of Malaysia, Malaysia, pp. 1-7.

Description

In Australia, railway systems play a vital role in transporting the sugarcane crop between farms and mills. The sugarcane transport system is very complex and uses a daily schedule, consisting of a set of locomotives runs, to satisfy the requirements of the mill and harvesters. In this paper, a more efficient sugarcane transport scheduling system is proposed to reduce the cost of sugarcane transport. A job shop scheduling approach is used to formulate this complicated system and constraint programming is applied to obtain feasibility of alternative, to deal with conflicting objectives and to produce global optimal solutions. Optimization Programming Language (OPL) is used to search for possible solutions to problems and for the best (optimal) solution based on specific criteria. A numerical investigation is presented and demonstrates that high quality solutions are obtainable for industry-scale applications.

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ID Code: 80716
Item Type: Chapter in Book, Report or Conference volume (Conference contribution)
ORCID iD:
Masoud, Mahmoudorcid.org/0000-0002-0130-4327
Kozan, Erhanorcid.org/0000-0002-3208-702X
Kent, Geofforcid.org/0000-0001-5720-2592
Measurements or Duration: 7 pages
Keywords: Constraint Programming, Job Shop Scheduling, Sugarcane Railway
ISBN: 978-967-5148-80-8
Pure ID: 32157534
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Past > QUT Faculties & Divisions > Science & Engineering Faculty
Current > Research Centres > Australian Research Centre for Aerospace Automation
Current > Research Centres > Centre for Tropical Crops and Biocommodities
Copyright Owner: Consult author(s) regarding copyright matters
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Deposited On: 21 Jan 2015 22:49
Last Modified: 21 Apr 2024 16:18