Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming

Hossny, Ahmad, Nahavandi, Saeid and Creighton, Douglas 2013, Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming, in SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics, IEEE, Piscataway, N.J., pp. 970-976.

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Title Minimizing impact of bounded uncertainty on McNaughton's scheduling algorithm via interval programming
Author(s) Hossny, Ahmad
Nahavandi, Saeid
Creighton, Douglas
Conference name IEEE Systems, Man and Cybernetics. Conference (2013 : Manchester, England)
Conference location Manchester, England
Conference dates 13-16 Oct. 2013
Title of proceedings SMC 2013 : Proceedings of the 2013 IEEE International Conference on Systems, Man and Cybernetics
Editor(s) [Unknown]
Publication date 2013
Conference series IEEE Systems, Man and Cybernetics Conference
Start page 970
End page 976
Total pages 7
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) scheduling uncertainty
interval programming
interval arithmetic
Summary Uncertainty of data affects decision making process as it increases the risk and the costs of the decision. One of the challenges in minimizing the impact of the bounded uncertainty on any scheduling algorithm is the lack of information, as only the upper bound and the lower bound are provided without any known probability or membership function. On the contrary, probabilistic uncertainty can use probability distributions and fuzzy uncertainty can use the membership function. McNaughton's algorithm is used to find the optimum schedule that minimizes the makespan taking into consideration the preemption of tasks. The challenge here is the bounded inaccuracy of the input parameters for the algorithm, namely known as bounded uncertain data. This research uses interval programming to minimise the impact of bounded uncertainty of input parameters on McNaughton’s algorithm, it minimises the uncertainty of the cost function estimate and increase its optimality. This research is based on the hypothesis that doing the calculations on interval values then approximate the end result will produce more accurate results than approximating each interval input then doing numerical calculations.
ISBN 9781479906529
9780769551548
Language eng
Field of Research 010101 Algebra and Number Theory
010206 Operations Research
Socio Economic Objective 910404 Productivity (excl. Public Sector)
HERDC Research category E1 Full written paper - refereed
Copyright notice ©2013, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30058807

Document type: Conference Paper
Collection: Centre for Intelligent Systems Research
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