Extrapolating from limited uncertain information in large-scale combinatorial optimization problems to obtain robust solutions

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Climent, Laura
Wallace, Richard J.
O'Sullivan, Barry
Freuder, Eugene C.
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World Scientific Publishing
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Data uncertainty in real-life problems is a current challenge in many areas, including Operations Research (OR) and Constraint Programming (CP). This is especially true given the continual and accelerating increase in the amount of data associated with real life problems, to which Large Scale Combinatorial Optimization (LSCO) techniques may be applied. Although data uncertainty has been studied extensively in the literature, many approaches do not take into account the partial or complete lack of information about uncertainty in real-life settings. To meet this challenge, in this paper we present a strategy for extrapolating data from limited uncertain information to ensure a certain level of robustness in the solutions obtained. Our approach is motivated and evaluated with real-world applications of harvesting and supplying timber from forests to mills and the well known knapsack problem with uncertainty.
Uncertainty , Robustness , Optimization
Climent, L., Wallace, R. J., O'Sullivan, B. and Freuder, E. C. (2016) 'Extrapolating from Limited Uncertain Information in Large-Scale Combinatorial Optimization Problems to Obtain Robust Solutions', International Journal On Artificial Intelligence Tools, 25 (01), doi: 10.1142/S0218213016600058
© 2016 World Scientific Publishing Company. This is the accepted version of an article published in International Journal on Artificial Intelligence Tools Vol. 25, No. 01, https://www.worldscientific.com/doi/pdf/10.1142/S0218213016600058