Skip to content
DraconiticResearch
Search Sources API
203,234 studies31 sourcesUpdated 3 min ago
PreprintCC BY 4.0arXiv

CLInstGen: An Instance Generation Framework for Two-Tier City Logistics

Abstract

City logistics addresses the design and management of efficient freight transportation in urban areas. Two-tier networks, where goods are moved by different vehicle fleets from distribution centers to customers using intermediate consolidation facilities, are particularly effective. Although studied from routing, location, and network design perspectives, the research progress is slowed by the lack of standardized, state-of-the-art benchmark data. Many studies rely on outdated and artificial or case-specific instances, limiting reproducibility, comparability, and generality of results. To address this gap, we identify general problem characteristics for two-tier city logistics, provide an instance generation framework CLInstGen that is applicable to a wide range of problem settings, and propose the first benchmark library CLLIB. The library and the CLInstGen code are open-source, allowing researchers to easily use, adapt, and generate instances for their specific applications. CLLIB is based on real-world geographies of urban regions and provides a curated collection of standardized instances for direct benchmarking of models and methods as well as statistical evaluation. A computational study with two different problem settings, namely tactical service network design on two tiers and vehicle routing with simultaneous pickups and deliveries on a single tier, confirms the versatility of both the generator and the library, and provides first numerical and managerial insights on the CLLIB benchmark instances.

Related

  1. A Constraint-Aware Generative Framework for Synthetic Origin-Destination Demand in Logistics Networks
    Leian Chen
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  2. Instance Optimal Sparse Recovery from Nonlinear Observations: A Unified Framework
    Junren Chen, Arian Maleki
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  3. Unifying Transforms for Singular Cauchy Problems in Quantum Logistics, Supply Chain Resilience, and Energy Systems
    Padmaja G
    International Journal of Innovative Research in EngineeringPeer-reviewedCC BYFull text8 citedGeneral mathematicsOpenAlex
  4. SNACK: A Sequential Notation Framework for Probabilistic Graph Generation
    Hohyun Kim, Hyesung Kim, Min-hwan Oh +1
    arXivPreprintarXiv licenceFull textStatisticsarXiv
  5. An Energy-Driven Framework for Privacy-Aware Synthetic Data Generation
    Pierpaolo Massoli, Fabio Spagnuolo
    arXivPreprintarXiv licenceFull textStatisticsarXiv
BibTeX
@misc{lange2026clinstgen,
  title = {{CLInstGen: An Instance Generation Framework for Two-Tier City Logistics}},
  author = {Julia Lange and Johannes Gückel and Pirmin Fontaine and Timo Gschwind},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.07289},
  eprint = {2609.07289},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.07289},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - CLInstGen: An Instance Generation Framework for Two-Tier City Logistics
AU  - Julia Lange
AU  - Johannes Gückel
AU  - Pirmin Fontaine
AU  - Timo Gschwind
PY  - 2026
DA  - 2026/09/07
PB  - arXiv
DO  - 10.48550/arxiv.2609.07289
UR  - https://arxiv.org/abs/2609.07289
AB  - City logistics addresses the design and management of efficient freight transportation in urban areas. Two-tier networks, where goods are moved by different vehicle fleets from distribution centers to customers using intermediate consolidation facilities, are particularly effective. Although studied from routing, location, and network design perspectives, the research progress is slowed by the lack of standardized, state-of-the-art benchmark data. Many studies rely on outdated and artificial or case-specific instances, limiting reproducibility, comparability, and generality of results. To address this gap, we identify general problem characteristics for two-tier city logistics, provide an instance generation framework CLInstGen that is applicable to a wide range of problem settings, and propose the first benchmark library CLLIB. The library and the CLInstGen code are open-source, allowing researchers to easily use, adapt, and generate instances for their specific applications. CLLIB is based on real-world geographies of urban regions and provides a curated collection of standardized instances for direct benchmarking of models and methods as well as statistical evaluation. A computational study with two different problem settings, namely tactical service network design on two tiers and vehicle routing with simultaneous pickups and deliveries on a single tier, confirms the versatility of both the generator and the library, and provides first numerical and managerial insights on the CLLIB benchmark instances.
ER  -