Anyone formulating an optimization problem faces uncertainty in selecting the model parameters. Transportation costs, probabilities of different events, budget constraints, among other parameters, are all unlikely to be known with any degree of accuracy. There may also be doubts about the structure of the model, such as whether to add or remove constraints. Understanding the effect of changes in parameter values and model structure on the optimum solution is the subject matter of sensitivity analysis. Our goal in this chapter is to give an overview of the main algorithmic ideas in sensitivity analysis for combinatorial optimization.