Sensitivity Analysis for Scheduling Problems
Journal of Scheduling
A scheme for unifying optimization and constraint satisfaction methods
The Knowledge Engineering Review
Bender's Cuts Guided Large Neighborhood Search for the Traveling Umpire Problem
CPAIOR '07 Proceedings of the 4th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Sensitivity analysis for distributed optimization with resource constraints
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Duality in optimization and constraint satisfaction
CPAIOR'06 Proceedings of the Third international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Scheduling a two-stage flowshop under makespan constraint
Mathematical and Computer Modelling: An International Journal
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A new method of sensitivity analysis for mixed integer/linear programming (MILP) is derived from the idea of inference duality. The inference dual of an optimization problem asks how the optimal value can be deduced from the constraints. In MILP, a deduction based on the resolution method oftheorem proving can be obtained from the branch-and-cut tree that solves the primal problem. One can then investigate which perturbations ofthe problem leave this proof intact. On this basis it is shown that, in a minimization problem, any perturbation that satisfies a certain system of linear inequalities will reduce the optimal value no more than a prespecified amount. One can also give an upper bound on the increase in the optimal value that results from a given perturbation. The method is illustrated on two realistic problems.