Journal article
A hybrid urban delivery system with robots
European journal of operational research, Vol.331(2), pp.441-461
06/2026
DOI: 10.1016/j.ejor.2025.10.008
Abstract
Cities are now restricting access for conventional delivery technologies in some areas, requiring businesses to adopt more flexible distribution systems to complete their deliveries. We present a two-echelon hybrid truck-based robot delivery system for last-mile logistics operations. Robots can navigate through truck no-go areas such as pedestrian zones and college campuses, while trucks can travel through less restricted areas. The hybrid delivery model allows the distribution system to automatically select the better distribution strategy, thereby improving distribution efficiency.We present a mixed-integer linear program to model the proposed system. We also offer valid inequalities to strengthen the formulation and a large neighborhood search-based algorithm with innovative adaptive methods and multiple operators to solve medium and large-scale instances efficiently. Computational experiments are conducted to evaluate how our proposed model performs. Sensitivity analysis experiments considering truck no-go areas of different sizes and area access time windows are performed and reveal managerial insights. We suggest setting up appropriate time windows for some truck no-go areas to reduce the burden of logistics companies in increasing distribution routes due to access restrictions.
Details
- Title: Subtitle
- A hybrid urban delivery system with robots
- Creators
- Shaohua Yu - Nanjing University of Science and TechnologyAnn Melissa Campbell - University of Iowa [Iowa City]Jan Fabian Ehmke - University of ViennaJakob Puchinger - EM Normandie Business School
- Resource Type
- Journal article
- Publication Details
- European journal of operational research, Vol.331(2), pp.441-461
- DOI
- 10.1016/j.ejor.2025.10.008
- ISSN
- 0377-2217
- eISSN
- 1872-6860
- Publisher
- Elsevier
- Grant note
- National Natural Science Foundation of China: 72301137
The research is supported by the National Natural Science Foundation of China [grant no. 72301137] .
- Language
- English
- Electronic publication date
- 10/2025
- Date published
- 06/2026
- Academic Unit
- Business Analytics
- Record Identifier
- 9985014870402771
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