Decision making is an integral part of any organization. When dealing with a complex business problem, a task of the decision maker is to choose a solution that optimizes the objective and satisfies the constraints. A solution, for which all problem constraints are satisfied, is called a feasible solution. To find a feasible solution is often the most difficult task as there should be no contradiction between the constraints. Another difficulty occurs when a feasible solution does not make sense in a given situation, even when all constraints are satisfied mathematically.
One example is when trying to minimize the calorie intake in your project Solver arrives at a value of zero calories by making all food portion amounts equal to zero. While this is mathematically correct, in fact, this means no eating or drinking. This is hardly an acceptable solution.
Solver is designed to find solutions. In the best case, Solver returns the optimal solution (all constraints are satisfied and the objective function is optimized). In the worst case, Solver is unable to find any feasible solutions for various reasons.
Discuss the possible reasons for Solver not being able to find any feasible solutions. Give examples (at least, one of each) of theoretically optimal and practically good solutions when applied to your food log project. In 10-15 sentences, address the principal options for further optimization of your food log Solver model and discuss when results of a Solver can be considered acceptable for making an adequate decision.
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