In the discrete p-hub location problem, various nodes interact with each other by sending and receiving given levels of traffic (such as telecommunications traffic, data transmissions, airline passengers, packages, etc.). It is necessary to choose p of the given nodes to act as hubs, which are fully interconnected; it is also necessary to connect each other node to one of these hubs so that traffic can be sent between any pair of nodes by using the hubs as switching points. The objective is to minimize the sum of the costs for sending traffic along the links connecting the various nodes. Like many combinatorial problems, the p-hub location problem has many local optima. Heuristics, such as exchange methods, can terminate once such a local optimum is encountered. This paper describes new heuristics for the p-hub location problem, based on tabu search and on a greedy randomized adaptive search procedure (GRASP). These recently developed approaches to combinatorial optimization are capable of examining several local optima, so that, overall, superior solutions are found. Computational experience is reported in which both tabu search and GRASP found ‘optimal’ hub locations (subject to the assumption that nodes must be assigned to the nearest hub) in over 90% of the test problems. For problems for which such optima are not known, tabu search and GRASP generated new best-known solutions.
