Merits and Demerits of Constrained Optimization Models

Constrained optimization models have certain merits and demerits. Some of the merits of such models are:

• An optimization model reduces the number of feasible solutions to a manageable number. This results in an increase in clarity of the situation to the decision-maker and enables him evaluate the alternatives easily.

• Since management decisions are complex and include the study of interrelationships among organizational units, optimization models provide an optimal solution for the organization as a whole.

• Optimization models help a decision-maker perform 'what-if' analysis (also called sensitivity analysis). Using this analysis, the decision-maker can quickly determine the optimum solution of the problem by making modifications to the constraints or the objective function.

• Optimization models also help a decision-maker solve problems mathematically with the help of a computer or otherwise.

However, optimization models also suffer from inherent drawbacks and may result in poor decisions. One of the main drawbacks of these models is that the solution obtained from the model may not always be the optimal solution for the real problem, as these models do not take into account non-quantifiable criteria. For example, the motivation level of a worker affects the production rate. But this cannot be taken into account for determination of optimal solution, as motivation is not quantifiable.

Sometimes, the models may provide a solution that cannot be put into practice. For example, the optimal solution might be that a firm can maximize its profits if it produces 101.5 units, which is impractical.

Hence, these models are considered as tools in decision-making; but they cannot provide the final decision. Operations managers should also study the reasonableness of the results obtained.               

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