Abstract
Real-world Large Scale Combinatorial Optimisation problems (LSCOs) have inherent data uncertainties. The uncertainty can be due to the dynamic and unpredictable nature of the commercial world, but also due to the information available to those modelling the problem. We are concerned with the latter form of uncertainty, which can arise when the data is not fully known or is even erroneous. Our work is motivated by practical issues faced in real-world applications, for instance in energy trading [1]. Here, the demand and cost profiles have evolved due to market privatisation; thus the existing simulation or stochastic data models would not help address the actual problem.
This work was partially supported by the EPSRC under grant GR/N64373/01.
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References
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Yorke-Smith, N., Gervet, C. Data Uncertainty in Constraint Programming: A Non-Probabilistic Approach. In: Proc. of AAAI’01 Fall Symposium on Using Uncertainty within Computation, Cape Cod, MA (2001) 146–152
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Yorke-Smith, N. (2002). On Constraint Problems with Incomplete or Erroneous Data. In: Van Hentenryck, P. (eds) Principles and Practice of Constraint Programming - CP 2002. CP 2002. Lecture Notes in Computer Science, vol 2470. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46135-3_64
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DOI: https://doi.org/10.1007/3-540-46135-3_64
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