Final answer:
The statement is true; unbounded linear programming problems often occur due to the omission of necessary constraints, leading to a solution wherein the objective function can indefinitely increase, not accurately reflecting the real-world scenario.
Step-by-step explanation:
The statement that unbounded linear programming problems typically arise as a result of misformulation is true. In the context of linear programming, an unbounded solution occurs when the objective function can increase indefinitely due to the lack of constraints that limit the feasible region in the direction of optimization. This can indeed be a result of misformulation, where essential constraints have been omitted, leading to an optimization problem that does not properly represent the real-world situation it is supposed to model.
For example, consider an optimization problem that seeks to maximize profits without a constraint on the maximum production capacity. Without this constraint, the solution suggests that profits could increase infinitely, which is not realistic and thus represents an unbounded scenario due to the misformulation by not including the production capacity constraint.
Another common error leading to unboundedness in linear programming could be an underestimate of population size in scenarios involving animal studies that adapt to seek out bait. Such underestimation fails to impose a realistic limit on the population to be managed, potentially resulting in an unbounded model.