## 2 Background
[PERSON] [13] developed an MILP formulation for the single-allocation p-hub median problem that required fewer variables than previous formulations, while [PERSON] et al. [14], focusing on the uncapacitated single-allocation p-hub median problem, presented two efficient genetic algorithms. [PERSON] et al. [15] illustrated a combinatorial formulation for the p-hub center problem and established its NP-hardness. [PERSON] et al. [16] developed an MILP formulation for the uncapacitated single or multiple allocation hub center problems and presented a branch-and-bound approach for the multiple allocation case. [PERSON] et al. [17] illustrated a two-phase algorithm for efficiently solving larger-scale uncapacitated single allocation p-hub center problems. Furthermore, [PERSON] et al. [18] presented a variable neighborhood search approach for considering the uncapacitated single allocation p-hub median problem. In the hub-covering problem framework, [PERSON] et al. [19] studied the single allocation case presenting an integer programming formulation and three different linearization methods, and later, [PERSON] et al. [20] presented an improved MILP model. The literature further includes research efforts that incorporate stochastic modeling. [PERSON] et al. [21] considered stochastic uncapacitated hub location problems under mixed uncertainty related to demand levels and transportation costs. Two MILP formulations were proposed and evaluated by [PERSON] et al. [22] for the capacitated single allocation hub location problem incorporating decisions concerning the capacity level at which each hub should operate. Among recent research efforts, an H&S model incorporating backup hubs and alternative routes to handle hub disruptions proactively was presented by [PERSON] et al. [23]. [PERSON] and [PERSON] [24] implemented simulated annealing (SA) and genetic algorithms (GA), merged with [PERSON]'s algorithm, for solving the multiple allocation p-hub center problem. [PERSON] et al. [25] addressed both the capacitated single and multiple hub allocation problems with stochastic considerations related to the flow quantities between pairs of nodes in the network. [PERSON] et al. [26] studied the single allocation p-hub median problem incorporating hub unavailability, while [PERSON] et al. [27] illustrated robust counterparts for uncapacitated hub location problems, considering uncertainty in demand and costs. Moreover, an intermodal H&S model with uncertainties in both transportation cost and travel time was proposed by [PERSON] et al. [28], and a modified Benders decomposition was introduced by [PERSON] et al. [29] for large-scale applications of the two-allocation p-hub median problem. Moreover, [PERSON] et al. [30] introduced a multi-objective MILP formulation for designing an air transportation H&S network incorporating environmental concerns. Another factor that can critically affect the performance of an H&S network concerns potential delays and congestion; hence, many recent research efforts incorporate this parameter in the analysis. [PERSON] et al. [31] proposed an MILP formulation for uncapacitated hub location problems assuming that each hub can fail with a site-specific probability, while [PERSON] et al. [32] presented a trade-off model between cost savings and congestion costs due to flow variability in hub facilities. A nonlinear MILP model, including inter-hub economies-of-scale and hub congestion, was introduced by [PERSON] et al. [33]. Moreover, a bi-objective nonlinear MILP model for the single allocation multi-commodity H&S network design under hub congestion was illustrated by [PERSON] et al. [34]. Focusing on marine transportation, a few studies addressed the H&S strategy mainly on liner shipping network design. [PERSON] et al. [35] implemented a genetic algorithm for containership route scheduling incorporating pickups, deliveries and time deadlines, validating the model efficiency through a small container fleet case study in the Aegean Sea, Greece. [PERSON] et al.