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The objective function of Goal Programming is a linear function of the multiple objective functions involved.

Diversification with stocks from opposite industries $($airline with energy$)$ or irrelevant industries $($energy with tech$)$ would decrease the risk compared to not diversifying, whereas diversifying within the same industry $($American Airlines with United Airlines$)$ would increase the risk compared to not diversifying.

In Satisfycing, we have the option of turning the objective function into an upper$-$limit or lower$-$limit constraint based on the client's preference.

Suppose that we are shipping an indivisible product $($such as TV$)$ in a network using minimum$-$cost flow models, where the capacity of each link is the number of TVs that can be shipped through the link. We do not need to explicitly impose integer constraints, as the solution will be automatically integer.

The robust shortest path problem, in which we want to find two disjoint paths from an origin point to a destination point in a map, so as to minimze the length of the longer path among the two, can be modeled linearly.

In the aforementioned robust shortest path problem, we do not need to explicitly impose binary constraints, as the solution will be automatically integer.