Software testing in industrial projects typically requires large test suites. Executing them is commonly expensive in terms of effort and wall-clock time. Indiscriminately executing all available test cases leads to sub-optimal exploitation of testing resources. Selecting too few test cases for execution on the other hand might leave a large number of faults undiscovered. Limiting factors such as allocated budget and time constraints for testing further emphasizes the importance of test case prioritization in order to identify test cases that enable earlier detection of faults while respecting such constraints. This paper introduces a novel method prioritizing test cases to detect faults earlier. The method combines TOPSIS decision making with fuzzy principles. The method is based on multi-criteria like fault detection probability, execution time, or complexity. Applying the method in an industrial context for testing a train control management subsystem from Bombardier Transportation in Sweden shows its practical benefit.