A general parallel simulated annealing library and its application in airline industry

To solve real-world discrete optimization problems approximately metaheuristics such as simulated annealing and other local search methods are commonly used. For large instances of these problems or those with a lot of hard constraints even fast heuristics require a considerable amount of computational time. At the same time, especially for sensitivity analyses, fast response times are necessary in real-world applications. Therefore, to speed up the computation a parallelization of metaheuristics is very desirable. We present parSA, an object-oriented simulated annealing library based on C++ and using the MPI message passing interface. It provides an automatic, transparent way of parallelizing simulated annealing. The efficient communication in parSA is the main reason for its success in several real-world applications. To demonstrate performance of parSA we address the weekly fleet assignment problem (FAP) as a real-world application. It is one of the optimization problems which occur in the process of operating an airline. Given a flight schedule and aircraft of different types (subfleets), to each flight leg a subfleet has to be assigned. Large real-world instances have been provided by internationally operating airlines. We show that our heuristic approach using our library parSA is very competitive to the commonly used integer-program (IF) approach.