Journal article
The value of crowdshipping from microhubs in urban environments
Decision sciences
04/23/2026
DOI: 10.1111/deci.70032
Abstract
We introduce a framework to assess the value of crowdshipping in last-mile delivery at a strategic level with a focus on a wave-based structure for delivery operations, considering service areas that are part of a larger urban environment. For each service area and wave, a truck departs from a depot and completes a route, including a visit to a microhub and a set of customers. The rest of the customers receive deliveries from crowdshippers, who pick up packages from the microhub. We derive a set of analytical results for this base model of the problem on a solid grid to provide strategic decision-making insights into when crowdshipping can be implemented in a service area at a cost-benefit compared to truck delivery. For this setting, we derive the minimum cost solution based on whether the grid is even or odd and the location of the microhub. We find that the structure of the solution changes at certain breakpoints in the ratio of the cost of crowdshipping to the cost of truck delivery. To understand how the value of crowdshipping changes across settings, we provide a mixed integer programming formulation and extensions to more general settings that we evaluate in a set of computational experiments. We consider the impact of factors such as limited crowdshipper availability and multiple microhubs in a service area. We discuss the effect on the total cost of delivery and the number and location of customers that are assigned to crowdshipped delivery.
Details
- Title: Subtitle
- The value of crowdshipping from microhubs in urban environments
- Creators
- Sarah Powell - University of IowaAnn Campbell - University of IowaIman Dayarian - University of Alabama
- Resource Type
- Journal article
- Publication Details
- Decision sciences
- DOI
- 10.1111/deci.70032
- ISSN
- 0011-7315
- eISSN
- 1540-5915
- Publisher
- Wiley
- Number of pages
- 22
- Language
- English
- Electronic publication date
- 04/23/2026
- Academic Unit
- Business Analytics
- Record Identifier
- 9985163398602771
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