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Reducing the impact of bounded parametric uncertainty on Hodgson's scheduling algorithm using interval programming

journal contribution
posted on 2017-12-01, 00:00 authored by A H Hossny, Douglas CreightonDouglas Creighton, Saeid Nahavandi
Input uncertainty is amajor challenge to the decisionmaking process as it leads to output inaccuracy, which increases the cost and the risk. Bounded uncertainty is usually formulated as mathematical intervals as it provides the upper bound and the lower bound without any information between them such as probability distribution or membership function. The lack of descriptive function between the upper and lower bounds makes the probabilistic and fuzzy techniques not effective. This research aims to reduce the impact of bounded uncertainty on the final result of the scheduling objective function and algorithm. The premise of this research is that performing the calculations using uncertain values and then approximating the final result produces more accurate results than approximating the uncertain input values before performing the calculations. The proposed methodology was to extend the scheduling algorithm to be interval based through extending numerical arithmetic to interval arithmetic and extending Boolean logic to interval logic. The methodology is applied to Hodgson's scheduling algorithm, which is used to minimize the number of delayed tasks. The solution is implemented using a MATLAB toolbox named TORSCHE by slicing its code and extending it. The experiments used the aircraft landing data with bounded uncertainty, and it enhanced the accuracy of the results by 12% than using the averaging or midpoint approximation.

History

Journal

IEEE systems journal

Volume

11

Issue

4

Pagination

1983 - 1993

Publisher

IEEE

Location

Piscataway, N.J.

ISSN

1932-8184

eISSN

1937-9234

Language

eng

Publication classification

C1.1 Refereed article in a scholarly journal

Copyright notice

2015, IEEE