We introduce an effective and efficient iterative algorithm for solving the Continuous-Time Service Network Design Problem. The algorithm achieves its efficiency by carefully and dynamically refining partially time-expanded network models so that only a small number of small integer programs, defined over these networks, need to be solved. An extensive computational study shows that the algorithm performs well in practice, often using time-expanded network models with size much less than 1% (in terms of number of variables and constraints) of a full time-expanded network model. The algorithm is inspired by and has many similarities to the dynamic discretization discovery algorithm introduced in Boland et al. (2017) [The Continuous-Time Service Network Design Problem. Operations Research], but generates smaller partially time-expanded models, produces high-quality solutions more quickly, and converges faster.
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