Reliable Hub Location Model for Air Transportation Networks Under Random Disruptions

Problem definition : In this paper, we study a reliable hub location model, the objective of which is to minimize the associated costs plus the penalty for unserved demands. The model assigns to each origin–destination pair a primary path and a backup path to hedge against the risk of random disruptions. Aside from the fixed costs of locating hubs, the fixed costs incurred by hub-connecting arcs that exhibit economies of scale in transportation are considered. Academic/practical relevance : The widely adopted hub-and-spoke architecture in the network designs can trigger cascading effects during and in the aftermath of disruption events and lead to further losses. By incorporating a general specification of disruptions, our model helps firms improve network reliability while inheriting the benefits of routing via hubs. Therefore, this work extends the literature on hub location problems by considering reliability under disruptions. Methodology : We present a path-based model and formulate a combinatorial optimization problem. By exploiting the structural properties of the problem, we introduce a tractable mixed-integer linear program reformulation and develop a constraint generation method to accelerate the solution procedure. We further construct two effective heuristics. Results : We demonstrate the necessity of considering disruptions and the profound benefits of employing backup paths. Moreover, we show via numerical studies that the path-based model delivers efficient and effective reliable hub-and-spoke designs. Managerial implications : We find that establishing backup paths according to the proposed model barely escalates network costs yet significantly enhances service levels, and that exploiting disruption correlation helps reduce the expected total cost especially when the underlying disruption correlation is strong. We also discover that the decisions of our model on the locations of hubs and hub arcs are relatively robust compared with those on path assignments. We summarize key insights as observations that can be used directly as rules of thumb to guide designs in practice.