In the field of operations research, an exhaustive search, also known as brute force search, refers to a problem-solving approach where every possible combination or permutation of the variables and parameters in the problem space is systematically evaluated to find the optimal solution. This method guarantees that the global optimal solution will be found, as it considers all possible scenarios, leaving no stone unturned.

However, while an exhaustive search ensures accuracy, it can be highly impractical and computationally expensive, especially for large and complex problems with numerous variables and constraints. For such cases, alternative optimization methods, such as heuristics or metaheuristics, may be employed to find a near-optimal solution within a reasonable time frame. Exhaustive search can be used as a benchmark to compare the performance of these alternative methods, as it represents the ideal, albeit sometimes unattainable, solution.

For instance, in route optimization problems, a key application in operations research, an exhaustive search would involve evaluating every possible route to determine the most efficient path for delivery or transportation. In a scenario with multiple destinations, the number of possible routes grows factorially with the number of destinations, making exhaustive search impractical even for a relatively small number of destinations due to the overwhelming computational resources required. Hence, while an exhaustive search could theoretically find the absolute best route, it is typically more efficient and practical to use optimization algorithms and heuristics that can find a near-optimal route in a fraction of the time. These algorithms, while they might not always find the global optimal solution, provide a balance between accuracy and efficiency, which is crucial in real-world applications such as last-mile delivery and logistics planning.