In practice, linear programming has some limitations that deteriorate the model fitness to real-life situations. There is a trade-off between suitability and solvability of the problem. Linear programming provides solutions under some assumptions. These are proportionality, additivity, divisibility and certainty assumptions. As also understood from the information given, the correct answer is E. These are important to mention as it will be easier for operations researcher to evaluate how well linear programming applies to a given problem. In both the objective function and constraints, the contribution of every decision variable is proportional to its value. As a result, proportionality assumption casts out any variable that has an exponent other than 1. Consider a firm that aims to maximize its profits under the constraint of its production costs. In practice, production costs are not the same for different levels of production as higher production lowers costs by the economies of scale. To avoid a violation of the proportionality assumption, some characteristics of real-life situations such as economies of scale must be ignored. That undoubtedly makes the model less reliable. Either it is the objective or the constraints, every function is the sum of the individual contribution of the respective variables. This is called the additivity assumption, which prohibits cross-product terms in the expressions of a linear program. This assumption restricts the model design as well. For instance, a linear maximization model of aggregate revenue to be received from complementary products has to exclude cross-product terms that proxy the multiplier effect of each product over the other. In a linear program, decision variables are not limited to integer values. In fact, they are divisible. In certain situations, the divisibility assumption does not hold as some decision variables can only be an integer. Methods of integer programming, which are not in the scope of this book, overcome this obstacle. The last assumption is certainty, which means that each parameter of a linear programming model is constant in all conditions. In real applications, factors affecting the decisions are not as stable as they are strictly assumed. In the modeling phase, all these assumptions need to be considered while preserving an acceptable level of similarity between reality and what is realized.