Common errors in LP models that exhibit unboundedness are a constraint that has been omitted or an input which is incorrect.

Question

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Constraints that have been omitted or incorrect inputs can cause unboundedness in LP models. This means that the model does not have a finite solution and can continue to improve indefinitely in certain directions. It is important to carefully review and validate all constraints and inputs in an LP model to ensure that it is well-defined and has a bounded solution.

Common Errors in LP Models That Exhibit Unboundedness Are a Constraint