Combinatorial problems such as the traveling salesperson, assignment and set-covering problems often rear their heads in process optimization. Because these problems are NP-hard, it remains unlikely that they will yield global optima at reasonable computational cost. Therefore, we must rely on heuristic techniques which seek good, albeit sub-optimal, solutions at an acceptable cost. To this end, meta-heuristic search strategies are interesting because they require very little problem specific knowledge. What little they do need to know about a problem can be encapsulated in a neighborhood operator which provides a mechanism for seeking local optima. Unfortunately, these operators can be very difficult to implement and in this paper we define a practical interface for enumerating neighbors. Once a neighborhood operator has been defined, dynamic strategy selection becomes possible. This means that meta-heuristic strategies, such as simulated annealing and tabu search, can be plugged into problem contexts at run-time.