Abstract
In this study, a nonlinear optimization model is proposed to determine the constraint propagation (CP) of qualitative constraint sets to minimize search backtracking points. The model gives answers to the questions of what the optimal sequence is in the case that there is a set of variables with known values and, alternatively, what variable sequence is optimal to be able to have an optimal value propagation (what variable values should be known to have optimum variable sequence). In order to improve the solution performance, a constraint activation analysis is initiated for the constraints that are defined for the variables with known values by sign algebraic Karush-Kuhn-Tucker conditions. The optimization model and the qualitative activity analysis carried out can be applied to any constraint propagation problem where the variables have a limited set of values.
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Hocaoğlu, M.F. (2019). An Optimization Model for Variable Ordering in Qualitative Constraint Propagation. In: Calisir, F., Cevikcan, E., Camgoz Akdag, H. (eds) Industrial Engineering in the Big Data Era. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-03317-0_10
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