Strategic planning for hub resilience: a stochastic location-routing perspective

This study addresses the Reliable Capacitated Hub Location-Routing Problem in the presence of probabilistic disruptive events leading to unavailability of hub facilities. The problem is mathematically formulated as a two-stage stochastic mixed-integer linear programming model. In the initial stage, the objective is to minimize the fixed establishment costs of hubs. In the subsequent stage, emphasis is placed on minimizing the comprehensive expected cost, encompassing costs related to transportation, vehicle usage, vehicle routing, and service loss incurred due to disruptions. The proposed solution methodology hinges on a scenario reduction strategy, complemented by a two-stage improvement procedure. To tackle instances of large scale, a hybrid meta-heuristic algorithm is introduced. This algorithm leverages variable neighborhood search and simulated annealing meta-heuristics. Furthermore, parameters of the algorithm are rigorously fine-tuned using the Taguchi method. To validate the proposed approach, extensive computational experiments are conducted utilizing CAB and AP datasets. The findings produce invaluable managerial insights. Numerical experiments affirm the adeptness of the solution methodology in effectively managing failure costs. Additionally, the outcomes underscore the effectiveness of the scenario reduction technique.