Siqiao Li

% VERIFIED; verified 2026-10-11
@article{orbit-b7a9b36bff7fbe36,
  title = {A decomposition-based matheuristic for the passenger-oriented integrated passenger-freight transport problem},
  author = {Li, Siqiao and Stokkink, Patrick and Cats, Oded and Saeednia, Mahnam},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2027},
  volume = {194},
  part = {Part A},
  pages = {105996},
  eid = {105996},
  doi = {10.1016/j.trc.2026.105996},
  url = {https://doi.org/10.1016/j.trc.2026.105996},
  issn = {0968-090X, 1879-2359},
  publisher = {Elsevier},
  abstract = {Integrating passenger and freight transport has become increasingly important for improving the efficiency of urban mobility systems, as planning them separately often results in underutilized capacity and redundant vehicle movements. Coordinating the two enables more effective use of existing transport resources for public transport operators, helps logistics providers maintain cost-effective and reliable deliveries, and assists public authorities in alleviating urban congestion. When implementing such integration, railway stations play a key role as urban hotspots with limited access, where road-rail connections can facilitate efficient intermodal operations. In this work, we introduce a passenger-oriented pickup and delivery problem with explicit road-rail connections and a fleet of shared-use light vehicles. The problem is formulated as a mixed-integer linear program to jointly determine fleet size, routing, and scheduling plans, ensuring a balanced service quality for both passengers and freight. A decomposition-based matheuristic is developed that iteratively coordinates a routing-oriented master problem and a scheduling-oriented subproblem, with feasibility partially restored through a schedule-shifting strategy. The master problem is tackled using an adaptive large neighborhood search enhanced with variable neighborhood search, featuring a novel adaptive selection mechanism based on the relative contributions of service quality indicators. The resulting routing decisions are then passed to the scheduling subproblem, which is solved efficiently using a propagation-based approach that guarantees local optimality. Extensive sensitivity and benchmark analyses demonstrate the benefits of integrated transport in reducing delays, particularly in resource-constrained settings and across different passenger–freight demand compositions.},
  keywords = {Pickup and delivery problem, Integrated transport, Matheuristic, Adaptive large neighborhood search},
}

% VERIFIED; verified 2026-10-11
@article{orbit-866cac1d1b0899c6,
  title = {A two-stage stochastic programming approach for strategic rolling stock scheduling in passenger railway transport},
  author = {Li, Siqiao and van Lieshout, Rolf and Stokkink, Patrick},
  journal = {Omega},
  year = {2026},
  volume = {144},
  pages = {103593},
  eid = {103593},
  doi = {10.1016/j.omega.2026.103593},
  url = {https://doi.org/10.1016/j.omega.2026.103593},
  issn = {0305-0483, 1873-5274},
  publisher = {Elsevier BV},
  abstract = {Conventional railway planning follows a sequential approach in which rolling stock scheduling is performed only after timetables are constructed, forcing early-stage line-planning decisions to rely on simplified rolling stock cost approximations. The lack of precise cost information hampers line-plan evaluation and limits the design of cost-efficient services. This study considers rolling stock scheduling in a strategic setting, aiming to estimate fleet size and operational costs for a given line plan without solving the timetabling problem. Since predefined train services are unavailable at this level, unlike in tactical planning, we introduce a network representation incorporating shunting movements like coupling and uncoupling. To account for demand uncertainty, a two-stage stochastic programming approach is proposed, in which first-stage decisions determine fleet acquisition and second-stage decisions optimize rolling stock circulation, including shunting, under each demand scenario. Two models are developed, differing in whether the fleet size is estimated using a flow-based or circulation-based approach. The applicability of the proposed models is demonstrated using instances derived from the Dutch Intercity network. Compared to a trip-based benchmark, the circulation-based model yields more accurate cost estimates, while short and simple cycles retain most of its value at much lower computational cost. Moreover, stochastic planning better captures the procurement–service trade-off under demand uncertainty and produces more robust fleet plans than a deterministic approach. Under strong emphasis on investment costs, fleet choices are dominated by flexible, cost-effective unit types. Allowing shunting enables flexible capacity redistribution, reducing fleet requirements and investment costs while maintaining comparable service levels.},
  keywords = {Railway planning, Rolling stock scheduling, Shunting, Uncertain demand, Stochastic programming},
}