A hybrid artificial intelligence approach based on combining simulated annealing (SA) and case-based reasoning (CBR) is presented. The approach is designed to allow for solving complex, time-critical optimization problems, examples of which lie at the heart of several intelligent transportation systems applications. According to this hybrid approach, the system, when faced with a new problem, first accesses the case base and attempts to locate a sufficiently similar case. If such a case can be located, the solution of the CBR-retrieved case is directly reused. If not, the SA algorithm is used to solve the problem, with the solution of the CBR-retrieved case serving as a starting point for the search algorithm. The rationale behind the approach is discussed, and its major design issues are analyzed. A case study is then presented to illustrate how the approach could be designed and to demonstrate its advantages. Results indicate that combining SA and CBR offers an efficient approach to solving complex, time-critical optimization problems. The results also indicate that SA, used in conjunction with CBR, should be started from a very low temperature to take advantage of the fact that the CBR-retrieved solution is close to the optimum.